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Adaptive Cloud Computing Based Services for Mobile Users

This paper discusses adaptive cloud-based computing services aimed at mobile users. It presents a binary programming optimization model to efficiently provision these services, considering factors like user mobility, energy consumption, and the quality of service (QoS). The simulation setup is utilized to evaluate various hosting strategies across multiple data centers while dynamically adjusting to user locations. The aim is to minimize costs associated with energy and service-level violations while maximizing service quality for mobile users in varying operational scenarios.

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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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