1. Preetam Ghosh School of Computing Mobile Grid Computing: From Theory to Practice Preetam Ghosh School of Computing The University of Southern Mississippi E-mail: preetam.ghosh@usm.edu http://www.cs.usm.edu/~pghosh

2. Preetam Ghosh School of Computing Outline  Mobile Grid  Mobile Grid Challenges and Applications  Mobile Grid Projects  Our Focus –Investigation of Pricing Model –Cost-effective Job Allocation Schemes  Conclusion

3. Preetam Ghosh School of Computing Computational Grid Resource Broker Resource Broker & Trade Server Resources and Topology (System) Tasks and Topology (Workload) DCADRB DCA LS GIS : Grid Information Server DRB: Domain Resource Broker DCA: Domain Control Agent LS : Local Scheduler The Grid Price Negotiation & Mapping Strategy GIS User User submits job to Resource Broker User submits job to Resource Broker Trade Server negotiates with the DRB’s (different VO’s) on behalf of the user.Trade Server negotiates with the DRB’s (different VO’s) on behalf of the user. Price negotiation results in optimal job distributions among Domains or VO’s. Price negotiation results in optimal job distributions among Domains or VO’s. Resource Broker optimally maps the jobs within each domain using a Distributed Mapping Strategy Resource Broker optimally maps the jobs within each domain using a Distributed Mapping Strategy

4. Preetam Ghosh School of Computing Mobile Grid: From System Level Perspective  Utilize huge resource pool of laptops, PDAs and other mobile devices –reduced CPU performance, small secondary storage, low battery power, and unreliable low-bandwidth communication  Motivate mobile devices to contribute their resources –negotiation mechanism (pricing strategy) –optimal Job allocation scheme Computational Grid Data Grid Grid Community Wireless Access Point A broad view of Grid Community, Wireless Access Point and Wireless devices Thomas Phan, Lloyd Huang, Chris Dulan “Challenge: Integrating Mobile Wireless Devices Into the Computational Grid ”MOBICOM’02, September 23-26,2002, Atlanta, Georgia, USA.Georgia

5. Preetam Ghosh School of Computing Mobile Grid Projects  AKOGRIMO (Access to Knowledge through the GRId in a MObile world) –Seen the integration problem from a business/commercialization perspective –Provide support for user mobility through the SIP –Terminal mobility through Mobile IPv6 techniques –Does not address incorporation of resource constrained devices  Cyber foraging (Surrogate Computing) –Offload demanding computational tasks to more capable nodes –Resource discovery and allocation –Runtime engine/platform to handle the offloading –Lack of provisioning service in small scale device A.Messer, I. Greeberg, P. Bernadat, and D. Milojicic, “Towards a Distributed Platform for Resource- Constrained Devices,” ICDCS'2002 B.M. Satyanarayanan, “Pervasive Computing: Vision and Challenges”, IEEE Personal Communications, 2001

6. Preetam Ghosh School of Computing Mobile Grid Projects  K*Grid –A Grid Research project supported and funded by Korean Ministry of Information and Communications –Make use of idle resources in the mobile community for high performance computing –Task scheduling  LEECH (Leveraging Every Existing Computer out there) –Proxy-based clustered approach for integrating mobile devices –Built on top of a customized MPI  P2P Middleware –Convergence of Grid and Peer to Peer techniques (P2P) 1. The K*Grid Mobile Grid project page: http://gridcenter.or.kr/MobileGrid/index.phphttp://gridcenter.or.kr/MobileGrid/index.php 2. N. Ruiz, “A Framework for Integrating Heterogeneous, Small Scale Devices into Computational Grids and Clusters," M.S. Thesis, University of California, US, 2003. 3. Foster and A. Iamnitchi, “On Death, Taxes, and the Convergence of P2P and Grid Computing,” in Proceedings of the 2nd International Workshop on Peer-to-Peer Systems (IPTPS '03), 2003.

7. Preetam Ghosh School of Computing Mobile Grid Challenges  Limited Resources –Make it difficult to install large software components (e.g. Globus Toolkit, due to s/w dependencies and significant amount of memory and storage capacity) –Needs a lightweight infrastructure (IBM’s web services Toolkit for Mobile devices)  Increased Dynamicity –Seamless terminal mobility, resource mobility, user mobility -> session management –User moves his session from one WAP to another –Need to take care of various mobile computing related issues (low and fluctuating bandwidth availability, mobility management etc.)

8. Preetam Ghosh School of Computing Mobile Grid Challenges  Increased Heterogeneity –should care for the bridging of a plethora of middleware possibilities  Integration Challenges –big differences in reliability, availability and performance –Operational failures due to low battery level or because of mobility and roaming –Unreliable weak link of mobile devices in the application execution chain –Protection of Grid scheduling and brokering systems from the reduced availability and unpredictability of the mobile resources

9. Preetam Ghosh School of Computing Importance of Mobility Management  User mobility (current and future) is very important in a mobile grid computing paradigm  Efficient resource usage of mobile devices along predicted routes  Guaranteeing accurate (timely) completion of allocated jobs (QoS)

10. Preetam Ghosh School of Computing  Efficient Symbolic Representation of node mobility –Movement patterns of a mobile node is a (piece-wise) stationary, stochastic (ergodic) process  Location update Strategy –Does not use location updates on every movement of the mobile node –Updates only on an appropriately determined entropy-minimized subset of this movement sequence –Estimates number of mobile nodes available (for job allocation) under a particular WAP within the threshold time T User Mobility Challenges Grid Controller (GC), Wireless Access Points (WAP), Basic Service Set (BSS), Extended Service Set (ESS)

11. Preetam Ghosh School of Computing Our Contributions in the System-Level Perspective  Goal is to harness the idle CPU cycles of mobile devices –Requires a pricing strategy that can attract the mobile device owners to contribute their devices for grid jobs.

12. Preetam Ghosh School of Computing Our Contributions in the System-Level Perspective  Goal is to harness the idle CPU cycles of mobile devices –Requires a pricing strategy that can attract the mobile device owners to contribute their devices for grid jobs.  The pricing strategy is then used to devise cost-effective grid job allocation schemes to the mobile nodes

13. Preetam Ghosh School of Computing Our Contributions in the System-Level Perspective  Goal is to harness the idle CPU cycles of mobile devices –Requires a pricing strategy that can attract the mobile device owners to contribute their devices for grid jobs.  The pricing strategy is then used to devise cost-effective grid job allocation schemes to the mobile nodes –consider the processing cost (or delay) at the mobile nodes  Need to consider the internal jobs at the mobile device (e.g. call processing activities)

14. Preetam Ghosh School of Computing Our Contributions in the System-Level Perspective  Goal is to harness the idle CPU cycles of mobile devices –Requires a pricing strategy that can attract the mobile device owners to contribute their devices for grid jobs.  The pricing strategy is then used to devise cost-effective grid job allocation schemes to the mobile nodes –consider the processing cost (or delay) at the mobile nodes  Need to consider the internal jobs at the mobile device (e.g. call processing activities) –consider the communication cost (or delay) for transferring the jobs (and hence the dynamic wireless channel bandwidth)

15. Preetam Ghosh School of Computing Our Contributions in the System-Level Perspective  Goal is to harness the idle CPU cycles of mobile devices –Requires a pricing strategy that can attract the mobile device owners to contribute their devices for grid jobs.  The pricing strategy is then used to devise cost-effective grid job allocation schemes to the mobile nodes –consider the processing cost (or delay) at the mobile nodes  Need to consider the internal jobs at the mobile device (e.g. call processing activities) –consider the communication cost (or delay) for transferring the jobs (and hence the dynamic wireless channel bandwidth) –consider the node mobility as the results of the jobs assigned by a particular WAP to the nodes need to come back to the WAP after completion  Requires a mobility management algorithm to track the number of mobile devices present under a particular WAP within a specific time period (in which the assigned jobs need to complete)

16. Preetam Ghosh School of Computing Our Contributions in the System-Level Perspective  Goal is to harness the idle CPU cycles of mobile devices –Requires a pricing strategy that can attract the mobile device owners to contribute their devices for grid jobs.  The pricing strategy is then used to devise cost-effective grid job allocation schemes to the mobile nodes –consider the processing cost (or delay) at the mobile nodes  Need to consider the internal jobs at the mobile device (e.g. call processing activities) –consider the communication cost (or delay) for transferring the jobs (and hence the dynamic wireless channel bandwidth) –consider the node mobility as the results of the jobs assigned by a particular WAP to the nodes need to come back to the WAP after completion  Requires a mobility management algorithm to track the number of mobile devices present under a particular WAP within a specific time period (in which the assigned jobs need to complete) –consider dynamic session management techniques i.e., if a particular mobile node goes out of the WAP’s coverage area, how can the completed jobs be transferred back to the original WAP.

17. Preetam Ghosh School of Computing Grid: Existing Pricing Strategies Market ModelAdopted by Auction ModelSpawn and Popcorn Bargaining ModelMariposa and Nimrod-G Posted Price ModelNimrod-G Commodity Market ModelMungi, MOSIX and Nimrod-G Bid based proportional ResourceRexec and Anemone Community,Coalition and BarteringSETI@Home,Condor,MojoNation Tender/Contract-Net ModelMariposa Table II: Different Distributed Computing Scheduling Systems with the adopted Game Theoretic Approach Lack of formulation Fails to capture competitiveness among the mobile users. Cooperative game theory solution not suitable

18. Preetam Ghosh School of Computing Motivation: Game theoretic Approach Figure 1: Dynamics of different Mobile User groups with different Wireless Access Point Mobile Users with WAP 1 Mobile Users with WAP p Groups of Mobile Users WAP 1 WAP p Grid Community Job assignment Job Assignment Resource assignment Resource assignment Pricing strategy implemented using a Game theoretic Model :-  Two player non-cooperative bargaining game  Efficient, Stable, Simple, Symmetric  No central Matchmaker  Optimal Static Job Allocation Scheme based on this pricing strategy.

19. Preetam Ghosh School of Computing Needs for Bargaining Model  Alternating-offer bargaining under incomplete information  Updates probabilistic preference list  Bargainers are rational  choose a strategy leading to Nash equilibrium  Conflict of interests  WAP Servers and Mobile users choose a mutually beneficial agreement  agreement can’t be imposed on either WAP Server or Mobile users without their approval

20. Preetam Ghosh School of Computing Mobile User WAP Server Proposal Accept Agreement Reject Counter-offer Continues until agreement/breaks off Bargaining Protocol

21. Preetam Ghosh School of Computing Cost Optimal Job Allocation Schemes None of these models consider the dynamic session management requirement ! SchemesMobile nodes dedicated for grid jobs ? Considers communication delay (wireless bandwidth) ? Considers node mobility ? Grid job class PRIMALYesNo Single PRIMANGLENoYesNoSingle PRIMULTINoYesNoMulti PRIBANDNoYesNoMulti PRIPROCNoYesNoMulti PRIMOBNoYes Single

22. Preetam Ghosh School of Computing Summary of the Job Allocation Schemes PRIMAL PRIMULTI PRIBAND PRIPROC PRIMANGLE PRIMOB M/G/1 Preemptive Priority Model (considers communication delay) Single-class jobs Multi-class jobs (do not consider node mobility) Does not consider node mobility Considers node mobility Generalized systems Bandwidth constrained systems Processing -power constrained systems Job Allocation Schemes M/M/1 Model (does not consider communication delay)

23. Preetam Ghosh School of Computing Publications: 1.Preetam Ghosh, Kalyan Basu and Sajal Das, A Game Theory based Pricing Strategy to support Single/Multi-Class Job Allocation Schemes for Bandwidth Constrained Distributed Systems. IEEE Transactions on Parallel and Distributed Systems, 2007, Volume 18, Issue 3, pp. 289-306. 2.Preetam Ghosh, Nirmalya Roy, Sajal Das and Kalyan Basu, A Pricing Strategy for Job Allocation in Mobile Grids using a Non-cooperative bargaining Theory Framework, in Special Issue on Design and Performance of Networks for Super-Cluster and Grid-Computing, JPDC, 2005, Volume 65, Issue 11, pp. 1366-1383. 3.Preetam Ghosh, and Sajal Das, Mobility-aware Cost-efficient Job Scheduling for Single-class Grid jobs in a generic Mobile Grid Architecture. Under 2 nd round review at Elsevier Future Generation Computer Systems, 2009. 4.Preetam Ghosh, Nirmalya Roy and Sajal Das, Mobility-based Cost-efficient Job Scheduling in Mobile grids. 1st IEEE International Workshop on Context-Awareness and Mobility in Grid Computing (held in conjunction with CCGrid 2007), 2007, Brazil, pp. 701-706. 5.Preetam Ghosh, Kalyan Basu and Sajal Das, Cost-Optimal Job Allocation Schemes for Bandwidth-Constrained Distributed Computing Systems. 12th Annual IEEE International Conference on High Performance Computing (HiPC), 2005, Goa, India, pp. 40-50. 6.Preetam Ghosh, Nirmalya Roy, Sajal Das and Kalyan Basu, A Game Theory based Pricing Strategy for Job allocation in Mobile Grids. 18th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2004, USA, pp. 82-91.

24. Preetam Ghosh School of Computing