Mobile Grid/Cloud Computing

1. Mobile Grid/Cloud Computing

2. Dealing with mobility • Proxy must be close to device for effectiveness • Idea – use a network of proxies and identify how to use the proxy network for multiple moving devices… • Where to obtain proxies • Fixed backbone infrastructure (Akamai) • Could be effective, but proprietary • Grid infrastructure (MAPGRID) • Open access, but intermittently available • Cloud infrastructure (Charisma) • Open access, limited ability to tailor use of proprietary backbone resources

3. Observations (I) – Proxy-based Techniques • Network proxies support for mobile applications, e.g.: • Proxy caching: • [Hadjiefthymiades WWW01], [Xu TKDE04],[Wang TM 07], etc. • Proxy-based content transcoding: • [Shenoy, MMCN03] [Change EUROMICRO Journal 07], etc. • Offloading tasks: • [Kejariwal, Trans VLSI 06], [Mohapatra, ACM 2003],[ Li, NOSSDAV 2003], etc. • Grid/Cloud infrastructure support for implementing proxy-based solutions, e.g: using grid machines as proxies for mobile applications [PhanMobiCom 02] • Online Server or third party Storage • e.g. iPhone 3G MobileMe keeps all user information in the “cloud” • “

4. Observations (II)- Context Awareness and Predictability • Importance of Context information, e.g. • Enabling mobile applications: • Location-based services • Achieving service flexibility and adaptability: • Layered video transcoding [Shanableh Transaction of Multimedia 05], etc. • Improving system performance: • Mobility-aware data allocation (increasing cache hit ratio) [Peng ICPP00], etc. • Predictability and Patterns of Context • Exploited for improving handoff performance, reducing task failure rate and dynamic resource allocation, etc • Mobility prediction [Song, IEEE Transaction on Mobile Computing 06] • Mobility models [ MCNett SIGMOBILE review 05] [Hong MSWiM99] • Characterizing resource availability in desktop grids [Kondo FGCS 07]

5. VS 3 VS 2 VS 1 MAPGrid: A New ParadigmGrid Enabled Mobile Applications Broker

6. Research Challenges • Resource Discovery • Application aspect • Ensure user QoS satisfaction • System View • Define an “optimal” grid resource allocation • User mobility • resource heterogeneity • Grid intermittent availability • Adapt to changing context • User mobility, device energy, and proxy availability • Data Placement • Application aspect • Guarantee data availability • Improve information retrieval efficiency with user mobility • System View • Address both short term on-demand caching and long term data placement • Make caching or data replication at finer granularity • Balance availability efficiency tradeoff

7. MapGrid • Uses (idle) grid computing resources as the intermediate node cache proxy for mobile applications. • Resources on grid (storage and computation) are intermittently available. • MapGrid uses the interval tree data structure for storing the information about available resources on grid. • Mobility pattern has been used to optimal data replication policy in MapGrid.

8. 1. Resource Discovery 2. Schedule Planning Contains information about available resources on Grid and Clients Replace the data to grid resources Tertiary Storage Directory Service Data Admission Control Info Placement Management Volunteer Servers(Grids) Request Scheduler Req@ Broker Req Req* Mobile Client Req Resource Reservation Req# Mobile Client Monitoring Grid Monitoring Moving_Profile Reports changes in grid resources Mobility Pattern detection MapGrid Middleware Service Architecture

9. MapGrid • Request: R(VID, itinerary)where VID is the video and itinerary is mobility information. • Grid Loading Factor: • Grid Factor: If grid j is available at time t equal 1 else 0. Distance of grid J from request R.

10. Grid Resource Discovery for Mobile Services R: < IDR , T , QL , QH, , ER , itinerary> Partition Service Period • apply mobility or location information • choose the number of chunks (partition) • decide the size of each chunk Proxy (VS) Selection All chunks have VS assignments? • choose available VSs • choose lighter loaded VSs • choose “closer” VSs • Satisfy different QoS reqirements on service response time, service continuity… N Y • Rescheduling when device has no enough residual energy for the current QoS level • Rescheduling when grid node fails Rescheduling if dynamic changes happen

11. Grid Resource Discovery for Mobile Services Partition Service Period Problem: • Determine number of partitions and size of each partition Machine learning approach using mobility info Volunteer Server Allocation All chunks have VS assignments? N Y Rescheduling if dynamic changes happen

12. Grid Resource Discovery for Mobile Services Partition Service Period Volunteer Server Allocation All chunks have VS assignments? N Optimizing grid resource selection • Factors: Load, location, availability, capacity • Achieve system-wide load balancing • Maximize the number of accepted services Graph theoretic approach Y Rescheduling if dynamic changes happen

13. Grid Service Discovery (Multimedia Application) Collections of services support by grid significantly increases request acceptance and Completion ratios P2: Deterministic data placement T2: Random VS availability

14. MAPGrid Prototype Failure detection for both client and VSs using Globus life time management control. Volunteer Servers GridFTP Globus Toolkit Information Services Broker Life time control JADE Http Data The Volunteer Server can join and disjoin the grid dynamically . Client can specify QoS requirements via friendly GUI Client JMF (Java Media Framework)

15. Mobile Cloud Computing

16. What is Cloud Computing • Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). • It can be rapidly provisioned and released with minimal management effort. • It provides high level abstraction of computation and storage model. • On-Demand Self Service, • HeterogeneousAccess, • Resource Pooling.

17. Essential Characteristics • On-Demand Self Service: • A consumer can unilaterally provision computing capabilities, automatically without requiring human interaction with each service’s provider. • HeterogeneousAccess: • Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms.

18. Essential Characteristics (cont.) • Resource Pooling: • The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model. • Different physical and virtual resources dynamically assigned and reassigned according to consumer demand. • Measured Service: • Cloud systems automaticallycontrol and optimize resources used by leveraging a metering capability at some level of abstraction appropriate to the type of service. • It will provide an analyzable and predictable computing platform.

19. Service Models • Cloud Software as a Service (SaaS): • The capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure. • The applications are accessible from various client devices such as a web browser (e.g., web-based email). • The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage,… • Examples: Caspio, Google Apps, Salesforce, Nivio, Learn.com.

20. Service Models (cont.) • Cloud Platform as a Service (PaaS): • The capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported by the provider. • The consumer does not manage or control the underlying cloud infrastructure. • Consumer has control over the deployed applications and possibly application hosting environment configurations. • Examples: Windows Azure, Google App.

21. Service Models (cont.) • Cloud Infrastructure as a Service (IaaS): • The capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources. • The consumer is able to deploy and run arbitrary software, which can include operating systems and applications. • The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). • Examples: Amazon EC2, GoGrid, iland, Rackspace Cloud Servers, ReliaCloud.

22. Service Models (cont.) Service Model at a glance: Picture From http://en.wikipedia.org/wiki/File:Cloud_Computing_Stack.svg

23. Deployment Models • Private Cloud: • The cloud is operated solely for an organization. It may be managed by the organization or a third party and may exist on premise or off premise. • Community Cloud: • The cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns. • It may be managed by the organizations or a third party and may exist on premise or off premise. • Public Cloud: • The cloud infrastructure is made available to the general public or a large industry group and it is owned by an organization selling cloud services. • Hybrid cloud: • The cloud infrastructure is a composition of two or more clouds (private, community, or public).

24. Deployment Models (cont.) Service Model at a glance: Picture From http://en.wikipedia.org/wiki/File:Cloud_computing_types.svg

25. Towards Pervasive Computing Cloud Computing Promises Evolution of Computing Environment: [Satyanarayanan_2001] : Toward Pervasive Computing environment

26. Cloudlet • It has been shown that a one hop connection from mobile device to internet is not efficient. • Humans are sensitive to the current delay in clouds • latency is unlikely to improve significantly • security and firewall it is unlikely that latency improves (although increase in bandwidth). • Cloudlet • Between mobile devices and cloud pools (Infrastructure). • A cloudlet (micro edition of cloud)only contains soft states such as cache copies of data or code.

27. Cloudlet (cont.) The prototype based on this systems is implemented in CMU and called Kimberley [Satyanarayanan_2009] . Cloudlets are as the infrastructure for mobile cloud computing. From: [Satyanarayanan_2009]

28. Dynamic Task Reconfiguration • How can we support dynamic task/service reconfiguration to achieve energy gains (i.e. migrate computationally expensive tasks from the device to the proxy and use the results locally) • Approach: • Identify “energy intensive” components that can be dynamically migrated to a proxy • What to reconfigure? • computation/communication characteristics of tasks (use Profiling) • Current residual power of the device • When to reconfigure? • Identify set of policies that dictate how often or under what conditions reconfigurations should be initiated

29. Dynamo: Dynamic Task Reconfiguration Problem How to maintain an optimized component distribution under dynamic device power conditions? Solution • Cast the distribution problem as a “Source Parametric Flow Network”. • Use Current residual energy at deviceto make the flow graph “source parametric”

30. Source Parametric Flow Graph Runtime on Device Rd Bi = Energy cost of executing task Mi at the device. X1 Bd infinite X2 M1 A1 B1 Y1 M2 A2 Xn B2 D P proxy device Y2 An Bn Mn Ai = Energy cost of executing task Mi at the proxy. infinite Ap Yn Rp Runtime on Proxy • If Mi is executing on the Device, then Xi = communication costs in energy terms • If Mi is executing on the Proxy, then Yi = communication costs in energy terms

31. High Level Algorithm • The minimum cut of the graph determines which components can be moved to the proxy. • Can be solved using a modified FIFO Pre-Flow push algorithm. • Complexity = O(n3) • FOR (each reconfiguration interval) DO • BEGIN • update list of components/residual energy on device • generate network flow graph • determine component partitioning (min cut) • IF (new partition) • reconfigure components between device and proxy • END

32. AlfredO Platform (ETH Zurich) • SaaS distribution model for physical services (MW 2008, 2009) • Mobile Phones as universal interfaces to cloud applications • Supports partition and distribution of modularized applications • in a client-server setup • OSGI-based (Open Services Gateway Initiative) • Focus on presentation and logic modules Home 3D Planner Virtual ticket machine Turn the phone into an interface for the ticket machine

33. AlfredO architecture and applications (MW 2009) Premise: Modularized application and Model of resource consumption and dependencies among application modules Goal: Partition the application between phone and server based on different criteria (bandwidth, memory) • Component Based Architecture • Supports optimaltask migration between server and mobile client [Middleware 2009]. • Generate an application graph and determine a cut (tasks done on server side and remaining will be done on mobile side) to meet the required optimization issues.

34. AlfredO Original Plan S6 S6 S6 S1 S1 S1 S2 S2 S2 S3 S3 S3 S4 S4 S4 Mobile Cut S8 S8 S8 S5 S5 S5 S7 S7 S7 Mobile Cut

35. AlfredO (MW 2009)

36. CHARISMA: Tiered Clouds • Just like ISPs cloud computing could be considered in different Tiers. • The backbone Cloud which usually large organization such as Google, Yahoo, Microsoft support them. • The second tier is edge Cloud which is near end user. Tier 2-Cloud (Edge Cloud) Tier 1-Cloud (Backbone Clouds,. Amazon EC2, Azure,… )

37. Mobile Client Cloudlet and Cloud Pool Middleware(Broker) Cloud Service Registry and Discovery Qos Monitoring Qos Monitoring , Service Discovery Cloudlet Pools Cloud Pools Mobile Client Admission Control Qos Analyzer Scheduler Mobile Profile Monitoring Mobile Profile Analyzer

39. Barcode Reader: Sample Application • Simple barcode reader service has been implemented. • In this scenario the user takes a picture of the barcode for getting some information about the object, for example the cheapest price location. • The picture is sent to cloudlet for processing. • The cloudlet extracts the information and queries Amazon or other clouds for price and returns back to the user

40. Cloudlet Cloud

41. Yahoo Cloud (Flicker) as a Cache. Cloud(Yahoo) Cloudlet Cloud(Amazon)

42. http://www.youtube.com/watch?v=fQywFeN1wdM

43. Cloud computing • Remote access to distributed and shared cluster resources • Potentially owned by someone else (e.g. Amazon, Google, …) • Users rent a small fraction of vast resource pools • Advertised service-level-agreements (SLAs) • Resources are opaque and isolated • Offer high availability, fault tolerance, and extreme scale • Relies on OS, network, and storage virtualization/isolation Virtualization SLAs Web Services

44. Future: Interoperable Networking Heterogeneous access technologies available 802.11b End devices equipped with multiple radio interfaces 802.11a Wi-Fi Bluetooth Ad Hoc Cellular Applications are run over the most efficient access networks GPRS ...... Reliable Secure QoS over multiple access networks UMTS

45. Always Best Connected: Solution Components • personal profiles for ABC • access selection & content adaptation profile handling content adaptation • applications adapting to access & device • session continuity, session transfer • support for real-time services mobility management • authentication, authorization, accounting • accesses, services...  single logon AAA support • what access to choose; what is “best”? • user/network-based solution • one or multiple accesses in parallel access selection access discovery • what accesses are currently available? • what are their bandwidth and delay?

46. Access Selection: Benefits • Continual connectivity • Uninterrupted Internet access even when some of the access networks are unavailable • High throughput/QoS • Connect via the access network with highest data rate and/or shortest delay • Broader bandwidth • Aggregate the bandwidth offered by multiple access networks • Energy Conservation • Different power consumption properties of access networks provide chances for better energy efficiency. • Enhanced Security • Transmitting sensitive data on multiple access networks potentially provides higher security.

47. Access Selection: Research Challenges • Non-predetermined traffic flows start/terminate at discrete time points. • Access networks’ bandwidth and delay might fluctuate. How to select appropriate access networks for application traffic flows? Dynamicity Multiple Constraints • QoS requirements • Energy consumption • User/application specified access preferences

48. Groupware apps • Multiparty collaborations • Seguti • Reliability + Security

49. Summary • New Generation of Pervasive and Mobile Computing Applications with diverse content • Need significant enhancements in system and software support • Distributed mobile middleware can significantly improve performance of next generation mobile applications. • Open interfaces will yield much more optimized solutions for distributed mobile devices. The use of cross-layer design architectures is inevitable for future mobile systems, if we are to realize cumulative benefits of independent research at each system layer.