contributions to the theory and implementation of ... · of conceptual validation scenarios of the...
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Universitatea “Politehnica” din Bucureşti
Facultatea de Electronică, Telecomunicaţii şi Tehnologia Informaţiei
Contributions to the Theory and
Implementation of Communication
Applications on Cloud Computing
Platforms
George SUCIU
Summary version: 05.12.2013
Supervisors
Prof. Simona HALUNGA (UPB)
December 2013
Acknowledgements
First I would like to thank to my supervisors.
Thank you for the great team at work (BEIA Consult Int.) and university (Bucharest and
Aalborg).
To all my friends.
Thank you to my family, especially my brother who is also doing PhD.
To everyone who supported me,
Thank you.
Contents
Chapter 1. Introduction ............................................................................................ 5
1.1 Thesis Objectives and Outline .............................................................................. 5
1.2 Motivation of the Study – Challenges and Possible Solutions ............................. 8
Chapter 2. Cloud Computing Technologies and Systems ...................................... 9
2.1 Introduction .......................................................................................................... 9
2.2 Necessity of cloud computing solutions ............................................................... 9
2.3 Technologies ...................................................................................................... 10
2.3.1 Virtualization .................................................................................................. 10
2.3.2 SOA And Web 2.0 Services ........................................................................... 10
2.3.3 IPv6 ................................................................................................................. 10
2.3.4 Distributed and Decentralized Systems .......................................................... 11
2.3.5 P2P Systems ................................................................................................... 11
2.4 Summary ............................................................................................................ 11
Chapter 3. Cloud Computing Architectures and Algorithms ............................. 12
3.1 Introduction ........................................................................................................ 12
3.2 Implementation models ...................................................................................... 12
3.3 Resource management ........................................................................................ 12
3.3.1 General characteristics .................................................................................... 13
3.3.2 Algorithms for resource management ............................................................ 13
3.4 Summary ............................................................................................................ 14
Chapter 4. Cloud Computing Services .................................................................. 15
4.1 Introduction ........................................................................................................ 15
4.2 Cloud Service Attributes .................................................................................... 15
4.3 Cloud Service Delivery Levels .......................................................................... 15
4.4 Summary ............................................................................................................ 16
Chapter 5. Analysis of an Experimental Cloud Platform .................................... 17
5.1 Introduction ........................................................................................................ 17
5.2 Simulation of cloud applications ........................................................................ 17
5.3 Analysis of simulation applications for cloud systems ...................................... 17
5.4 Requirements for the experimental platform ..................................................... 18
5.5 Comparative analysis of existing cloud platforms ............................................. 18
5.6 Summary ............................................................................................................ 18
Chapter 6. Development of the Open Source Cloud Platform SlapOS .............. 20
6.1 Introduction ........................................................................................................ 20
6.2 SlapOS Architecture ........................................................................................... 20
6.3 SlapOS Protocol ................................................................................................. 21
6.4 Summary ............................................................................................................ 22
Chapter 7. Implementing a Cloud Communication Node ................................... 22
7.1 Introduction ........................................................................................................ 22
7.2 Hardware and software characteristics of the cloud platform ............................ 22
7.3 Methodology for Installation and Configuration of a SlapOS Node ................. 24
7.4 Summary ............................................................................................................ 24
Chapter 8. Conclusions and Future Work ............................................................ 25
8.1 General Conclusions and Main Contributions ................................................... 25
8.2 Future Work ....................................................................................................... 28
References ................................................................................................................... 29
Annexes ........................................................................................................................ 32
Own Publications ........................................................................................................ 32
Chapter 1. Introduction
Internet users are increasingly adding video content to existing online services and
applications, therefore having the effect that the number of people viewing videos online has
grown over the past year and the time spent per viewer has increased accordingly. Google
sites, including YouTube, continue to be the most watched online video sites with more than
35.4 million Google sites visitors watching YouTube.
Many real-world systems involve large numbers of highly interconnected over Internet
heterogeneous components. The Cloud is among one of the more promising system that will
be deployed at a large scale in the near future because the field counts yet on many success
stories: Amazon EC2, Windows Azure or Google App Engine [1.1].
Cloud Computing is traditionally divided in three market segments: Infrastructure as a
Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). To better
understand cloud communications, it is useful to understand the different service models of
cloud computing. The best known is SaaS where the customer purchases access to an
application of a service hosted in the cloud. PaaS refers to access to platforms that allows the
customers to deploy their own applications in the cloud, and IaaS is at a lower level with
access to the systems, storage, network connectivity, and OS management.
1.1 Thesis Objectives and Outline
The main objective is to develop a protocol and architecture for interconnecting wired
and wireless networks for providing Telemetry M2M and IoT Telecommunication
applications based on an open source Cloud platform.
A methodology of obtaining state of the art and further identification of future
requirements for Cloud will be used in Chapter 2 and will be extended by a case study of the
proposed test open source Cloud platform. In parallel to the proposed methodology, best
practices and policies will be collected in the form of desk studies, literature review,
brainstorming and problem delineation.
Dissemination of knowledge will be done through conference, journal papers or
project deliverables on the website. I plan to make my research results available to the
community within the scope of the CloudConsulting project. Intellectual Property Rights
(IPR) considerations will be evaluated whenever applicable.
In the second chapter the timeliness of the proposed cloud communications concept is
justified and we present a brief summary of projects aimed at the development of cloud
computing solutions. The technologies and systems for building a cloud computing hardware
and software solutions are researched, and the role of communication networks in such
solutions underlined. The contributions to this chapter are published in the following
conference and journal papers:
• G. Suciu, O. Fratu, S. Halunga, C. G. Cernat, V. Poenaru, and V. Suciu, “Cloud
consulting: ERP and communication application integration in open source cloud
systems”,19th Telecommunications Forum, TELFOR 2011, IEEE Communications
Society, pp. 578-581, 2011.
• C. G. Cristian, V. A. Poenaru, and G. Suciu, “Multimedia content distribution between
core network routers using Peer-to-Peer (P2P)”,19th Telecommunications Forum,
TELFOR 2011, IEEE Communications Society, pp. 254-257, 2011.
Chapter 3 deals with the subject of detailed cloud computing architectures and
resource management mechanism, and analyzes the general characteristics that resource
management algorithms must satisfy. Also, a comparative analysis of existing and proposed
algorithms was conducted, with the goal to achieve improved planning and allocation of
resources. The main findings are published in the conference papers:
• G. Suciu, C. Cernat, G. Todoran, V. Suciu, V. Poenaru, T. Militaru, and S. Halunga,
“A solution for implementing resilience in open source Cloud platforms”, 9th
International Conference on Communications, COMM 2012, IEEE Communications
Society, pp. 335-338, 2012.
• G. Suciu, V. Poenaru, C. Cernat, T. Militaru, G. Todoran, “A Study of Security in
Open Source Cloud Platforms”, 5th International, SECITC 2012, pp. 125-130, 2012.
• G. Suciu, O. Fratu, C. Cernat, T. Militaru, G. Todoran, and Vlad Poenaru, “Cloud
systems for environmental telemetry - A case study for ecological monitoring in
agriculture”, XLVII International Scientific Conference on Information,
Communication and Energy Systems and Technologies, ICEST 2012
• G. Suciu, C. G. Cernat, and G. Todoran, “Cloud Research - Implementing Scientific
Research Information Systems in Open Source Cloud Platforms”, Fifth Romania Tier
2 Federation "Grid, Cloud & High Performance Computing Science", RO-LCG 2012
Different types of cloud services are detailed in Chapter 4, where we developed
several examples of communications applications based on cloud computing systems. We
examine how the services offered by a cloud computing system can be grouped according to
the category of resources offered at certain levels. The contributions of are presented in the
following papers:
• V. A. Poenaru, G. Suciu, C. G. Cernat, G. Todoran, and T. L. Militaru, “Attacking the
cloud”, XLVII International Scientific Conference on Information, Communication
and Energy Systems and Technologies, ICEST 2012
• T. L. Militaru, G. Suciu, et.al. “Rule based expert systems running over the cloud”,
Proceedings of the Fourth International Conference on Intelligent Networking and
Collaborative Systems, INCOS 2012, pp. 495-496, 2012.
• G. Suciu, E. G. Ularu, R. Craciunescu „Public versus Private Cloud Adoption – a Case
Study based on Open Source Cloud Platforms”, 20th Telecommunications Forum -
TELFOR 2012, IEEE Communications Society, ISI Thomson, ISBN 978-1-4673-
2983-5 , Belgrade, Serbia, 20-22 Nov. 2012, pp. 494 – 497.
Chapter 5 gives an overview of different simulation systems and cloud computing
platforms. The goal is to determine the requirements of an experimental cloud platform and
propose a methodology of initial simulation of the applications to be run in the cloud, before
proceeding to implement a system. We perform a comparative study of various cloud
platforms, and justify the choice of open source platform SlapOS. The finds contribute to the
following papers:
• E.G. Ularu, F.C. Puican, A. Apostu, G. Suciu, A. Vulpe, ”Analytical Databases for the
Cloud and Virtualization”, 12th International Conference on Informatics in Economy
(IE 2013), ISSN 2284-7472, ISI Thomson, Bucharest, Romania, 25-28 Apr. 2013, pp.
337-341
• G. Suciu, C. Voicu, G. Todoran, A. Martian, S. Halunga, C. Butca, ”Network Cloud
Simulator for Modelling Trust in Cognitive Radio Applications”, Accepted 21st
Telecommunications Forum TELFOR, IEEE, 2013.
Chapter 6 presents in detail the SlapOS cloud platform, analyzing the system
architecture and the structure of components, including performance and security issues.
Chapter 7 describes the implementation of a SlapOS cloud node, and the development
of conceptual validation scenarios of the cloud platform for communications applications. The
following papers summarizes the main findings of the chapters:
• G. Suciu, A. Vulpe, S. Halunga, O. Fratu, G. Todoran, V. Suciu, “Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things”, 19th International Conference on Control Systems and Computer Science (CSCS), IEEE, ISBN 978-0-7695-4980-4, Bucharest, Romania, 25-28 May 2013, pp. 513-518.
• G. Suciu, A. Vulpe, G. Todoran, G.; Cropotova, J.; Suciu, V., “Cloud Computing and Internet of Things for Smart City Deployments”, 7th International Conference Challenges of the Knowledge Society (CKS 2013), ISSN 2068-7796, Index Copernicus Journals Master List ICV 3.86, Bucharest, Romania, 17-18 May 2013, pp. 1409-1416.
• G. Suciu, S. Halunga, O. Fratu, A. Vasilescu, V. Suciu, “Study for Renewable Energy Telemetry using a Decentralized Cloud M2M System”, 15th International Symposium on Wireless Personal Multimedia Communications (WPMC), IEEE, ISBN 978-87-92982-52-0, Atlantic City, SUA, 24-28 Jun. 2013, pp. 1-5.
• G. Suciu, S. Halunga, A. Vulpe, V. Suciu, "Generic Platform for IoT and Cloud
Computing Interoperability Study", 2013 11th International Symposium on Signals,
Circuits and Systems (ISSCS), IEEE, ISBN 978-1-4673-6143-9, Iasi, Romania, 10-12
Jul 2013, pp.1-4.
In the last chapter, the main research aspects and contributions and summarized,
furthermore presenting future research directions.
1.2 Motivation of the Study – Challenges and Possible
Solutions
Recent research on Cloud Computing has focused on the implementation of Service
Level Agreements (SLA) and operation of large Data Centers. However, in case of Force
Majeure such as natural disaster, strike, terrorism, unpreventable accident, etc., SLA no
longer apply. Rather than centralizing Cloud Computing resources in large data centers,
Distributed Cloud Computing resources are aggregated from a grid of standard PCs hosted in
homes, offices and small data centers.
Based on the implemented scenario, several question rise regarding its performances
and efficiency. Cloud nodes report on resources used and trusting client to report billing
values is a well-known security issue. The security mechanisms included in proposed solution
are setup to prevent a node from cheating on billing values reported. However traffic on
unencrypted links could be intercepted and it is possible for a node to join the cloud and start
sniffing sensitive data.
Currently, after many standardization efforts, the approach promoted for cloud
providers is to impalement dual mode WiFi / GSM Fixed Mobile Convergence (FMC) and
transition to 4G (WiMax / LTE) and Cloud-based (web) data brokerage platforms.
Chapter 2. Cloud Computing Technologies and Systems
2.1 Introduction
In this chapter we present the technologies and systems that are the basis for cloud
computing solutions and the role of communication networks in such solutions. The
timeliness of the proposed concept of cloud communications is justified and a brief summary
of projects aimed at the development of cloud computing solutions is presented. Also, we
evaluate technologies like grid computing, virtualization systems and communications aspects
such as IPv6 networking.
2.2 Necessity of cloud computing solutions
Cloud computing is the delivery of computing and storage capacity as a service to a
community of end-users. Cloud computing also extends the concept of IT services by
combining user data, software and on-demand computation resources over a network. It relies
on sharing of resources to achieve coherence and economies of scale similar to a utility (like
the electricity grid) over a network (typically the Internet). At the foundation of cloud
computing is the broader concept of converged infrastructure, virtualization and shared
services.
The traditional layered approach implicitly supposes that the IaaS layer of Public
Clouds is implemented by very large server farms, which are supposed to provide optimal
efficiency through economies of scale and automation. The IaaS layer of Private Clouds is
implicitly supported by expensive Storage Area Networks (SAN) hardware. There are several
efforts already under way, including the Distributed Management Task Force (DMTF) Open
Cloud Standards Incubator, the Open Grid Forum’s Open Cloud Computing Interface
working group, and the Storage Network Industry Association Cloud Storage Technical Work
Group. In France the Free Cloud Alliance promotes the first Open Source Cloud Computing
Stack which covers IaaS, PaaS and SaaS with a consistent set of technologies targeted at high
performance and mission critical applications. A great resource to see the spectrum of cloud
standards activity can be found at the OMG’s cloudstandards.org wiki.
On the SaaS side, cloud communications services support embedding communications
capabilities into business applications such as Enterprise Resource Planning (ERP) and
Customer Relationship Management (CRM) systems. For “on the move” business people,
these services can be accessed through a smartphone, supporting increased productivity while
away from the office. These services are over and above the support of service deployments
of VoIP systems, IP contact centers, collaboration systems, and conferencing systems for both
voice and video. These services can be accessed from any location and linked into current
services to extend their capabilities, as well as stand alone as service offerings. In terms of
social networking, using cloud-based communications provides click-to-call capabilities from
social networking sites, and access to Instant Messaging systems and video communications,
broadening the interlinking of people in the social circle.
2.3 Technologies
2.3.1 Virtualization
Virtualization of resources is the core of any cloud computing architecture, allowing
the use of abstract logical interfaces for accessing physical resources (servers, network,
storage). Among the methods of simulation of the interface to the physical objects are (a)
multiplexing - creating multiple virtual objects in a single instance of a physical object , such
as a processor to process multiplexed multiple linked processes (threads) , (b) emulation -
building a virtual object in a physical object of another type , such as a hard disk can emulate
physical RAM (through a file or partition interchangeable - swap ), (c) aggregation - creating
a single virtual object from multiple physical objects, for example a number of hard drives to
form a RAID disk unit, (d) Multiplexing combined with emulation - for example TCP
emulates secure communication channel and multiplexes the data transfer between channel
physical communication and processor.
2.3.2 SOA And Web 2.0 Services
Along with virtualization, service-oriented architecture concepts (SOA) and Web 2.0
services are the fundamental technologies for the creation of a cloud. These latter two can be
connected and coordinated together representing SOA architecture whose components are
implemented as independent services that communicate with messages, but need not
necessarily web services technology.
In systems based on cloud computing, the above mentioned technologies for
infrastructure, platforms and virtualized applications are implemented as services (usually
web), and offered to users in the form of service-oriented architecture systems. Most public
cloud systems, provide access to services via standardized interfaces and protocols, as
described on the services of Web 2.0 and RESTful (Representational State Transfer - a
description of the software architecture model based on HTTP).
2.3.3 IPv6
Addressing Internet resources is one of the main characteristics of a cloud network, so
the migration to IPv6 is one of the important topics, as will be shown below. Protocol IPv4
address space is 232 (4.3 billion addresses) a number that turned out to be insufficient, on
January 11, 2011 was officially announced as exhausted [16]. In contrast, IPv6 using 128 bit
addressing has a capacity of up to 2128 (3.4 x 1038 addresses).
The advantages of a virtual routing system based on virtual machines is proposed, as
they are able to isolate each different instance (as stored in files or operating system files), the
ability to easily perform common operations (start, stop, create, delete, copy, move), the
ability to achieve additional operations (return to saved snapshots, backup and restoration of
snapshots), and last but not least, the simultaneous use of different protocol stacks (IPv4 and
IPv6).
2.3.4 Distributed and Decentralized Systems
Research over many decades in the field of parallel and distributed computing is the
basis for the development of cloud-based computing systems, considering that many of the
problems identified in the resource management algorithms were already solved, or problems
at the implementation level can be avoided. The cloud system is an evolution of distributed
computing systems that extends the following paradigms: (a) computing grid systems, (b)
Utility computing (computer processing systems as utilities), (c) Internet Computing (Systems
using the Internet for calculations), (d) autonomous computing systems, (e) Edge computing
(computing systems at the network perimeter), (f) Green computing (computing systems with
low environmental impact).
Another important aspect is the issue of communication and modification of existing
applications to convert them to the SaaS model. As we will explain in the next chapter, the
P2P volunteer computing paradigms, contributed to the development of distributed computing
systems and decentralized applications.
2.3.5 P2P Systems
The P2P model can be viewed as a precursor cloud systems, being a variant of
distributed system that was based on flexible and least cost access at processing and storage
resources provided by participants of the system (that are in different administrative areas).
As a definition, P2P (Peer-to-Peer) system represents a distributed and decentralized network
architecture, where peer nodes are typically user terminal. These nodes have equal attributes
and tasks, and also function as a consumer, but also as a data source by providing a portion of
the processing and storage capacity for use by other nodes, thus relieving network from
certain tasks [17].
2.4 Summary
For building a cloud network, we propose the transformation of the Chord [18]
protocol structure, from a typical ring topology, to a master -slave multi- ring structure, by
using the address grouping mechanism of IPv6. Also, taking into account factors such as
performance and reliability of a node in the cloud to manage heterogeneous resources
efficiently, we propose to calculate the reputation and performance of a node by extending the
P2P mechanism Credence [22], resulting a decentralized algorithm where each node uses the
locally stored information to evaluate other nodes and to share assessments of performance
and reputation nodes neighbors. Reputation is very important for these systems, where
unstable or even malicious nodes may appear, being calculated for each node based on their
matching between their own votes and the votes of nodes in a group with similar voting
criteria.
Chapter 3. Cloud Computing Architectures and
Algorithms
3.1 Introduction
Cloud architectures can be analyzed from two different perspectives, from an
organizational perspective and from a technical standpoint. In this chapter we will discuss in
the first section the organizational point of view, which implies a distinction of the domains in
which users and service providers are organized, and how they are separated. The following
sections will research technical functionality and algorithms for cloud resource management
system.
3.2 Implementation models
The traditional way of IT in a company can be very problematic. As changes in the
business environment occur, so appears the need to implement more efficient enterprise
systems. Cloud Computing technology represents the next enterprise computing paradigm and
a solution to most current enterprise IT problems. This chapter examines how Cloud
technology has evolved and the way it affects related performances by presenting a case study
based on a proposed open source Cloud platform.
A deployment model defines the purpose of the Cloud and the nature of how the
Cloud is located. The NIST definition [2.1] for the four deployment models is as follows:
• Public Cloud: The public Cloud infrastructure is available for public use alternatively
for a large industry group and is owned by an organization selling Cloud services.
• Private Cloud: The private Cloud infrastructure is operated for the exclusive use of
an organization. The Cloud may be managed by that organization or a third party. Private
Clouds may be either on or off-premises.
• Hybrid Cloud: A hybrid Cloud combines multiple Clouds (private, community of
public) where those Clouds retain their unique identities, but are bound together as a unit. A
hybrid Cloud may offer standardized or proprietary access to data and applications, as well as
application portability.
• Community Cloud: A community Cloud is one where the Cloud has been organized
to serve a common function or purpose.
3.3 Resource management
Networks based on cloud computing offers the ability to manage resources that are
theoretically almost unlimited in terms of computing power, memory, processing and storage,
while customers have the ability to adjust the dynamic consumer needs in a short time,
usually a few minutes [5].
3.3.1 General characteristics
To manage a cloud services network, five specific factors were defined [24], which
will be detailed in the following chapters: self-service according to need; network
accessibility; shared resources; rapid elasticity, measurable services.
Policies are necessary to determine which are the principles by which decisions are
made for resource management, and thereafter to determine the mechanisms for implementing
those policies. The cloud resource management policies can be grouped into five classes:
• Access control - is restricting access to a system, in the sense of accepting new
processing tasks under the control policy , but also to complete tasks already
in work requiring knowledge of the state to limit overall system ;
• Efficient allocation of capacity - refers to the allocation of resources for each
active instance of a service in the cloud , as a global optimization problem ,
which requires the search for resources in a space with constraints and
frequent changes of the component systems.
• Network load balancing ( Load Balancing ) - can be done locally with energy
optimization to distribute processing tasks equally in a group of servers is
difficult but globally.
• Energy optimization - is one of the main objectives to reduce service costs ,
and can be achieved by focusing processing tasks on the lowest possible
number of servers, and connections to other servers in standby mode is,
however, difficult to manage QoS in this context.
• Ensuring QoS - can be treated as an optimization problem of resources, but the
models require complex calculations that can not be performed efficiently in a
short enough time for management decision making and resource allocation
3.3.2 Algorithms for resource management
Resource management is the core operation of any computer system, and is subject to
three basic criteria for evaluating system performance, functionality and cost.
Implementation of resource management policies can be achieved by four types of
mechanisms, which rely on a well-defined approach rather than ad- hoc methods: (a) Control
theory - using the principle of feedback in order to ensure stability of the system and predict
the transition [28], but can only predict the behavior of local, rather than global, with
simplified models, but unrealistic use of Kalman filters for it; (b) Utility algorithms - need a
performance model and a mechanism for allocating the cost and coordination of its user-level
performance [29]; (c) Machine learning systems - uses a branch of artificial intelligence
techniques by which a system can learn from the process data. An advantage is that these
techniques do not require a performance model of the system [26], and can be applied for
coordination of multiple nodes that are themselves managers of autonomous systems; (d)
Economic mechanisms - are mechanisms that take into account operation principles of a free
market trading of resources and does not require a model of the system, for example using
combinatorial auctions for resource packages [30].
3.4 Summary
The performance model of a cloud system may become very complex cloud and a part
of the analytical solution as described above is reaching its limits in the case of a large
number of nodes. Also in some cases it was found that monitoring systems that require
collecting information on the state system may be regarded as intrusive or models cannot
provide accurate data.
As parameters needed to be are the average use of a CPU, memory, storage space and
power consumption, a strategy is considered improved when reducing the number of requests
to controllers to change the amount of virtual machines. A theoretical approach to optimal
control proved difficult in terms of the amount of computation required. Also convincing
results cannot be based on empirical values of some parameters for optimal control equations.
Another approach to combinatorial auctions offered a simple solution for resource
management, reducing the problem to that of a packing model for sets of resources.
As innovative solutions we proposed to use epidemic algorithms (infection /
immunity) to reproduce by cloning instead of crossing for virtual machines, and the
application of at least one mutation of each individual in the population, resulting in a
selection of the best individuals and the loss of diversity below 70%, compared to 90% for
genetic algorithms.
Thus, this approach foresees that in the future we will use servers with normalized
performance, uniform communication links and data centers composed of modular
components easily interchangeable when new modules are available as the technology
improves.
Chapter 4. Cloud Computing Services
4.1 Introduction
In this chapter we present the services offered by a cloud computing based system, and
how they can be grouped into the category of resources offered in certain levels. We underline
the difference between cloud services and their attributes, and the concept of cloud computing
as described in previous chapters, the latter being actually the technologies and systems that
enable the creation of cloud services .
As was shown in previous chapters, cloud systems allow the implementation of
heterogeneous systems and technologies, resource allocation mechanisms and applications as
Web services. Consequently, it is no wonder that the cloud service and is suitable very
diverse, and at first sight very heterogeneous in terms of functionality and use.
Based on the above conceptual architectures, we will build first a comparison between
cloud computing and its services, and then carry out a chart that will help categorize the
different levels of service. This will allow comparison of instances of each class and is useful
for determining the equivalence classes and to find some complementary cloud services to
achieve optimal solutions for different usage scenarios.
4.2 Cloud Service Attributes
In Chapter 2 we found that there are many standardization initiatives for defining
types and classes of cloud services, and structuring of the levels of service delivery, best
known as IaaS, PaaS and SaaS. Management strategies for these levels are different, but as
common property there is the very high demand fluctuation of resources that cloud service
operators should take into account to streamline the allocation of resources by implementing
mechanisms for the elasticity attribute type. In some cases, traffic peaks can be managed, for
example for web services that have seasonal peaks, but contingency must be implemented at
the level of automatic allocation mechanisms. This implies that there is a pool of resources
that may be issued or allocated according to demand and that a monitoring system is available
with a control mechanism to decide in real time reallocation of resources.
4.3 Cloud Service Delivery Levels
Further detailed are the levels IaaS, PaaS and SaaS [39]. Infrastructure as a Service
(IaaS) is one of the most popular ways to provide resources as a service, known also as
Hardware as a Service (HaaS). It can be divided into several categories , the most important
being : (a) Computing as a Service (CaaS) where virtual machines are rented and priced
according to resource consumption in a given unit of time, the main memory, CPU , features
of its operating system and pre-installed applications , (b) Data as a Service (DaaS) , also
known as storage as a Service (STaas), where virtually unlimited storage space is provided for
storing files, regardless of size and type, being charged according to the amount of data stored
or transferred , (c) Network as a Service (Network as a Service - Naas) refers to cloud
services which provide virtual network connections, such as VPN or MVNO, or by
infrastructure sharing that may belong to third parties e.g. communications operators.
Platform as a Service (PaaS) are services mainly used by developers to code
applications for users, rather than to be directly accessed by the latter. The platform is
essentially a middleware that provides a programming environment and run by different
applications written in different programming languages can be offered as services.
Software applications are offered as cloud services make up the level of Software as a
Service (SaaS), being placed in terms of service delivery levels above PaaS and IaaS. This
model has the advantage that users do not need to install software on local resource limited
equipment, but can be easily accessed and configured through the web interface.
Recent scientific literature introduced new concepts of other applications offered as
services (XaaS), culminating in the topmost level in the form of people offered as a service
over the stack of cloud-based computing, the so-called Human as a Service (HuaaS) . This
approach emphasizes that the paradigm of "cloud computing" is not limited to technological
resources, but can be expanded to provide services through the participation of human beings
as resources. As a subset of HuaaS the term of crowdsourcing is emerging, which describes a
service provided by a group of interconnected people performing certain tasks or solving
some complex problems, including crisis situations [40].
In [41] the authors propose a service for managing contextual information in large
scale distributed systems. This work proposes a concept of Reasoning as a Service (RAAS)
and is based on XML messages for configuration of M2M services , which adapts according
to the changing context. In addition, authors in [42] propose a system for management of
communications that use the services of contextual information for communications platforms
with a goal to make the user interaction more effective.
4.4 Summary
These services are also the foundation for the new paradigm of the Internet, such as:
- Internet of Things (IoT): a global infrastructure of sensor networks and devices, based on
interoperable communications protocols interconnecting physical and virtual objects in an
information network; - Internet of services (IoS) standardized interfaces, open and
configurable enables different applications to function as interoperable services using specific
semantic understanding, aggregation and processing of information derived from different
sources, formats or other levels services ; - Internet of People (IoP) as shown by the concept
of HuaaS, people become part of intelligent heterogeneous networks, being able to connect,
interact and share information easily between them and the social or environmental; - Internet
of Everything (IoE) : the ability to connect any device capable of providing web service
interfaces for accessing their natural human-machine interaction.
Also, we published the results of the research work on the use of cloud platforms for
different applications in areas such as agriculture, smart cities, expert systems, e-learning
platforms or neutrino radiation monitoring.
Chapter 5. Analysis of an Experimental Cloud Platform
5.1 Introduction
In this chapter we will consider various platforms for simulation and implementation
of a cloud system. We will further analyze several approaches to cloud simulation
applications and will investigate the criteria for choosing a cloud platform.
5.2 Simulation of cloud applications
The analysis of approaches to modeling and simulation applications in the cloud will
help in choosing a technical solution to implement an experimental platform. From the point
of view of elasticity several models were developed, being one of the most important criteria
in choosing a cloud system from the point of view of the user. For example, we can model
each resource (CPU , memory , network, storage , etc.) as a unit that can be allocated and
monitored by the user to meet the defined QoS metrics [43]. SLA modeling can be done by
defining simple mathematical relationships, by analyzing the correlation between the SLA
required for applications and the number of servers used to run the applications. It may take
into account, for example, the number of virtual machines and frequencies at which they must
run to minimize electricity consumption [44], this modeling being closely related to the
approaches presented. Energy efficiency of a cloud system was analyzed in the section on
resource management, and being proposed a model for the solutions studied for several types
of optimization problems such as the minimization of a cost function for different resources
(energy, virtual machines, bandwidth) or reduction of CO2 respecting QoS constraints.
5.3 Analysis of simulation applications for cloud
systems
The mathematical models presented relate mainly to optimize resource management
mechanisms based on resource management algorithms discussed in the section dedicated to
them. Although these mathematical models are available to resource allocation algorithms for
grid systems, cluster and P2P [45], they cannot be used as such a system for modeling cloud
architecture based on virtual machines due to the latter. In addition, the real cloud systems is
necessary to study aspects of communication and user behavior, the most appropriate
approach to use simulation applications are described and compared in the following sections:
GreenCloud [46], MDC [47], iCanCloud [48] GDC [49], DCSim [50], clouds [51], CDOs
[52], TeachCloud [53].
To demonstrate the utility of a cloud network for cognitive radio (CR), where multiple
senders compete for the available communication channels, we proposed the extension of a
cloud simulator (CloudSim) [51], with a scalable network model, and a model generalized
trust management application, based on historical data processing algorithm. This approach
allows more accurate assessment of policy planning and resource allocation to optimize the
performance of cloud infrastructure for CR applications.
5.4 Requirements for the experimental platform
Specific requirements of the cloud platform are listed below: Enable virtualization
running on different platforms and operating systems; existence of a development platform
for adding applications written in different programming environments; Using language
easy to learn programming, and allows portability to other platforms; ability to provide
resources to the cloud, regardless of network topology in which the nodes in the cloud,
including IPv6 networks; possibility of specifying how detailed the necessary resources
(processor, memory, network, storage, etc.). be open source (open source) to allow
modifications and optimizations of resource management algorithms.
5.5 Comparative analysis of existing cloud platforms
Several systems have been researched for developing a cloud platform, consequently
presenting a summary of comparative advantages and disadvantages of each system of two
categories of the public and private cloud deployment models.
The analysis analyzed systems such as Amazon [56], Google [59] and Microsoft
Azure [61], but also open source systems such as OpenStack [64], CloudStack [65],
Eucalyptus [66], OpenNebula [67], Nimbus [68], OpenCirrus [69] and SlapOS [70],
considering the requirements defined above for the experimental platform, finally justifying
the choice of choosing an open source platform, namely SlapOS.
SlapOS is a decentralized system for building open source cloud platform that can
automate the deployment and configuration of applications in a heterogeneous environment. It
can operate at levels of service delivery cloud IaaS, PaaS and SaaS, so it can be used whether
we use our own hardware infrastructure or a public one. In addition, it allows implementing a
mechanism for planning and resource allocation according to an auction model with modules
including metering and billing. SlapOS also provides a portal and interface for customer
registration, which processes requests for resources and monitoring information is displayed,
but also provides a development environment to transform any software application into a
SaaS service.
5.6 Summary
To choose a cloud platform, we have proposed a two-step approach, which consists in
a first step of mathematical modeling, and then to simulate the platform in order to determine
requirements and its behavior, depending on the applications it will run. We have analyzed
the modeling methods of the main features of a cloud system, and completed a study of the
different simulation applications, and their applicability for different use cases of cloud
systems. Depending on the functions of the simulator, we can implement complex algorithms
for planning and resource allocation, taking into account the mechanisms of virtualization and
network topology, but also have the possibility of generating processing tasks as varied in
terms of computing and communication models.
Chapter 6. Development of the Open Source Cloud
Platform SlapOS
6.1 Introduction
In this chapter we present the architecture aspects and main features of the SlapOS
platform, which was chosen to build a cloud platform from the research presented in the
previous chapter.
6.2 SlapOS Architecture
SlapOS architecture is based on the concept of Master and Slave, as shown in Fig. 6.1,
which will be detailed further in terms of software and the functionality of a platform for
distributed cloud. Master nodes are central directory nodes cloud system, serving to allocate
processes to Slave nodes and keep track of the situation of each slave node and software that
are installed on each node. Slave nodes can be installed on any computer, both in data centers
and in private networks, and their role is to install and run software processes.
Slave
Slave
Office
Home
Slave
Datacenter
Slave
Moble
device
Master
Client
Fig. 6.1 Proposed Open Source Distributed Architecture
SlapOS Software has a kernel consisting of an hierarchical architecture that is built on
an POSIX operating system, and the following modules: SLAPGrid, Supervisord [71] and
Buildout [78], as shown in Fig. 6.2.
POSIX (GNU/Linux)
SLAPGrid
Buildout
Supervisord
Fig. 6.2 SlapOS Slave Software Architecture
6.3 SlapOS Protocol
SlapOS works based on the SLAP protocol, which is an acronym for "Simple
Language for Accounting and Provisioning", as presented in Fig. 6.3. It is independent of
programming language, operating for experimental platform implemented by Python
implemented SLAPGrid Slave node, and the corresponding Python module ERP5 SLAP
Cloud Engine Master node.
Master NodeSlave Node
http: set-capacity
Once
http: get-sr-list
5 min
http: get-cp-list
5 min
1 sec
websocket
http: post-accounting
1 zi
Fig. 6.3 Proposed SLAP Protocol
After some time, a typical SlapOS Node will include multiple software applications
and, for each software application, multiple instances, each of which running in a different
process, as depicted in Fig. 6.4
POSIX (GNU/Linux)
SLAPGrid
Buildout
Supervisord
Partition
App. 1
Partition
App. N
Application
Computer
partitions
User
Folder
Shared
Folder
SlapOS
Kernel
Fig. 6.4 Proposed Cloud Service and Application Layers
As shown in Fig. 6.5 a computer partition is assigned a dedicated user N (slapuserN)
and a dedicated directory (/srv/slapgrid/slappartN) and several addresses on the network
connection: global IPv6 address, an IPv4 private and emulated Ethernet interface (slaptapN).
In addition, a public IPv4 address can be assigned, or a disk storage unit type (/dev/sdaN).
Partiție Aplicație N
Director dedicat
(/srv/slapgrid/
slappartN)
Utilizator dedicat
(slapuserN)
Disc de
stocare de tip
bloc
(/dev/sdaN)Ethernet emulat
(slaptapN)
IPv6
global
IPv4
privat
IPv4
public
Fig. 6.5 Block diagram of a computing partition on a SlapOS Slave
6.4 Summary
By running instances of applications as processes in place to create a virtual machine
for each application, as other model systems such as Amazon AWS EC2 cloud was shown to
slap allow more efficient use of hardware resources.
In conclusion, SlapOS is the recommended platform for open source application
developers to transform their applications to a SaaS model, including their migration to IPv6.
Chapter 7. Implementing a Cloud Communication Node
7.1 Introduction
This chapter presents the methodology of implementing a cloud node using SlapOS
platform. Also, we will describe scenarios to conceptually validate the cloud platform for
communications applications.
7.2 Hardware and software characteristics of the cloud
platform
Table 7.1 presents the hardware specifications of the two servers Fujitsu Siemens and
the 3 HP servers that were used to build private cloud system infrastructure presented in this
paper.
Table 7.1 – Hardware specifications of the HP and Fujitsu Servers
Server 1 Fujitsu RX300S4
Processor Intel Xeon E5405 @ 2GHz
Cores 4
RAM 2 x 2GB
Storage 2 x 140GB 15k SAS, 4 x 2TB 7.2k SATA
Hardware RAID LSI MegaRAID 5/6 SAS 256MB
Network 3x Gigabit
Server 2 Fujitsu RX300S6
Processor 2 x Intel Xeon E5506 @2,133 GHz
Cores 2 x 4
RAM 16GB
Storage 3 x 1TB 7.2 SATA, 3x 2TB SATA
Hardware RAID LSI MegaRAID
Network 3x Gigabit
Server 3 HP ProLiant DL380p Gen8
Processor 2 x Intel Xeon E5-2620 6-Core (2.00GHz 15MB L3 Cache)
Cores 2 x 6
RAM 32 GB
Storage 4 x 1TB 7.2 SAS, 3x 2TB SATA
Hardware RAID Smart Array P420i/1GB with FBWC (RAID 0/1/1+0/5/5+0)
Network 4 x Gigabit
In Fig. 7.1 we present the structure of the cloud network used for implementing the
SlapOS cloud system.
82.78.81.171
10 Partiții
82.78.81.172
20 Partiții
82.78.81.173
20 Partiții
141.85.151.166
20 Partiții
Ec2-176-34-68-121.eu-west-1.compute.amazonaws.com
10 Partiții
BCI – Calculator 5
20 Partiții
BCI – Calculator 6
20 Partiții
BCI – Calculator 7
20 Partiții
BCI-Utilizator 1
BCI-Utilizator 2
BCI-Utilizator 3
BCI – Utilizator 4
Master SlapOS.org
141.85.151.167
20 Partiții
Fig. 6.5 Cloud Network for implementing the SlapOS system
The development of Ubuntu alongside cloud networks was one of the reasons for
choosing it as the operating system implementation slap nodes. Both versions of Ubuntu
Server, the latest 13.10 12.04 LTS 64-bit and 64-bit servers were used in BEIA and ETTI
groups to host cloud nodes.
7.3 Methodology for Installation and Configuration of a
SlapOS Node
As a general approach, we first installed a UNIX operating system, Ubuntu Linux is
preferred, as was argued above. The next step is to configure network parameters and
downloading sources for installation, after which will install the kernel modules: Buildout,
Supervisord and SLAPGrid. Finally, we will set up the partitions and will assign different
applications to test the cloud system.
For a SlapOS Slave node composed of several computing partitions we will implement
the SLAP protocol and will demonstrate: how to create folders on a slave node, the allocation
of network interfaces to each partition, creating configuration files based on Buildout for
allocation and instantiation of applications, controlling processes by Supervisord.
SlapOS Master node will then be used for requesting an instance for various
communications applications and will seek a free partition according to specified SLA
parameters. SlapOS Slave node will install the chosen software on a free partition and start an
instance of the application, and when it is no longer needed it will be deleted.
Finally, we show how to implement on the SlapOS Master the mechanisms for
monitoring and metering of the resources consumed by the nodes SlapOS Slave processes.
7.4 Summary
At the extreme limit performance computing partition can include multiple instances
of the same application, consuming cloud resources node. To increase performance, it is
recommended that the principle of installing applications on each partition elementary
calculation, which allows network expansion and optimization of resources due to their
allocation granularity.
By running instances of applications as processes in place to create a virtual machine
for each application, as other model systems such as Amazon AWS EC2 cloud was shown to
slap allow more efficient use of hardware resources.
In conclusion, the slap is the recommended platform for open source application
developers to transform the model of SaaS applications, including their migration to IPv6
achieve.
Chapter 8. Conclusions and Future Work
8.1 General Conclusions and Main Contributions
In the thesis we analyzed the theoretical concepts of cloud computing systems in order
to implement a cloud platform for communications applications.
We researched current state of the art for projects aimed at the development of cloud
solutions and the analysis of the main problems encountered in previous projects have been
proposed and evaluated possible solutions. The actuality and the need for cloud solutions
was analyzed for interconnecting a large number of heterogeneous components over the
Internet and defined potential communications applications that can be deployed as a service (
SaaS). This approach allows access to the resources of the communications from any location
connected to the Internet, as well as the flexibility to add new services by linking existing
applications. Cloud systems have been studied from two different points of view that
technological change but also as an evolution of existing computer systems organization ,
considered as a basis for future concepts IoT / IOE . Current research trend was analyzed
with the focus on the implementation of SLA service levels for cloud service assurance in
large data centers. However, in case of force majeure, SLA requirements are no longer
applicable , so we proposed one possible solution, using a decentralized cloud computing
model for resource aggregation in a standard computer network located in homes, offices,
universities or smaller data centers. This approach brings an innovative solution for the issues
of energy efficiency, decentralized cloud nodes could be used depending on the season and
for heating rooms in which they are housed. Security issues were analyzed, being well
known that communication protocols in which we rely on cloud nodes to report resources
consumed, or mechanisms that are based on the belief that client elements provide valid
metering values, are major security problems. Consequently, the proposed solution are
developing security mechanisms that prevent nodes from falsifying reported metering values
and use encrypted connections, also being implemented an X.509 certificate authentication
scheme that prevents unauthorized nodes to connect to the cloud system and intercept data.
Another issue that hinders the deployment of cloud computing is the low level of adoption of
IPv6 globally is below 2%. Surprisingly, Romania is at the beginning of 2013 ranked first
worldwide in the availability of IPv6 connections (8.55 %, France ranked secondly with 5.09
%), representing a significant support for the implementation of cloud solutions using IPv6
advantages. We emphasized that computers and communications are very closely
interconnected - when one of the two areas is progressing, then progress can be critical for the
other. The cloud systems have become an alternative to grid or HPC solutions when the
Internet began to provide broadband capabilities with transfer latency quality guarantees and
low costs. Meanwhile, the current communications networks could not function without
performance management systems or switches controlled by software. We measured
effectiveness of parallelizing the processing tasks by defining the parameter that measure the
acceleration of the processing speed. As people learned to unite in groups to work more
effectively in parallel to achieve a common result, so network of entities can be organized to
perform complex tasks and solve problems that cannot be solved by a single entity. In
terms of standardization, we investigated several organizations and initiatives in this regard,
mostly from the grid systems or network storage. Notable are Romanian contributions to
standardization, through various collaborative research projects with European funding , or
cloud services on technological components based cloud systems . We presented
technologies and systems to achieve cloud computing hardware and software solutions, and
the role of communication networks in such solutions. We evaluated technologies such as grid
computing, virtualization systems and communications issues for interconnecting IPv6
network resources in a cloud. After studying the technologies on which cloud systems achieve
abstraction levels , and thus to provide cloud services to users and developers, we noted the
need to use virtualization technologies for operating systems, platforms, storage systems,
network and applications. Also, we examined the technological complexity that is often
hidden by cloud services and virtualization consists of interconnecting levels and interfaces of
SOA and Web 2.0 Services. The evolution of cloud systems from decentralized distributed
systems was motivated by listing common characteristics and assessing the applicability of
these systems solutions to problems found in cloud systems, and ways of integration between
systems. Furthermore, we presented the development of P2P (peer -to -peer ) cloud systems,
described their advantages and disadvantages, as well as architectures, applications and types
of networks that can be applied to cloud systems. Although not negligible, pure P2P networks
structured or structured have certain disadvantages. Therefore, combining P2P networks with
client- server model for centralized management and coordination services are envisioned to
provide resource management and QoS level without load network with broadcast messages
broadcast type, and maintaining the robustness by that all nodes have the same role in the
network. We proposed an adaptive model of a P2P protocol for decentralized cloud systems,
and an algorithm for calculating the reputation and performance in a cloud topology of multi-
master -slave ring. We analyzed the possibility of adapting the topology and metrics of P2P to
cloud systems, including efficient algorithms for unstructured and heterogeneous networks are
considered, such as auction type , or genetic/epidemic algortihms. They are considered
already at the stage of technology that is moving towards maturity stage, timeliness justified
the proposed concept in cloud communications. We studied cloud architectures and
algorithms that allow deployment of communications in the form of different levels of cloud
services. Cloud architectures were analyzed from two different perspectives, from an
organizational perspective and from a technical standpoint. Considering organizational
aspects we studied models of implementation, involving a distinction between the domain of
users and service providers, the way how they are organized and separated. We presented a
comparative study of different deployment models of cloud systems as public, private,
community or hybrid type, and also introduced the concept of cloud perimeter. He selected
the hybrid deployment model that combines two or more of deployment models (private,
public, community) that remain independent entities but are bound together by interfaces or
technology , either standardized or proprietary , allowing for portability data and applications
(including for example traffic balancing ). In addition to this, an innovative architecture model
is proposed to organize a cloud resources on three levels, creating a perimeter network
offering cloud computing services, storage and networking between devices and traditional
data centers (central cloud) , not but localized exclusively to the perimeter network but also
device-level (local cloud) . We treated in detail the subject of resource management in
cloud systems, as determined specific features and proposed algorithms for resource
management of such a system. To manage cloud services in a network, we have defined and
analyzed five factors characteristics and were analyzed separately six additional factors that
relate to the overall management of cloud resources. We implemented a policy
management system, so that the control of cloud resources can be achieved through four types
of mechanisms, which rely on a well-defined approach instead of ad hoc methods: control
theory, the utility model, machine learning systems and economic mechanisms. Implementing
algorithms that have been evaluated according to management policies are grouped into the
following categories: access control, efficient allocation of capacity, network load balancing ,
energy optimization and QoS guarantee. We proposed control algorithms based on utility
functions type , machine learning, auctions and genetic paradigms. In the first algorithm,
the goal was to demonstrate a methodology that can be applied for optimal management of
resources, based on the concepts of optimal control theory, thus examining the possibility of
expanding to a larger number of servers. It was found that extending the technique from a
single system to a large number of servers in a cloud computing presents a high complexity,
making it difficult if network cloud applications require other models for processing
applications, besides processing of a call stack. Auction type algorithms are suitable for
requirements management packages resources in the cloud, without the need for a model of
the system, but it is difficult to implement in practice for cloud service requests that arrive
randomly, because the algorithm was designed for the case auctions in which bidders respond
simultaneously. To solve this problem we proposed synchronization via an intermediate
proxy, therefore being only necessary to organize regular auctions, but discussed the fact that
a delay in response time does not correspond with the principle of cloud elasticity implies
immediate availability of a set of resources. Proposed Genetic algorithms are an innovative
way to deploy heuristics in artificial intelligence, modeling the processes of natural evolution
in cloud systems. Different types of cloud services were analyzed with the goal to define
how they can be grouped into the category of resources offered in certain levels. Cloud
service attributes were analyzed and we highlighted the difference between different levels of
service delivery, by detailing the service levels of IaaS, PaaS, SaaS and XaaS. Furthermore,
we described several examples of communications applications that can be deployed as
services using a cloud platform. We performed a comparative analysis between various
simulation systems and between major cloud platforms. The aim was to determine the
requirements of an experimental platform cloud and achieve a simulation of the first
applications to be run in the cloud, before proceeding to implement a system. Also, a
simulation was performed for implementing a communications protocol for parallel
computation and evaluated the performance results. Based on the criteria required for an
experimental platform cloud, we justified choosing open source platform SlapOS. As
experimental platform we presented in detail the SlapOS cloud system, analyzing the network
architecture and the structure of components, including performance and security issues. The
platform was implemented in a Master-Slave architecture with ring topology and we detailed
the operation of the protocol for resource control in network nodes. Finally, we described
the implementation of a SlapOS cloud node for a hybrid architecture and carried out
conceptual validation scenarios on the cloud platform for communications applications.
The author participated in the following research projects related to the thesis, the
work being published in a number of 10 deliverables and two research reports:
- CLOUD CONSULTING ( E * 6021 - 305E , during 2011-2013, coordinator France –
Nexedi, coordinator Romania - BEIA Consult Int.)
- SARAT-IWSN (PN-II-PT-PCCA-2011-3.2-1030, during 2012-2015, coordinator -
UPB partner - BEIA Consult Int.)
- eWALL ( FP7-ICT-2013-10/Grant no. 610 658 , during 2013-2016 , coordinator
Denmark - Aalborg Universitet, coordinator Romania - UPB )
The results obtained were validated by a number of 7 articles in ISI proceedings of
international conferences , 10 articles published in international specialized journals and more
than 20 articles published in proceedings of international conferences indexed BDI (IEEE,
ACM , etc. . ) totaling more than 40 citations ;
As a special contribution, we mention the filing of a patent application
("Telemonitoring System" OSIM A00544/2013 deposit number) for a M2M communication
application being developed and experimented on the cloud platform proposed in this thesis.
8.2 Future Work
The author has participated in the team of several research projects in the cloud
domain, and a good part of this thesis was conducted in the framework of the
CloudConsulting research project, which aims to create new technologies that automate the
configuration of a resource planning software system type "Enterprise resource Planning "
(ERP ) and system management software customer relationship type " Customer Relationship
management" (CRM ) for the benefit of small and medium enterprises (SMEs). The thesis
contains the author's direct contributions to this project. A possible continuation of this project
is to improve the security and resilience of this platform, as well as adding new mechanisms
for resource management.
Since the cloud-based computing systems is characterized by a highly dynamic
development was achieved current classification technologies and cloud services at the time
of writing , without claiming to be a complete description , but rather an archetypal approach .
The thesis may be a starting point for the development and implementation of new
architectures and algorithms for cloud systems.
As future work we will investigate how to use the cloud platform for other deployment
scenarios such as smart cities and IoT, with applicability in mobile networks, monitoring of
renewable energy and the environment, but also for areas such as agriculture or health.
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Annexes
Own Publications [1] G. Suciu, O. Fratu, S. Halunga, et.al. „Cloud Consulting: ERP and Communication Application Integration in Open Source Cloud
Systems”, 19th Telecommunications Forum - TELFOR 2011, IEEE Communications Society, ISBN 978-1-4577-1499-3, Belgrade, Serbia, 22-24 Nov. 2011, pp. 578-581.
[2] C.G. Cristian, V. A. Poenaru, G. Suciu, „Multimedia content distribution between core network routers using Peer-to-Peer (P2P)”,19th Telecommunications Forum - TELFOR 2011, IEEE Communications Society, ISBN 978-1-4577-1499-3, Belgrade, Serbia, 22-24 Nov. 2011, pp. 254-257
[3] G. Suciu, S. Halunga, “Cloud Collaboration - Platform for online collaboration in open source distributed cloud systems”, Proceedings of Second International Students Conference on Informatics - Imagination, Creativity, Design, Development (ICDD 2012), ISSN 2069-964X, 26 - 28 April 2012, Sibiu, Romania, pp. 179-187.
[4] G. Suciu, V. A. Poenaru, C. G. Cernat, G. Todoran, L. Militaru “ERP and e-business application deployment in open source distributed cloud systems”, The 11th International Conference on Informatics in Economy (IE 2012), Bucharest University of
Economic Studies Press, ISI Thomson, ISSN 2284-747, București, Romania, 10-11 May 2012, pp. 12-17. [5] L. Militaru, G. Suciu, G. Todoran “The evaluation of the e-learning applications’ quality”, The 11th International Conference on
Informatics in Economy (IE 2012)”, Bucharest University of Economic Studies Press, ISI Thomson, ISSN 2284-747, București, Romania, 10-11 May 2012, pp. 373-377.
[6] G. Suciu, V. Poenaru, C. Cernat, L. Militaru, G. Todoran "A Study of Security in Open Source Cloud Platforms". “Proceedings of the 5th International Conference on Security for Information Technology and Communications (SECITC 2012)”, Bucureşti, Romania, 31 May - 1 June 2012, pp. 125-130.
[7] G. Suciu, C. Cernat, G. Todoran, V. Suciu, V. Poenaru, L. Militaru, S. Halunga, “A solution for implementing resilience in open source Cloud platforms”, Proceedings of 2012 9th International Conference on Communications (COMM)”, IEEE Communications
Society, ISI Thomson, București, Romania, 21-23 Jun. 2012, Ed. Curran Associates, Inc., New York, 2012, pp. 335-338. [8] C. G. Cristian, V. A. Poenaru, and G. Suciu, ”Metric model for IPTV video streaming services distribution in content aware
networks”, Proceedings of 2012 9th International Conference on Communications (COMM)”, IEEE Communications Society, ISI Thomson, București, Romania, 21-23 Jun. 2012, Ed. Curran Associates, Inc., New York, 2012, pp. 285-288.
[9] G. Suciu, O. Fratu, et.al. “Cloud systems for environmental telemetry - A case study for ecological monitoring in agriculture”, XLVII International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2012, Proceedings
of Papers, Vol. 1”(online), Ed. Faculty of Telecommunications, Technical University of Sofia, Veliko Tarnovo, Bulgaria, 28-30 Jun. 2012, pp. 273-276.
[10] V.A. Poenaru, G. Suciu, C.G. Cernat, G. Todoran, L. Militaru “Attacking the cloud”. “XLVII International Scientific Conference on
Information, Communication and Energy Systems and Technologies - ICEST 2012, Proceedings of Papers, Vol. 1”(online), Ed.
Faculty of Telecommunications, Technical University of Sofia, Veliko Tarnovo, Bulgaria, 28-30 Jun. 2012, pp. 175-177. [11] G. Suciu, C. G. Cernat, and G. Todoran, “Cloud Research - Implementing Scientific Research Information Systems in Open Source
Cloud Platforms”, Fifth Romania Tier 2 Federation "Grid, Cloud & High Performance Computing Science", RO-LCG 2012, IEEE,
ISI Thomson, ISBN: 978-1-4673-2242-3, Cluj-Napoca, Romania, 25-27 Oct. 2012, pp. 31-34. [12] L. Militaru, G. Suciu, G. Todoran “The Evaluation of the e-Learning Applications’ Quality”, 54th International Symposium -
Proceedings ELMAR 2012, Croatian Society Electronics in Marine, IEEE Communications Society, Zadar, Croatia, 12-14 Sep. 2012, pp. 165-170.
[13] G. Suciu, L. Militaru, C.G. Cernat, V.A. Poenaru “Platform for online collaboration and elearning in open source distributed cloud systems”, 54th International Symposium - Proceedings ELMAR 2012”, Croatian Society Electronics in Marine, IEEE Communications Society, Zagreb, 12-14 Sep. 2012, pp. 337-340.
[14] G. Suciu, R. Cheveresan, M. Cealera, G. Todoran, “Sisteme informatice Open Source pentru gestiunea companiilor din domeniul energetic”, Simpozionul National de Informatica, Automatizari si Telecomunicatii in Energie, SIE2012, Sinaia, Romania, 24 - 26 Oct. 2012, pp. 1-4.
[15] G. Suciu, L. Militaru, G. Todoran “ERP and E-Business Application Deployment in Open Source Distributed Cloud Systems”. “Database Systems Journal - DBJournal”, Vol. III, No. 3 / 2012 (online), Editura ASE, București, 2012, pp. 3-12, indexat BDI
(Google Scholar, RePEc) [16] L. Militaru, G. Suciu, G. Todoran “The Use of Automated Software Tools in Evaluating an e-Learning Platform Quality”. “Journal of
Mobile, Embedded and Distributed Systems - JMEDS”, Vol. 4, No. 3, 2012 (online), pp. 150-158, indexat BDI (Google Scholar,
IndexCopernicus) [17] G. Suciu, V. Poenaru, C.Cernat, L. Militaru, G. Todoran “A Study of Implementing an Information Security Management System for
Open Source Cloud Computing”. “Journal of Mobile, Embedded and Distributed Systems - JMEDS”, Vol. 4, No. 3, 2012 (online), pp. 168-174, indexat BDI (Google Scholar, IndexCopernicus)
[18] L. Militaru, G. Suciu, G. Todoran “The use of expert systems in evaluating the quality of universities websites”.“Asigurarea
calităţii”, numărul 72, vol. XVIII (2012), Editura Tehnică, București, Oct. 2012, pp. 19-25. [19] G. Suciu, L. Militaru, G. Todoran “Cloud systems for monitoring environmental parameters - Case study for telemetry of nuclear
weapon radiation”, Proceedings of UN Youth Student Conference on International Relations, Security and Economy” (online), 26 - 28 Oct. 2012, pp. 1-12.
[20] G. Suciu, V. A. Poenaru, L. Nae , A. Florescu , L. Militaru, G. Todoran “Data mining in distributed cloud systems - an open source test platform for automated ERP configurations”.“Scientific Bulletins of Politehnica University”(propus spre publicare)
[21] L. Militaru, G. Suciu, G. Todoran “The use of expert systems in building the quality model of a web-based learning platform”, 11th International Conference Advances in Web-Based Learning – ICWL 2012”, Ed. seria “Lecture Notes in Computer Science”, vol.
7558, Springer, Heidelberg, ISBN 978-3-642-33641-6, Sinaia, Romania, 2-4 Sep. 2012, pp. 318-327, indexat BDI (Google Scholar) [22] L. Militaru, G. Suciu, G. Todoran ”The use of expert systems in designing the quality evaluation of an e-learning platform”.
“Proceedings of The International Conference INnovation and Collaboration in Engineering Research (INCER) 2012” (online),
Universitatea Politehnica din Bucureşti, 2-4 Jul. 2012
[23] L. Militaru, G. Suciu, et.al. “Rule based expert systems running over the cloud”. “Proceedings of The International Conference
‘INnovation and Collaboration in Engineering Research’ (INCER) 2012” (online), Universitatea Politehnica din Bucureşti, 2-4 Jul. 2012
[24] G. Suciu, L. Militaru, G. Todoran “Data Mining in Distributed Cloud Systems - An open source test platform for automated ERP configurations”. “Proceedings of The International Conference ‘INnovation and Collaboration in Engineering Research’ (INCER)
2012” (online), Universitatea Politehnica din Bucuresti, 2-4 Jul. 2012. [25] L. Militaru, G. Suciu, G. Todoran, “The use of expert systems in designing the quality evaluation of an e-learning platform”.
“Proceedings of the 2012 Fourth International Conference on INtelligent Networking and COllaborative Systems - INCOS”
(electronic), Universitatea Politehnica din Bucureşti, IEEE Computer Society, Bucharest, Romania,19-21 Sep. 2012, pp. 495-496. [26] L. Militaru, G. Suciu, G. Todoran “Rule based expert systems over cloud”. “Volumul Simpozionului Studenţesc de Electronică și
Telecomunicaţii, ediţia a VIII-a”(electronic), Universitatea Tehnică din Cluj-Napoca, 2012. [27] G. Suciu, L. Militaru, G. Todoran “Cloud e-Business – A Case Study for ERP Application Deployment in Open Source Distributed
Cloud Systems”. “Volumul Simpozionului Studenţesc de Electronică și Telecomunicaţii, ediţia a VIII-a”(electronic), Universitatea
Tehnică din Cluj-Napoca, 2012. [28] J. Cropotova, G. Suciu, “A novel approach to the monitoring of agri-food supply chain through Cloud Computing in developing
countries”, 1-st Conference «Physics for Development», Brussels, Belgium, 11-12 Oct. 2012, pp. 1-4. [29] G. Suciu, E. G. Ularu, R. Craciunescu „Public versus Private Cloud Adoption – a Case Study based on Open Source Cloud
Platforms”, 20th Telecommunications Forum - TELFOR 2012, IEEE Communications Society, ISI Thomson, ISBN 978-1-4673-2983-5 , Belgrade, Serbia, 20-22 Nov. 2012, pp. 494 – 497.
[30] G. Suciu, S. Halunga, “Cloud Content Distribution Networks for DVB Applications”, Constanța Maritime University Annals, vol. 13,
no. 18 (online), ISSN: 1582-3601, Dec. 2012, pp. 205-208, indexat BDI (Google Scholar) [31] E.G. Ularu, F.C. Puican, G. Suciu, A. Vulpe, G. Todoran, ”Mobile Computing and Cloud maturity - Introducing Machine Learning
for ERP Configuration Automation”, Informatica Economică, vol. 17, no. 1 (online), doi: 10.12948/issn14531305/17.1.2013.04, Jan. 2013, pp. 40-52, indexat BDI (Google Scholar, IndexCopernicus, INSPEC, RePEc, CNCSIS B+)
[32] E.G. Ularu, F.C. Puican, A. Apostu, G. Suciu, A. Vulpe, ”Analytical Databases for the Cloud and Virtualization”, 12th International
Conference on Informatics in Economy (IE 2013), ISSN 2284-7472, ISI Thomson, Bucharest, Romania, 25-28 Apr. 2013, pp. 337-341.
[33] A. Apostu, F. Puican, G. Ularu, G. Suciu, G. Todoran, “Study on advantages and disadvantages of Cloud Computing – the advantages of Telemetry Applications in the Cloud”, 13th International Conference on Applied Computer Science (ACS13) - Recent
Advances in Applied Computer Science and Digital Services, 978-1-61804-179-1, Morioka City, Iwate, Japan, 23-25Apr. 2013, pp. 118-123, indexat BDI (Google Scholar)
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[35] G. Suciu, A. Vulpe, S. Halunga, O. Fratu, G. Todoran, V. Suciu, “Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things”, 19th International Conference on Control Systems and Computer Science (CSCS), IEEE, ISBN 978-0-7695-4980-4, Bucharest, Romania, 25-28 May 2013, pp. 513-518.
[36] G. Suciu, A. Vulpe, G. Todoran, G.; Cropotova, J.; Suciu, V., “Cloud Computing and Internet of Things for Smart City Deployments”, 7th International Conference Challenges of the Knowledge Society (CKS 2013), ISSN 2068-7796, Index Copernicus Journals Master List ICV 3.86, Bucharest, Romania, 17-18 May 2013, pp. 1409-1416.
[37] G. Suciu, S. Halunga, O. Fratu, A. Vasilescu, V. Suciu, “Study for Renewable Energy Telemetry using a Decentralized Cloud M2M System”, 15th International Symposium on Wireless Personal Multimedia Communications (WPMC), IEEE, ISBN 978-87-92982-52-0, Atlantic City, SUA, 24-28 Jun. 2013, pp. 1-5.
[38] G. Suciu, S. Halunga, A. Vulpe, V. Suciu, "Generic Platform for IoT and Cloud Computing Interoperability Study", 2013 11th International Symposium on Signals, Circuits and Systems (ISSCS), IEEE, ISBN 978-1-4673-6143-9, Iasi, Romania, 10-12 Jul 2013, pp.1-4.
[39] A. Apostu, G. Suciu, A. Ochian, A. Vulpe, „Cloud Computing as a key enabler for pricing optimization in Small and Medium Sized Enterprises,” 6th International Conference “Small and Medium Sized Enterprises in a Globalized World”, ISBN 978-606-526-155-6, Cluj-Napoca, 25-28 Sept. 2013, pp. 231-241.
[40] G. Suciu, G. Tdoran, ”Cloud M2M Platform For Renewable Energy Tele-Monitoring”, Scientific Bulletin UPB, nr. special, seria C, 2013.
[41] G. Suciu, G. Todoran, A. Apostu, F. C. Puican, G. Ularu ”Mobile Cloud for Telemetry Applications”, Acceptat Journal of Electrical
Engineering and Computer Science, ISSN 0013-5852, Ljubljana, Slovenia, 2013, indexat BDI (INSPEC, COMPENDEX) [42] G. Suciu, C. Voicu, G. Todoran, A. Martian, S. Halunga, C. Butca, ”Network Cloud Simulator for Modelling Trust in Cognitive
Radio Applications”, Acceptat 21st Telecommunications Forum TELFOR, IEEE, 2013.