cloud computing lec7 - kursused€“ building a fault-tolerant bsp framework (newt) [kromonov et...

Basics of Cloud Computing – Lecture 7

More AWS and Cloud-based

Research at Mobile & Cloud Lab

Satish Srirama

Outline

• More Amazon Web Services

• How we are using cloud

• Cloud based Research @ Mobile & Cloud Lab

3/22/2016 Satish Srirama 2/41

Cloud Providers and Services – we

already discussed• Amazon Web Services

– Amazon EC2

– Amazon S3

– Amazon EBS

– Amazon Elastic Load Balancing

– Amazon Auto Scale

– Amazon CloudWatch

• Eucalyptus

• OpenStack

• SciCloud

• Management providers– ElasticFox

– RightScale

• PaaS– Google AppEngine

– Windows Azure


MORE AWS

3/22/2016 Satish Srirama 4

AWS we discuss

• AWS Management Console

• AWS Identity and Access Management

• AWS Elastic Beanstalk

• AWS CloudFormation

• Amazon Simple Workflow Service

• Amazon Elastic MapReduce


AWS Management Console

• Hope some of you have started using Amazon accounts

• You can manage your complete Amazon account with management console (Similar to Hybridfox)– AMI Management

– Instance Management

– Security Group Management

– Elastic IP Management

– Elastic Block Store

– Key Pair management etc.

• Have different panes for different services


AWS Management Console - screenshot

https://console.aws.amazon.com/


AWS Identity and Access Management

(IAM)

• How can an enterprise or group of people use a single credit card?

• Manage IAM users

– Create new users and manage them

– Create groups

• Manage permissions

– Creating policies

• Manage credentials

– Create and assign temporary security credentials


IAM policy

• Example policy giving access to complete EC2

http://aws.amazon.com/iam/


AWS Elastic Beanstalk

• Enables to easily deploy and manage applications in the

AWS cloud

– Simply upload a bundle of the applications built using .NET, PHP

and Java technologies

• Automatically handles the deployment details of capacity

provisioning, load balancing, auto-scaling, and

application health monitoring

• Something similar to PaaS

• One retains full control over the AWS resources powering

the application

– You can access the underlying resources at any time


AWS Elastic Beanstalk

• AWS EB is built using familiar software stacks such as

the Apache HTTP Server for PHP, IIS 7.5 for .NET, and

Apache Tomcat for Java

• There is no additional charge for Elastic Beanstalk

– Only the underlying AWS resources (e.g. Amazon EC2,

Amazon S3) are charged

• Leverages AWS services such as Amazon EC2, S3, SNS,

ELB, and Auto Scaling to deliver the same highly

reliable, scalable, and cost-effective infrastructure

http://aws.amazon.com/elasticbeanstalk


AWS CloudFormation

• Provides an easy way to create and manage a collection of related AWS resources, provisioning and updating them in an orderly and predictable fashion

• It is based on templates model– Templates describe the AWS resources, the associated

dependencies, and runtime parameters to run an app.

– The templates describe stacks, which are set of software and hardware resources.

– Something similar to CloudML and RightScale server templates

• Hides several details– How the AWS services need to be provisioned

– Subtleties of how to make those dependencies work.


AWS CloudFormation• Amazon provides several pre-built templates to

start common apps as:

– WordPress (blog)

– LAMP stack

– Gollum (wiki used by GitHub)

– …

• There is no additional charge for AWS CloudFormation. You pay for AWS resources (e.g. EC2 instances, Elastic Load Balancers, etc.) http://aws.amazon.com/cloudformation/


Amazon Simple Workflow Service• A workflow service for building scalable, resilient

applications

• Reliably coordinates all of the processing steps within

applications

– such as business processes, sophisticated data analytics

applications, or managing cloud infrastructure services

• Manages task execution dependencies, scheduling, and

concurrency

• Provides simple API calls from code written in any language

• Capable to run on EC2 instances, or any of the customer’s

machines located anywhere in the world


Amazon Simple Workflow Service

• Maintains application state

• Tracks workflow executions and logs their

progress

• Holds and dispatches tasks

• Controls which tasks each of the application

hosts will be assigned to execute

• http://aws.amazon.com/swf/


Amazon Elastic MapReduce

• Web interface and command-line tools for running Hadoop jobs on EC2

• Data stored in Amazon S3

• Monitors job and shuts machines after use

• Running a job

– Upload job jar & input data to S3

– Create the cluster

– Create a Job Flow as steps

– Wait for the completion and examine the results

3/22/2016

http://aws.amazon.com/elasticmapreduce/

Satish Srirama 16/41

Other interesting AWS

• Amazon Relational Database Service

– Provides access to the capabilities of familiar

database engines

– MySQL, Oracle or Microsoft SQL Server

• NoSQL databases

– Simple DB

– DynamoDB


CLOUD BASED RESEARCH @

MOBILE & CLOUD LAB


Scientific Computing on the Cloud

• Public clouds provide very convenient access to computing resources

– On-demand and in real-time

– As long as you can afford them

• High performance computing (HPC) on cloud

– Virtualization and communication latencies are major hindrances [Srirama et al, SPJ 2011; Batrashev et al, HPCS 2011]

• Things have improved significantly over the years

– Research at scale

• Cost-to-value of experiments


Adapting Computing Problems to

Cloud• Reducing the algorithms to cloud computing frameworks

like MapReduce [Srirama et al, FGCS 2012]

• Designed a classification on how the algorithms can be adapted to MR– Algorithm � single MapReduce job

• Monte Carlo, RSA breaking

– Algorithm � n MapReduce jobs• CLARA (Clustering), Matrix Multiplication

– Each iteration in algorithm � single MapReduce job• PAM (Clustering)

– Each iteration in algorithm � n MapReduce jobs • Conjugate Gradient

• Applicable especially for Hadoop MapReduce


Issues with Hadoop MapReduce

• It is designed and suitable for:

– Data processing tasks

– Embarrassingly parallel tasks

• Has serious issues with iterative algorithms

– Long „start up“ and „clean up“ times ~17 seconds

– No way to keep important data in memory between MapReduce job executions

– At each iteration, all data is read again from HDFS and written back there at the end

– Results in a significant overhead in every iteration


Alternative Approaches

• Restructuring algorithms into non-iterative versions – CLARA instead of PAM [Jakovits & Srirama, Nordicloud 2013]

• Alternative MapReduce implementations that are designed to handle iterative algorithms [Jakovits and Srirama, HPCS 2014]

– E.g. Twister, HaLoop, Spark

• Alternative distributed computing models– Bulk Synchronous Parallel model [Valiant, 1990] [Jakovits et al,

HPCS 2013]

– Building a fault-tolerant BSP framework (NEWT) [Kromonov et al, HPCS 2014]


Communication pattern of Cluster vs

Cloud

• Cloud has huge troubles with communication/transmission latencies– Virtualization technology is the culprit

• Performance Comparison of virtual machines and Linux containers (e.g. Docker)

3/22/2016 Satish Srirama23/31

[Srirama et al, SPJ 2011]

Remodeling Enterprise Applications for

the Cloud

• Remodeling workflow based applications for the cloud

– To reduce communication latencies among the components

– Intuition: Reduce inter-node communication and to increase the intra-node communication

• Auto-scale them based on optimization model and CloudML

3/22/2016

[Srirama and Viil, HPCC 2014]


Migrating Scientific Workflows to the

Cloud

• Workflow can be represented as weighted directed acyclic graph (DAG)

• Partitioning the workflow into groups with graph partitioning techniques [Srirama and Viil, HPCC 2014]

– Such that the sum of the weights of the edges connecting to vertices in different groups is minimized

– Utilized Metis’ multilevel k-way partitioning

• Scheduling the workflows with tools like Pegasus

– Considered peer-to-peer file manager (Mule) for Pegasus


3/22/2016

[Tomi T Ahonen]

Satish Srirama 26

Mobile Applications

• One can do interesting things on mobiles directly

– Today’s mobiles are far more capable

– Location-based services (LBSs), mobile social networking, mobile commerce, context-aware services etc.

• It is also possible to make the mobile a service provider

– Mobile web service provisioning [Srirama et al, ICIW 2006; Srirama

and Paniagua, MS 2013]

– Challenges in security, scalability, discovery and middleware are studied [Srirama, PhD 2008]

– Mobile Social Network in Proximity [Chang et al, ICSOC 2012; PMC

2014]


However, we still have not achieved

• Longer battery life

– Battery lasts only for 1-2 hours for continuous computing

• Same quality of experience as on desktops

– Weaker CPU and memory

– Storage capacity

• Still it is a good idea to take the support of external resources for building resource intensive mobile applications


Mobile Cloud Applications

• Bring the cloud infrastructure to the proximity

of the mobile user

• Mobile has significant advantage by going

cloud-aware

– Increased data storage capacity

– Availability of unlimited processing power

– PC-like functionality for mobile applications

– Extended battery life (energy efficiency)


Mobile Cloud – Our interpretation

• We do not see Mobile Cloud to be just a scenario where mobile is taking the help of a much powerful machine!!!

• We do not see cloud as just a pool of virtual machines

• Mobile Cloud based system should take advantage of some of the key intrinsic characteristics of cloud efficiently

– Elasticity & AutoScaling

– Utility computing models

– Parallelization (e.g., using MapReduce)


Mobile Cloud Binding Models

3/22/2016

Task Delegation Code Offloading

[Flores & Srirama, JSS 2014]

[Flores et al, MoMM 2011]

Mobile Cloud

[Flores and Srirama, MCS 2013]


MCM – enables

• Interoperability between different Cloud Services (IaaS, SaaS, PaaS) and Providers (Amazon, Eucalyptus, etc)

• Provides an abstraction layer on top of API

• Composition of different Cloud Services

• Asynchronous communication between the device and MCM [Warren et al, IEEE PC 2014]

• Means to parallelize the tasks and take advantage of Cloud’s intrinsic characteristics

3/22/2016

[Flores et al, MoMM 2011]


MCM applications

• CroudSTag [Srirama et al, MobiWIS 2011]

– Social group formation with people identified in Pictures/Videos

• Zompopo [Srirama et al, NGMAST 2011]

– Intelligent calendar, by mining accelerometer sensor data

• Bakabs [Paniagua et al, iiWAS-2011]

– Managing the Cloud resources from mobile

• Sensor data analysis– Human activity recognition

– Context aware gaming

– MapReduce based sensor data analysis [Paniagua et al, MobiWIS 2012]

• SPiCa: A Social Private Cloud Computing Application Framework [Chang et al, MUM 2014]


Code Offloading - Major Components

• Major research challenges – What, when,

where and how to offload?

• Mobile– Code profiler

– System profilers

– Decision engine

• Cloud based surrogate platform

3/22/2016

[Flores and Srirama, MCS 2013]


Challenges and technical problems

• Inaccurate code profiling– Code has non-deterministic behaviour during runtime

• Based on factors such as input, type of device, execution environment, CPU, memory etc.

– Some code cannot be profiled (e.g. REST)

• Integration complexity– Dynamic behaviour vs Static annotations

• E.g. Static annotations cause unnecessary offloading

• Dynamic configuration of the system

• Offloading scalability and offloading as a service– Surrogate should have similar execution environment

– Should also consider about resource availability of Cloud

3/22/2016

[Flores et al, IEEE Communications Mag 2015]


Practical adaptability of offloading

Applications that can benefit became limited with increase in device capacities 36/41

Internet of Things (IoT)

• “The Internet of Things allows people and

things to be connected Anytime, Anyplace,

with Anything and Anyone, ideally using Any

path/network and Any service.” [European

Research Cluster on IoT]

• More connected devices than people

• Cisco believes the market size will be $19

trillion by 2025

3/22/2016 Satish Srirama37

IoT - Scenarios

• Environment Protection

• Smart Home

• Smart Healthcare

• Smart Agriculture

[Kip Compton][Perera et al, TETT 2014]


Internet of Things – Challenges

Sensors Tags Mobile Things

Appliances & Facilities

How to interact

with ‘things’

directly?

How to provide

energy efficient

services?

How do we

communicate

automatically?

[Chang et al, ICWS 2015]

[Chang et al, SCC 2015;

Liyanage et al, MS 2015]


Cloud-based IoT

Sensing and smart devices

Connectivity nodes &

Embedded processing

Remote Cloud-based

processing

Proxy Storage

Processing


Further research focus in IoT

• We have established IoT and Smart Solutions

Lab with Telia company support

• Interesting topics

– Discovery of IoT devices

– Study of IoT based middlewares

– Study of available IoT platforms

• Amazon IoT

• Open IoT

3/22/2016 41 Satish Srirama

IoT Data Processing on Cloud

• Enormous amounts of unstructured data– In Zetabytes (1021 bytes) by 2020 [TelecomEngine]

– Has to be properly stored, analysed and interpreted and presented

• Big data acquisition and analytics– Is MapReduce sufficient?

• MapReduce is not good for iterative algorithms [Srirama et al, FGCS 2012]

– IoT mostly deals with streaming data• Message queues such as Apache Kafka can be used to buffer and feed

the data into stream processing systems such as Apache Storm

• Apache Spark streaming

• How to ensure QoS aspects such as security of data?– Anonymization and Expiry of data?

• Especially for the personal data


WE ALWAYS WELCOME NEW IDEAS!

email: [email protected]


This week in lab

• Work with Google AppEngine


Next Week

• Summarize what we have learnt

• How to prepare for the examination


References

• Check Amazon videos and webinars at http://aws.amazon.com/resources/webinars/

• List of Publications - Satish Narayana Srirama - http://math.ut.ee/~srirama/publications.html

• [Flores et al, IEEE Communications Mag 2015] H. Flores, P. Hui, S. Tarkoma, Y. Li, S. N. Srirama, R. Buyya: Mobile Code Offloading: From Concept to Practice and Beyond, IEEE Communications Magazine, ISSN: 0163-6804, 53(3):80-88, 2015. IEEE. DOI:10.1109/MCOM.2015.7060486

• [Chang et al, SCC 2015]C. Chang, S. N. Srirama, J. Mass: A Middleware for Discovering Proximity-based Service-Oriented Industrial Internet of Things, 12th IEEE International Conference on Services Computing (SCC 2015), June 27 - July 2, 2015, pp. 130-137. IEEE.

• [Liyanage et al, MS 2015] M. Liyanage, C. Chang, S. N. Srirama: Lightweight Mobile Web Service Provisioning for Sensor Mediation, 4th International Conference on Mobile Services (MS 2015), June 27 - July 2, 2015, pp. 57-64. IEEE

• [Chang et al, ICWS 2015] C. Chang, S. Loke, H. Dong, F. Salim, S. N. Srirama, M. Liyanage, S. Ling: An Energy-Efficient Inter-organizational Wireless Sensor Data Collection Framework, The IEEE 22nd International Conference on Web Services (ICWS 2015), June 27 - July 2, 2015, pp. 639-646. IEEE.

• [Flores and Srirama, JSS 2014] H. Flores, S. N. Srirama: Mobile Cloud Middleware, Journal of Systems and Software, ISSN: 0164-1212, 92(1):82-94, 2014. Elsevier. DOI: 10.1016/j.jss.2013.09.012.

• [Chang et al, PMC 2014] C. Chang, S. N. Srirama, S. Ling: Towards an Adaptive Mediation Framework for Mobile Social Network in Proximity, Pervasive and Mobile Computing Journal, MUCS Fast track, ISSN: 1574-1192, 12:179-196, 2014. Elsevier. DOI: 10.1016/j.pmcj.2013.02.004.

• [Warren et al, IEEE PC 2014] I. Warren, A. Meads, S. N. Srirama, T. Weerasinghe, C. Paniagua: Push Notification Mechanisms for Pervasive Smartphone Applications, IEEE Pervasive Computing, ISSN: 1536-1268, 13(2):61-71, 2014. IEEE. DOI:10.1109/MPRV.2014.34.

• [Chang et al, MUM 2014] C. Chang, S. N. Srirama, S. Ling: SPiCa: A Social Private Cloud Computing Application Framework, The 13th International Conference on Mobile and Ubiquitous Multimedia (MUM 2014), November 25-28, 2014, pp. 30-39. ACM.

• [Jakovits and Srirama, HPCS 2014] P. Jakovits, S. N. Srirama: Evaluating MapReduce Frameworks for Iterative Scientific Computing Applications, The 2014 (12th) International Conference on High Performance Computing & Simulation (HPCS 2014), July 21-25, 2014, pp. 226-233. IEEE.

• [Kromonov et al, HPCS 2014] I. Kromonov, P. Jakovits, S. N. Srirama: NEWT - A resilient BSP framework for iterative algorithms on Hadoop YARN, The 2014 (12th) International Conference on High Performance Computing & Simulation (HPCS 2014), July 21-25, 2014, pp. 251-259. IEEE.

• [Srirama and Viil, HPCC 2014] S. N. Srirama, J. Viil: Migrating Scientific Workflows to the Cloud: Through Graph-partitioning, Scheduling and Peer-to-Peer Data Sharing, 16th Int. Conf. on High Performance and Communications (HPCC 2014) workshops, August 20-22, 2014, pp. 1137-1144. IEEE.

• [Jakovits and Srirama, Nordicloud 2013] P. Jakovits, S. N. Srirama: Clustering on the Cloud: Reducing CLARA to MapReduce, 2nd Nordic Symposium on Cloud Computing & Internet Technologies (NordiCloud 2013), September 02-03, 2013, pp. 64-71. ACM.

• [Jakovits et al, HPCS 2013] P. Jakovits, S. N. Srirama, I. Kromonov: Viability of the Bulk Synchronous Parallel Model for Science on Cloud, The 2013 (11th) International Conference on High Performance Computing & Simulation (HPCS 2013), July 01-05, 2013, pp. 41-48. IEEE.

• [Srirama and Paniagua, MS 2013] S. N. Srirama, C. Paniagua: Mobile Web Service Provisioning and Discovery in Android Days, The 2013 IEEE International Conference on Mobile Services (MS 2013), June 27 - July 02, 2013, pp. 15-22. IEEE.

• [Flores and Srirama, MCS 2013] H. Flores, S. N. Srirama: Adaptive Code Offloading for Mobile Cloud Applications: Exploiting Fuzzy Sets and Evidence-based Learning, The Fourth ACM Workshop on Mobile Cloud Computing and Services (MCS 2013) @ MobiSys 2013, June 25-28, 2013, pp. 9-16. ACM.

• [Oliner et al, 2013] Oliner, Adam J., Anand P. Iyer, Ion Stoica, Eemil Lagerspetz, and Sasu Tarkoma. "Carat: Collaborative energy diagnosis for mobile devices." In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, p. 10. ACM, 2013.

• [Srirama et al, SOCA 2012] S. N. Srirama, C. Paniagua, H. Flores: Social Group Formation with Mobile Cloud Services, Service Oriented Computing and Applications Journal, ISSN: 1863-2386, 6(4):351-362, 2012. Springer. DOI: 10.1007/s11761-012-0111-5.

• [Srirama et al, FGCS 2012] S. N. Srirama, P. Jakovits, E. Vainikko: Adapting Scientific Computing Problems to Clouds using MapReduce, Future Generation Computer Systems Journal, 28(1):184-192, 2012. Elsevier press. DOI 10.1016/j.future.2011.05.025.


References - continued• [Chang et al, ICSOC 2012] C. Chang, S. N. Srirama, S. Ling: An Adaptive Mediation Framework for Mobile P2P Social

Content Sharing, 10th International Conference on Service Oriented Computing (ICSOC 2012), November 12-16, 2012, pp. 374-388. Springer LNCS.

• [Paniagua et al, MobiWIS 2012] C. Paniagua, H. Flores, S. N. Srirama: Mobile Sensor Data Classification for Human Activity Recognition using MapReduce on Cloud, The 9th Int. Conf. on Mobile Web Information Systems (MobiWIS2012), August 27-29, 2012, pp. 585-592. Elsevier.

• [Srirama et al, SPJ 2011] S. N. Srirama, O. Batrashev, P. Jakovits, E. Vainikko: Scalability of Parallel Scientific Applications on the Cloud, Scientific Programming Journal, Special Issue on Science-driven Cloud Computing, 19(2-3):91-105, 2011. IOS Press. DOI 10.3233/SPR-2011-0320.

• [Flores et al, MoMM 2011] H. Flores, S. N. Srirama, C. Paniagua: A Generic Middleware Framework for Handling Process Intensive Hybrid Cloud Services from Mobiles, The 9th International Conference on Advances in Mobile Computing & Multimedia (MoMM-2011), December 5-7, 2011, pp. 87-95. ACM.

• [Paniagua et al, iiWAS 2011] C. Paniagua, S. N. Srirama, H. Flores: Bakabs: Managing Load of Cloud-based Web Applications from Mobiles, The 13th International Conference on Information Integration and Web-based Applications & Services (iiWAS-2011), December 5-7, 2011, pp. 489-495. ACM.

• [Srirama et al, MobiWIS 2011] S. N. Srirama, C. Paniagua, H. Flores: CroudSTag: Social Group Formation with Facial Recognition and Mobile Cloud Services, The 8th International Conference on Mobile Web Information Systems (MobiWIS 2011), September 19-21, 2011, v. 5 of Procedia Computer Science, pp. 633-640. Elsevier. doi: 10.1016/j.procs.2011.07.082.

• [Srirama et al, NGMAST 2011] S. N. Srirama, H. Flores, C. Paniagua: Zompopo: Mobile Calendar Prediction based on Human Activities Recognition using the Accelerometer and Cloud Services, 5th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST 2011), September 14-16, 2011, pp. 63-69. IEEE.

• [Batrashev et al, HPCS 2011] O. Batrashev, S. N. Srirama, E. Vainikko: Benchmarking DOUG on the Cloud, The 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), July 4-8, 2011, pp. 677-685. IEEE.

• [Srirama, PhD 2008] S. N. Srirama: Mobile Hosts in Enterprise Service Integration, PhD thesis, RWTH Aachen University, September, 2008.

• [Srirama et al, ICIW 2006] S. N. Srirama, M. Jarke, W. Prinz: Mobile Web Service Provisioning, Proceedings of the Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services (AICT-ICIW 2006), February 23-25, 2006, pp. 120-125. IEEE Computer Society Press.

• [Valiant, 1990] L. G. Valiant: A bridging model for parallel computation, Commun. ACM, vol. 33, no. 8, pp. 103–111, Aug. 1990.


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