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Adaptive Cloud Computing Based Services for Mobile Users

Adaptive Cloud Computing Based Services for Mobile Users. Zahra Abbasi Adel Dokhanchi. Talk outline. Introduction Problem description: Adaptive cloud based service provisioning Problem formulation Formulating the problem as a binary programming optimization problem

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Adaptive Cloud Computing Based Services for Mobile Users

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  1. Adaptive Cloud Computing Based Services for Mobile Users Zahra Abbasi Adel Dokhanchi

  2. Talk outline • Introduction • Problem description: • Adaptive cloud based service provisioning • Problem formulation • Formulating the problem as a binary programming optimization problem • Simulation setup and evaluation

  3. Introduction-Motivation • Virtualized network/Cloud computing • The detail of infrastructure is hidden for service providers and users • Applications can be hosted in any node in a dynamic fashion

  4. Introduction- Assumptions • Providing service for mobile users through clouds • Cloud based services: Infrastructure of the network and DC are hidden from service provider and users • Service can be hosted in any DC of the cloud • The access point of mobile users changes over time

  5. Hosting models for mobile users • Extreme scenarios • Hosting the server in one data center • Hosting the servers in all data center • Adaptive could based service • Dynamically changing the # and location of hosting • Minimizing energy consumption • Maximizing quality of service for mobile users

  6. Related work • Cloud computing • New technology • Demand new algorithms/mechanisms for scheduling, security, accounting • Cloud computing for mobiles • Online or offline computing • Dynamic service migration for mobile users • Dynamic scheduling across data centers • Energy cost model

  7. Problem description

  8. Data Centers and Mobile Locations • M=4 data centers • K=10 locations • Each area ai contains ni users • N varies over time 2 3 1 4 10 4 1 3 9 2 5 8 6 7

  9. Delays between mobiles and servers • Mobility of users in each area changes nj • dij is the delay from data center si to area aj • M×K matrix for delays 2 3 1 d42 d43 4 10 OFF ON 4 OFF 1 d35 OFF OFF ON 3 9 5 2 d36 8 d37 6 7

  10. Architecture model a2 a3 a4 a2 -QoS requirement -# of users Scheduler (onSlots) X11 X31 s1 s2 s3 -Energy cost -performance parameters -utilization

  11. Cost Model [Kuris et. al.] ICAC 2008 • Computation Energy Cost • Paid to Data Center • Quality of Service Cost • Paid to Mobile User • Delay causes Service Level Violation • Migration Cost • Paid to Virtual Network provider • Imposes Delay Energy Cost $ Energy Cost $ QoS Cost $ Service Provider

  12. Problem formulation

  13. power Energy Cost ω + α Maximum power ω Idle power • Linear utilization model ui=nc • Linear power consumption model • Linear energy cost model: • zi: {0,1} • 1->si is in service • 0->si is NOT in service 0 1 Utilization

  14. SLA Violation Cost • η: paid per user

  15. Migration Cost • Migration cost: Setup a new service in a DC for connected users • Constant migration cost (β) • μij: migrate or not to migrate

  16. Binary programming model of the problem • Minimize total cost: • Subject to: • All variables are binary. • All users are assigned to a center: • Idle power for non zero utilized servers: • Migration: Binary programming are generally NP-complete BP=LP for uni-modular constraint matrix (B) # of vars: |A||S|+2|S| # of constraints: |A|+|S|+|A||S|

  17. Simulation

  18. Simulation setup • Developing a simulator by MATLAB • Solving the problem by GLPK solver (GLPK+MATLAB) • Verification/evaluation

  19. Preliminary simulation setup • Uniform mobility pattern 2 3 1 4 10 2 1 d35 3 9 5 4 8 6 7

  20. Active data centers

  21. -Cost comparison

  22. Conclusion • Simulation setup improvement • Mobility pattern • Costs • Modeling • Migration modeling • Evaluation

  23. Referenes • [M. Bienkowski et al] “Competitive analysis for service migration in Vnets” ACM Virtualized Infrastructure Systems and Architectures, 2010. • K. Kumar et al] “Cloud computing for mobile users: Can off loading computation save energy?” IEEE Computer, vol. 99, pp. 51–56, 2010. • [M. Satyanarayanan et al] “The case for vm-based cloudlets in mobile computing,” IEEE Pervasive Computing, vol. 8(4), pp. 14–23, 2009. • D. Kusic et al] “Power and performance management of virtualized computing environments via lookahead control,” IEEE Cluster Computing, vol. 12, pp. 1–15, 2009. • [F. Hermenier et al] “Entropy: a consolidation manager for clusters,” ACM Virtual Execution Environmen, pp. 41–50 , 2009.

  24. Flow of the simulator

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