Mobile Cloud Computing

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1 MTAT Mobile Application Development Mobile Cloud Computing Satish Srirama, Huber Flores Tartu, Estonia, 2013

2 Outline Cloud Computing Mobile Cloud Access schemas Research challenges

It s nothing new It s a trap...we ve redefined Cloud It s worse than stupidity: it s Computing to include everything marketing hype. Somebody is that we already do.

3 It s nothing new It s a trap...we ve redefined Cloud It s worse than stupidity: it s Computing to include everything marketing hype. Somebody is that we already do... I don t saying this is inevitable and understand what we would do whenever you hear that, it s very differently... other than change likely to be a set of businesses the wording of some of our ads. campaigning to make it true. WHAT IS CLOUD COMPUTING? Larry Ellison, CEO, Oracle (Wall Richard Stallman, Founder, Free Street Journal, Sept. 26, 2008) Software Foundation (The Guardian, Sept. 29, 2008) No consistent answer! Everyone thinks it is something else Slide taken from Professor Anthony D. Joseph s lecture at RWTH Aachen

4 What is Cloud Computing? Computing as a utility Utility services e.g. water, electricity, gas etc Consumers pay based on their usage 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

5 Timeline

6 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

7 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 A pay-as-you-go model for IT resources accessed over the Internet 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, Amazon S3, etc IaaS Amazon EC2, SciCloud, Joyent Accelerators, Nirvanix Storage Delivery Network, etc. Level of Abstraction

8 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

9 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

10 Resources Cloud Application Demand Many cloud applications have cyclical demand curves Daily, weekly, monthly, Demand 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 Time

11 Resources Resources Economics of Cloud Users Pay by use instead of provisioning for peak Capacity Demand Capacity Demand Time Static data center Time Data center in the cloud Unused resources

12 Resources Economics of Cloud Users - continued Risk of over-provisioning: underutilization Huge sunk cost in infrastructure Capacity Unused resources Demand Time Static data center

13 Resources Resources Resources Economics of Cloud Users - continued Heavy penalty for under-provisioning Capacity Demand Time (days) Capacity Demand Time (days) Lost revenue Capacity Time (days) Lost users Demand

14 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

15 Long Term Implications of clouds Application software: Cloud & client parts, disconnection tolerance Infrastructure software: Resource accounting, VM awareness Hardware systems: Containers, energy proportionality

16 Cloud Computing Progress Armando Fox, 2010

17 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 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

18 Mobile Cloud Applications Mobile has significant advantage by going cloud-aware Increased data storage capacity Availability of unlimited processing power Extended battery life [Rudenko et al., SIGMOBILE 1998] Mobile Cloud based mash-up applications Mobiles need to possess multiple APIs Cloud interoperability is a huge trouble

19 Demo Mobile application using a cloud service Working with JetS3t Amazon.git

20 Mobile Cloud Access Schemas

21 Mobile Cloud Access Schemas Code Offloading Bytecode level Code partitioning Remote execution in Dalvik x86 Task Delegation Service integration Cloud infrastructure programming Cloud intercommunication Asynchronous delegation

22 Mobile Cloud Middleware [Srirama et al, ICIW 2006] [Flores et al, MoMM 2011]

23 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

24 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

25 2. CroudSTag [Srirama et al, PCS 2011] Taking picture/video using the camera Cloud services used Media storage on Amazon S3 Processing videos on Elastic MapReduce Facebook Login 4. Main Menu Storage Services Facial Recognition Process 7. Selecting Cloud Send Asynchronous Notification and Results face.com to recognize people on facebook Starting social group on facebook 5. Facebook Authentication 6. Selecting Cloud 8. Selecting people Send next invitation Start Process Back to Menu Send invitation to the social group 9.

26 Process-intensive tasks on cloud Sensor data analysis Media processing

27 Workshop Working with MCM Amazon.git

28 Home Assignment - 3 Indoor positioning Choose one floor from UT building and draw it Create a dynamic layout (Zoom must be available) Integrate to the code provided Three weeks time to deliver this home assignment after given this lecture

29 App. Provided

30 App. Expected

31 Next week Fortumo Guest lecture by Lars Eckart from Fortumo

32 THANK YOU

Mobile Cloud Computing

MTAT.03.262 -Mobile Application Development Lecture 8 Mobile Cloud Computing Satish Srirama, Huber Flores satish.srirama@ut.ee Outline Cloud Computing Mobile Cloud Access schemes HomeAssignment3 10/20/2014