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

2. 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

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 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

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

11. 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

12. Problem formulation

13. Energy Cost Linear utilization model Linear power consumption modelω
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

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 Modeling Evaluation
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,
K. Kumar et al] “Cloud computing for mobile users: Can off loading computation save energy?” IEEE Computer, vol. 99, pp. 51–56,
[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