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4 Process Burst time Table 2. Process table Table 2 in the above is defining the process pool with several Burst Time 15, 10 and 5. Here we have mentioned the burst table according to the present scenario of system state. P 2 P 1 P 3 P 2 P 1` P 2 Table 2.1. Burst table for the above processes The estimated waiting time of the several processes and the average waiting time of the cloud service provider P 1 (5+(20-10)) 15 P 2 (0+( )+(25-20) P 3 10 Table 2.2. Waiting time of several processes Here the average Waiting time = ( )/3 = 40/3 = The third option is being explained in the next table of process pool. Here a slight variation can be seen using different notion of process shuffling inside CSP. Table 3. Process table Table 3 in the above is defining the process pool with several Burst Time 10, 5 and 15. Here we have mentioned the burst table according to the present scenario of system state. P 1 P 3 P 2 P 1 P 2 P 2 Table 3.1 Burst table for the above processes Process name Calculation of waiting time Table 3.2 Waiting time for several processes Here the average Waiting time = ( )/3 = 30/3 = 10. The fourth option is being explained in the next table of process pool. Here a slight variation can be seen using different notion of process shuffling inside CSP. Output P 1 (0+(15-5)) 10 P 2 (10+(20-15)) P a g e

5 P 2 10 P 1 15 Table 4. Process table Table 4 in the above is defining the process pool with several Burst Time 15, 5 and 10. Here we have mentioned the burst table according to the present scenario of system state. P 2 P 3 P 1 P 2 P 1 P 2 Table 4.1 Burst Table for the above processes P 1 (10+(20-15)) 15 P 2 (0+( )+(25-20)) Table 4.2 Waiting time for several processes Here the average Waiting time = ( )/3 = 35/3 = The fifth option is being explained in the next table of process pool. Here a slight variation can be seen using different notion of process shuffling inside CSP Table 5. Process table Table 5 in the above is defining the process pool with several Burst Time 5, 15 and 10. Here we have mentioned the burst table according to the present scenario of system state. P 3 P 2 P 1 P 2 P 1 P 2 Table 5.1 Burst table for the above processes P 1 (10+(20-15)) 15 P 2 (0+( )+(25-20)) P 3 0 Table 5.2 Waiting time for several processes 145 P a g e

7 ii. system must be degraded a lot. The algorithm is not applicable in online application, means when any new process arrives at RQ the GAP must not use it in current operation which is already started. So the algorithm works as static mode. IV. CONCLUSION AND FUTURE WORK Previous two sections we describe the GA based scheduling algorithm. Genetic algorithm is a heuristic search algorithm and it always find out the solution which takes minimum time to execute or find optimal solution from the set of possible solutions. This algorithm must increase the throughput of the system. Here the tasks those are ready to execute are operate in all possible way and find the best sequence of the tasks which average waiting time must be optical one. So the waiting time of the system must be optimum. In future we are trying to reduce the problems of that algorithm and try to create another algorithm which also produces an optimal solution. In future we also create a simulator of that algorithm and implement it in our environment. REFERENCES [1] Service Performance and Analysis in Cloud Computing by Kaiqi Xiong, Harry Perros /09 \$ IEEE page [2] Virtual Infrastructure Management in Private and Hybrid Clouds by Borja Sotomayor, Rubén S. Montero and Ignacio M. Llorente, Ian Foster /09/\$ IEEE [3] Research on Distributed Architecture Based on SOA by Hongqi Li, Zhuang Wu /09 \$ IEEE [4] A Berkeley View of Cloud computing. M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia. Above the Clouds: Technical Report No. UCB/EECS , University of California at Berkley, USA, Feb. 10, 2009 [5] Handbook of Genetic Algorithms, Davis, L Van,Nostrand Reinhold. [6] An Approach to a Cloud Computing Network by Francesco Maria Aymerich, Gianni Fenu1, Simone Surcis /08/\$ IEEE 113 page [7] Cloud Computing and Services Platform Construction of Telecom Operator by Xu Lei, Xin Zhe, Ma Shaowu, Tang Xiongyan. Broadband Network & Multimedia Technology, IC-BNMT '09. 2nd IEEE International Conference on Digital Object Identifier, pp [8] Service Performance and Analysis in Cloud Computing, Kaiqi Xiong and Harry Perros, 2009 Congress on Services I [9] An Optimistic Differentiated Service Job Scheduling System for Cloud Computing Service Users and Providers by Luqun Li 2009 Third International Conference on Multimedia and Ubiquitous Engineering page [10] Multiprocessor Tasks with Genetic Algorithms, M Golub, S Kasapovic Scheduling - Applied Informatics- Proceedings [11] Parallel Genetic Algorithms to Find Near Optimal Schedules for Tasks on Multiprocessor Architectures, Communicating Process Architectures, Moore, M., IOS Press, 2001, pgs, [12] Process Scheduling with Genetic Algorithms, Pai-Chou Wang, W. Korfhage, Proceedings of the 7th IEEE Symposium on Parallel and Distributed Processing, Page: 638, ISBN: , October 2005, IEEE Computer Society Washington, DC, USA. 147 P a g e

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