Abstract Using a single cloud datacenter in Cloud network can have several disadvantages for users, from excess energy consumption to increase dissatisfaction of users of service and price of provided services. The Cloud broker as an intermediary between users and datacenters can play a key role to enhance users' satisfaction and reducing energy consumption of datacenters that are located geographically in different areas. In this paper, we have attempted to provide an algorithm that assigns datacenter to users through rating various datacenters. This algorithm has been simulated by Cloudsim and will result in high levels of user satisfaction, cost-effectiveness and improving energy consumption. In this paper, we show that this algorithm can save 44% of energy consumption and 7% of cost saving to users are in sample simulation space.

References

[1] Beloglazov A. and Buyya R., Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Ef cient Dynamic Consolidation of Virtual Machines in Cloud Data Centers, Concurrency and Computation Practice and Experience, vol. 24, no. 13, pp. 1397-1420, 2012.

[2] Beloglazov A., Energy-Ef cient Management of Virtual Machines in Data Centers for Cloud Computing, Thesis, the University of Melbourne, 2013.

[3] http://adrianotto.com/2013/01/ssd-power- savings-pays-for-itself/, Last Visited, 2015.

[4] http://energystar.org/about.html, Last Visited, 2014.

[5] http://greenit.netwhygreenit.html, Last Visited, 2014.

[6] Kapgate D., Efficient Service Broker Algorithm for Data Center Selection in Cloud Computing, International Journal of Computer Science and Mobile Computing, vol. 3, no. 1, pp. 355-365, 2014.

[7] Koomey J., Estimation Total Power Consumption by Services in the U.S. and the World, Technical Report, 2007.

[8] Limbani D. and Oza B., A Proposed Service Broker Policy for Data Center Selection in Cloud Environment with Implementation, International Journal Computer Technology and Applications, vol. 3, no. 3, pp. 1082-1087, 2012.

[9] Marinescu D., Cloud Computing Theory and Practice, Morgan Kaufmann, 2014.

[10] Raj G. and Setia S., Effective Cost Mechanism for Cloudlet Retransmission and Prioritized VM Scheduling Mechanism over Broker Virtual Machine Communication Framework, International Journal on Cloud Computing: Services and Architecture, vol. 2, no. 3, 2012. 3200 3300 3400 3500 3600 3700 3800 3900 Proposed AlgorithmLocal SelectionRandom Local Selection Flat DistributionProposed Algorithm with energy weight 0.2 and cost weight 0.4 Energy Consumption Improvement and Cost Saving by Cloud Broker in Cloud Datacenters 411

[11] Rodero I., Guim F., Corbalan J., Fong L., and Sadjadi S., Grid Broker Selection Strategies Using Aggregated Resource Information, Future Generation Computer Systems, vol. 26, no. 1, pp. 72-86, 2010.

[12] Schlegel T., Kowalczyk R., and Vo Q., Decentralized Co-Allocation of Interrelated Resources in Dynamic Environments, in Proceedings IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, pp. 104-108, 2008.

[13] Subramani K., Shanmugasundaram V., and Chandran K., Design and Implementation of a Synchronous and Asynchronous-Based Data Replication Technique in Cloud Computing, The International Arab Journal of Information Technology, vol. 13, no. 1, pp. 100-107, 2014.

[14] Teng F. and Magoul`es T., A New Game Theoretical Resource Allocation Algorithm for Cloud Computing, in Proceedings of International Conference on Green, Pervasive, and Cloud Computing, Hualien, pp. 321-330, 2010.

[15] Tordsson J., Moreno R., Moreno-Vozmediano R., and Llorente I., Cloud Brokering Mechanisms for Optimized Placement of Virtual Machines Across Multiple Providers, Future Generation Computer Systems, vol. 28, no. 2, pp. 358-367, 2012.

[16] Tsai J., Fang J., and Chou J., Optimized Task Scheduling and Resource Allocation on Cloud Computing Environment using Improved Differential Evolution Algorithm, Computers and Operations Research, vol. 40, no. 12, pp. 3045-3055, 2013.

[17] Yang C., Lin C., and Chen S., A Workflow- Based Computational Resource Broker with Information Monitoring in Grids, in Proceedings of 5th International Conference on Grid and Cooperative Computing, Hunan, pp. 199-206, 2006.

[18] Zhang Z., Wang T., Xiao L., and Ruan L., A Statistical Based Resource Allocation Scheme in Cloud, in Proceedings of International Conference on Cloud and Service Computing, Hong Kong, pp. 266-273, 2011. Ahmad Karamolahy received the B.S. degree in Computer Engineering from Sham sipur College of Tehran, Iran. He obtained M.Sc. degree in Software Computer Engineering from Azad University of Kermanshah, Iran. His research interests are fields of Cloud Computing, Search Engine Crawler Algorithms and Analyzing connections in Social Networks. Abdolah Chalechale born in Kermanshah, Iran, received his B.S. Electrical Engineering (Hardware) and Computer Engineering (Software) from Sharif University of Technology, Tehran, Iran. He received his Ph.D. degree from Wollongong University, NSW, Australia in 2005 and currently is with Razi University, Kermanshah, Iran. His research includes image processing, machine vision and human-machine interaction. Mahmood Ahmadi received the B.S. degree in Computer engineering from Isfahan University, Isfahan, Iran. He received the M.Sc. degrees in Computer architecture and engineering from Tehran Poly technique University. He got his PhD in May 2010. His research interests include Computer architecture, network processing, signal processing and reconfigurable computing. He is currently working as an assistant professor in the Department of Computer Engineering at the Razi University, Kermanshah, Iran.