*

maryam.rastegarzadeh@qiau.ac.ir

mmeybodi@aut.ac.ir

morshedlou@aut.ac.ir

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Disc = {Disc1, , Discz} Host = { Host1, , Hostu } CPU = {CPU1, , CPUv} DC = {DC1, , DCw} Cost = {Cost1, , Costn} Bandwith = {BD1, , BDs}

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. Algorithm: Resource Allocation Optimization by Learning Automata (RAOLA)

RAOLA (T, V, H, TH, S) Inputs: T: List of Request for VMs from client applications. Spase: Set of space/ GB, DC: number of Datacenter, CPU: set of CPU / GHz. Host: number of Host, Cost: List of cost for Network, BW: the Bandwidth of Network. TH: Threshold H: Array with size TH, initial is set by 0 Output: S: List of VM allocated to T where Utility is High and Debt is low Initialize: In each state, by Learning Automaton for Resource requirement of Task get decided. The set of actions of this LA is the set of permissible VM allocated to tasks (or cloudlet). The following steps are taken 1. while T

do 2. Compute T and Vf

3. Sort T; 4. for i = 1 to n do 5. if Ti

Vf

6. Add Ti to T'" 7. fi 8. of 9. while Vf

10. for i= 1 to n' // n' n 11. X=1; 12. for j =i to n' 13. if H(Ti)

H(Tj) do 14. X=0; 15. fi 16. of 17. if X=1 do 18. A VM allocated to Ti

19. Update H(|Ti|) // as 1-3 Equation 20. Update T // as 1-4 Equation 21. Update Vf // as 2-4 Equation 22. fi 23. Of 24. end while 25. end while

.

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CloudSim

CloudSim

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Grid

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User Code

CloudSim

Core Simulation Engine

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CloudSim

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CloudSim

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Cloudlet :

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Vm :

Time-Shared

Space-Shared.

CPU:Xeon 2.66

GH .

Memory:

32 GB .

.

(

long size = 50000; //Size of cloudlet int ram = 2024; int mips = 650; long bw = 10000; // Bound wide int pesNumber = 24; // No. CPU vmm = "Xen"; // VMM name

:

gridsimsimjav

cloudsim

cloudlet.

.

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RAOLA

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(

]) [(

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01020

30405060

7080

40 42 50 60 70 80 90 100 110 120 130 140 150

RAOLA

LSTR

(cloudlet

|Ti|

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2 0 0

4 0 0

6 0 0

8 0 0

10 0 0

1 2 3 4 5 6 7 8 9 10

R A O LA

LS TR

0

5

10

15

20

25

30

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130

150

RAOLA

LSTR

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cloudlet

-

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.

.

.

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[10] S. Sawant, "A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment", Master's Projects, Paper 198, 2011.

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algorithms for Infrastructure as a Service Clouds , Future Generation Computer Systems, pp. 158 169, Vol. 29, Issue. 1, 2013.

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Utilization and Performance of Cloud Systems based on Grid Middleware , KSII The first International Conference on Internet (ICONI), pp. 25-30, 2009.

[14] H. Kim, W. Kim, Y. Kim, Experimental Study to Improve Resource Utilization and Performance of Cloud Systems Based on Grid Middleware , Journal of Communication and Computer, Vol.7, No.12, pp. 25-30, 2010.

[15] G. Sireesha, L.Bharathi, Exploiting dynamic resource allocation for efficient parallel data processing in the cloud , International Journal of Engineering Research & Technology (IJERT), Vol. 1, Issue 6, pp.1-6 , 2012.

[16] S. Nagadevi, P. Sharmila, An Approach Towards Efficient Dynamic Resource Allocation in Nephele , The International Journal of Computer Science & Applications (TIJCSA), Vol. 1, No. 11, pp. 67-73, 2013.

[17] M. R. Meybodi, H. Beigy, M. Taherkhani, Cellular Learning Automata and Its

Applications , Journal of Science and Technology, University of Sharif, No. 25, pp. 54-77, 2004.

1 Resource 2 Infrastructure 3 Safely 4 Scalability 5 Availability 6 One Service for several times 7 Multiple Service for one time 8 Directed execution graphs 9 system load balancing 10 Ability 11 Workflow 12 Request 13 Task 14 Shortest task first 15 Average application execution 16 Makespan 17 Debt

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