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

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

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

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

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

Related work Cloud computing Cloud computing for mobiles 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

Problem description

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

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

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

Cost Model $ $ $ Computation Energy Cost Quality of Service Cost [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

Problem formulation

Energy Cost Linear utilization model Linear power consumption model ω Idle power power ω + α Maximum power Utilization 1 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

SLA Violation Cost η: paid per user

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

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|

Simulation

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

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

Active data centers

-Cost comparison

Conclusion Simulation setup improvement Modeling Evaluation Mobility pattern Costs Modeling Migration modeling Evaluation

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.

Flow of the simulator