mobile cloud computing - arvutiteaduse instituut

Mobile Cloud Computing

Satish [email protected]

MTAT.03.262 - Mobile Application Development – Lecture 8

Outline

• Cloud Computing

• Mobile Cloud

– Binding Models

• HomeAssignment3

10/30/2015 Satish Srirama 2/43

WHAT IS CLOUD COMPUTING?

No consistent answer!

Everyone thinks it is something else…

“It’s nothing new”“...we’ve redefined Cloud Computing to include everything that we already do... I don’t understand what we would do differently ... other than change the wording of some of our ads.”

Larry Ellison, CEO, Oracle (Wall Street Journal, Sept. 26, 2008)

“It’s a trap”“It’s worse than stupidity: it’s marketing hype. Somebody is saying this is inevitable—and whenever you hear that, it’s very likely to be a set of businesses campaigning to make it true.”

Richard Stallman, Founder, Free Software Foundation (The Guardian, Sept. 29, 2008)

Slide taken from Professor Anthony D. Joseph’s lecture at RWTH Aachen10/30/2015 Satish Srirama 3

What is Cloud Computing?

• Computing as a utility

– Utility services e.g. water, electricity, gas etc

– Consumers pay based on their usage


Timeline


Clouds - Why Now (not then)?

• Experience with very large datacenters

– Unprecedented economies of scale

– Transfer of risk

• Technology factors

– Pervasive broadband Internet

– Maturity in Virtualization Technology

• Business factors

– Minimal capital expenditure

– Pay-as-you-go billing model


• Virtualization techniques are the basis of the cloud computing

• Virtualization technologies partition hardware and thus provide flexible and scalable computing platforms

• Virtual machine techniques

– VMware and Xen

– OpenNebula

– Amazon EC2

Virtualization

Hardware

OS

App App App

Hypervisor

OS OS

Virtualized Stack


Cloud Computing - Characteristics

• Illusion of infinite resources

• No up-front cost

• Fine-grained billing (e.g. hourly)

• Gartner: “Cloud computing is a style of

computing where massively scalable IT-related

capabilities are provided ‘as a service’ across

the Internet to multiple external customers”


Cloud Computing - Services• Software as a Service – SaaS

– A way to access applications hosted on the web through your web browser

• Platform as a Service – PaaS

– Provides a computing platform and a solution stack (e.g. LAMP) as a service

• Infrastructure as a Service –IaaS

– Use of commodity computers, distributed across Internet, to perform parallel processing, distributed storage, indexing and mining of data

– Virtualization

SaaS

Facebook, Flikr, Myspace.com,

Google maps API, Gmail

PaaS

Google App Engine, Force.com, Hadoop, Azure,

Heroku, etc

IaaS

Amazon EC2, Rackspace, GoGrid, SciCloud, etc.

Level of

Abstraction


Cloud Computing - Themes

• Massively scalable

• On-demand & dynamic

• Only use what you need - Elastic – No upfront commitments, use on short term basis

• Accessible via Internet, location independent

• Transparent – Complexity concealed from users, virtualized,

abstracted

• Service oriented – Easy to use SLAs

SLA – Service Level Agreement


Cloud Models

• Internal (private) cloud– Cloud with in an organization

• Community cloud– Cloud infrastructure jointly

owned by several organizations

• Public cloud– Cloud infrastructure owned by

an organization, provided to general public as service

• Hybrid cloud– Composition of two or more

cloud models


Cloud Application Demand

• Many cloud applications have cyclical demand curves– Daily, weekly, monthly, …

• Workload spikes are more frequent and significant– When some event happens like a pop star has expired:

• More # tweets, Wikipedia traffic increases

• 22% of tweets, 20% of Wikipedia traffic when Michael Jackson expired in 2009 – Google thought they are under attack

Demand

Time

Res

ourc

es


Unused resources

Economics of Cloud Users

• Pay by use instead of provisioning for peak

Static data center Data center in the cloud

Demand

Capacity

Time

Res

ourc

es

Demand

Capacity

Time

Res

ourc

es


Unused resources

Economics of Cloud Users - continued

• Risk of over-provisioning: underutilization

– Huge sunk cost in infrastructure

Static data center

Demand

Capacity

Time

Res

ourc

es


Economics of Cloud Users - continued

• Heavy penalty for under-provisioning

Lost revenue

Lost users

Res

ourc

esDemand

Capacity

Time (days)

1 2 3

Res

ourc

es

Demand

Capacity

Time (days)

1 2 3

Res

ourc

es

Demand

Capacity

Time (days)

1 2 3


Optimal resource provisioning for

auto-scaling enterprise applications• Auto-Scaling

– Scaling policy -> When to Scale

– Resource provisioning policy -> How to scale

• Cloud providers offer various instance types with different processing power and price– Can it be exploited in deciding the resource provisioning policy?

– Makes the policy to be aware of current deployment configuration

• Another challenge: Cloud providers charge the resource usage for fixed time periods– E.g. Hourly prices of Amazon cloud

• Developed an LP based optimization model which considers both the issues [Srirama and Ostovar, CloudCom 2014]


Scaling enterprise application with the

optimization model

10/30/2015 Satish Srirama

Incoming load and scaling

curves of Optimization model

Instance type usage curves of

Optimization model

Scaling with Amazon

AutoScale

[Srirama and Ostovar, CloudCom 2014]

Basics of Cloud Computing - MTAT.08.027 (Spring 2016)17/43

Short Term Implications of Clouds

• Startups and prototyping

– Minimize infrastructure risk

– Lower cost of entry

• Batch jobs

• One-off tasks

– Washington post, NY Times

• Cost associatively for scientific applications

• Research at scale


Long Term Implications of clouds

• Application software:

– Cloud & client parts, disconnection tolerance

• Infrastructure software:

– Resource accounting, VM awareness

• Hardware systems:

– Containers, energy proportionality


Economics of Cloud Providers

• Cloud Computing providers bring a shift from high reliability/availability servers to commodity servers– At least one failure per day in large datacenter

• Why?– Significant economic incentives

• much lower per-server cost

• Caveat: User software has to adapt to failures– Very hard problem!

• Solution: Replicate data and computation– MapReduce & Distributed File System


Basics of Cloud Computing - MTAT.08.027 (Spring 2016)[Srirama et al, FGCS 2012]20/43

Cloud Computing Progress

Armando Fox, 2010


Mobile Cloud Computing

• One can do interesting things on mobiles directly

– Today’s mobiles are far more capable

– We can even provide services from smart phones[Srirama et al, ICIW 2006; Srirama and Paniagua, MS 2013; Chang et al, ICSOC 2012; Liyanage et al, MS 2015]

• However, some applications need to offload certain activities to servers

– Processing sensor data

• Resource-intensive processing on the cloud

– To enrich the functionality of 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 - Binding models

Delegation Code Offloading

MCM

[Flores and Srirama, JSS 2014] [Flores et al, IEEE Communications Mag 2015]


Demo & Exercise

• Start an Amazon EC2 instance from mobile

• Exercise: Submit a job there

https://bitbucket.org/mobilecloudlab/mad_fall2

015_ec2_adaptor/


Mobile Cloud mash-up applications

• Mobiles may have to take advantage of multiple clouds

• Follows traditional SOA model to invoke services

• Typical scenarios – Process intensive services

• Face recognition, sensor mining etc.

– Data Synchronization (SyncML, Funambol, Google Sync)• Calendar, contacts etc.

• Critical challenges – Cloud interoperability is a huge trouble

– Mobiles need to possess multiple APIs

– Unavailability of standards and mobile platform specific API


Mobile Cloud Middleware

[Flores et al, MoMM 2011; Flores and Srirama, JSS 2014]

[Srirama and Paniagua, MS 2013]

10/30/2015

[Warren et al, IEEE PC 2014]

Satish Srirama

Google Cloud

Messaging

Microsoft Push

Notification

Service

27/43

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

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


CroudSTag – Scenario

• CroudSTag takes the pictures/videos from the cloud and tries to recognize people

– Pictures/Videos are actually taken by the phone

– Processes the videos

– Recognizes people using facial recognition technologies

• Reports the user a list of people recognized in the pictures

• The user decides whether to add them or not to the social group

• The people selected by the user receive a message in facebook inviting them to join the social group


CroudSTag [Srirama et al, PCS 2011;

SOCA 2012]

• Cloud services used

– Media storage on

Amazon S3

– Processing videos on

Elastic MapReduce

– face.com to recognize

people on facebook

– Starting social group

on facebook


Other applications

• 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, MobiWIS2012]

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


Checking for updates

• Polling

• Polling consumes lot of battery of mobile

• Heavy load on the resources of the server

• Strains the network


Any new updates ?

No

Any new updates ?

No

Any new updates ?

YES

32/43

Google Cloud Messaging

• Free service that allows you to send data from

your servers to your users and vice versa

• Handles queuing of messages and delivery to

the target application

– Application does not need to be running in the background for receiving data messages

– The system will wake up the Android application

via Intent broadcast when the message arrives


Working with GCM


Mobile App

GCM

1.Request for Registration

2.Reply with Registration Id

5. Send Message

3.Send Registration Id 4. Send message {RegId, msg}

https://developers.google.com/cloud-messaging/

[Warren et al, IEEE PC 2014]

34/43

Exercise

• Working with GCM

https://bitbucket.org/snsrirama/gcmtrial

http://52.91.75.87:8080/gcm/


Code Offloading

• Also known as Cyber-foraging [M. Satyanarayanan, 2001]

• Not a completely new topic

– Can be realized similar to RMI, RPC etc.

• Devices offload some of their heavy work to

stronger surrogate machines in the vicinity

(Cloudlets)


Major Components

• Mobile

– Code profiler

– System profilers

– Decision engine

• Cloud based surrogate platform


Some of the well known frameworks

• MAUI

– Manual annotations [Cuervo et al., 2010]

• CloneCloud

– Code profilers & Automated process [Chun et al.,

2011]

• ThinkAir

– Manual annotations and scalability [Kosta et al, 2012]

• EMCO [Flores and Srirama, MCS 2013] & etc.


Code offloading – Major challenges

• Work in controlled environments like nearby

servers

– However, none can be adapted for real life

applications

• Major research challenges

– What, when, where and how to offload?


[Flores et al, IEEE Communications Mag 2015]

39/43

Practical adaptability of offloading

Applications that can benefit became limited with increase in device capacities10/30/2015 Satish Srirama 40/43

Exercise - optional

• Java reflection

– https://gist.github.com/huberflores/9829019

• Transforming a java method to a

computational offloading format

– https://gist.github.com/huberflores/6e4e0894d95

e16f1c25e


Home Assignment - 3

• Encrypting and decrypting an image on the Cloud instance (15 points)

• Initiate the procedure on receiving a GCM message (10 points)– Design a protocol for the received messages

• Deadline – 12th November 2015

• Last deadline for all submissions – 19th November 2015


References• S. N. Srirama, A. Ostovar: Optimal Resource Provisioning for Scaling Enterprise Applications on the Cloud, The 6th IEEE International Conference on

Cloud Computing Technology and Science (CloudCom-2014), December 15-18, 2014, pp. 262-271. IEEE.

• 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.

• 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 CS Press.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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.

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

• Cuervo, Eduardo, et al. "MAUI: making smartphones last longer with code offload." Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 2010.

• Kosta, Sokol, et al. "Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading." INFOCOM, 2012 Proceedings IEEE. IEEE, 2012.

• Satyanarayanan, Mahadev, et al. "The case for vm-based cloudlets in mobile computing." Pervasive Computing, IEEE 8.4 (2009): 14-23.

• 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) @ The 11th International Conference on Mobile Systems, Applications and Services (MobiSys 2013), June 25-28, 2013, pp. 9-16. ACM.


mobile cloud computing - arvutiteaduse instituut

Documents