Improved Scheduling Procedure for Intensify Resource Utilization in Heterogeneous Cloud Environment
DOI:
https://doi.org/10.26438/ijcse/v6i5.304308Keywords:
FJFFA, Optimal job selection, least cost and maximum costAbstract
Resource allocation is critical to investigate the need for resources in substantially enhancing every day. To tackle this issue our proposed policy presents a new hybrid strategy known as the fittest job firefly algorithm(FJFFA) which sorts the jobs in the queue according to least cost and maximum profit. This queue is presented to firefly algorithm. Jobs are again sorted randomly and presented to firefly algorithm. The solution thus obtained from the algorithm is superior. Makespan and Flowtime obtained as a result is improved by 6%.
References
A. Juan et al., “OPTIMIS : A holistic approach to cloud service provisioning,” Futur. Gener. Comput. Syst., vol. 28, no. 1, pp. 66–77, 2012.
Y. Chu, N. Huang, S. Member, and S. Lin, “Quality of Service Provision in Cloud-based Storage System for Multimedia Delivery,” IEEE, vol. 8, no. 1, pp. 292–303, 2014.
E. R. Gomes, Q. B. Vo, and R. Kowalczyk, “Pure exchange markets for resource sharing in federated clouds,” wileyonlinelibrary, pp. 977–991, 2012.
H. Shen, S. Member, G. Liu, and S. Member, “An Efficient and Trustworthy Resource Sharing Platform for Collaborative Cloud Computing,” IEEE Trans., vol. 25, no. 4, pp. 862–875, 2014.
A. Beloglazov and R. Buyya, “Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers Under Quality of Service Constraints,” IEEE Commun. Lett., vol. X, no. X, pp. 1–14, 2012.
J. F. N. W. Lang, J.M. Patel, “On Energy Management, Load Balancing and Replication,” ACM SIGMOD Rec., pp. 35–42, 2009.
J. So and N. H. Vaidya, “Load-balancing routing in multichannel hybrid wireless networks with single network interface,” IEEE Trans. Veh. Technol., vol. 56, no. 1, pp. 342–348, 2007.
M. M. Alobaedy and K. R. Ku-Mahamud, “Scheduling jobs in computational grid using hybrid ACS and GA approach,” Proc. - 2014 IEEE Comput. Commun. IT Appl. Conf. ComComAp 2014, pp. 223–228, 2014.
D. Paul and S. K. Aggarwal, “Multi-objective evolution based dynamic job scheduler in grid,” Proc. - 2014 8th Int. Conf. Complex, Intell. Softw. Intensive Syst. CISIS 2014, pp. 359–366, 2014.
R. K. Jena, “Multi objective Task Scheduling in Cloud Environment Using Nested PSO Framework,” Procedia - Procedia Comput. Sci., vol. 57, pp. 1219–1227, 2015.
M. Wang and W. Zeng, “A comparison of four popular heuristics for task scheduling problem in computational grid,” 2010 6th Int. Conf. Wirel. Commun. Netw. Mob. Comput. WiCOM 2010, pp. 3–6, 2010.
C. R. Reeves, “A genetic algorithm for flowshop sequencing,” Comput. Oper. Res., vol. 22, no. 1, pp. 5–13, Jan. 1995.
S. Saha, S. Pal, and P. K. Pattnaik, “A Novel Scheduling Algorithm for Cloud Computing Environment,” vol. 1, 2016.
T. Keskinturk, M. B. Yildirim, and M. Barut, “An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times,” Comput. Oper. Res., vol. 39, no. 6, pp. 1225–1235, 2012.
L. Zuo, L. E. I. Shu, and S. Dong, “A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing,” IEEE Access, vol. 3, 2015.
N. Jain and K. Inderveer, “Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach,” J. Grid Comput., 2016.
A. Khatami and S. H. A. Rahmati, “An efficient firefly algorithm for the flexible job shop scheduling problem,” pp. 2144–2146, 2015.
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