Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

A Novel Method to Improve the Traffic Cost and Energy Consumption of Grid Networks based on AHP

Samira Hourali, Fatemeh Hourali Published in Networks

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
Download full text
  1. Samira Hourali and Fatemeh Hourali. Article: A Novel Method to Improve the Traffic Cost and Energy Consumption of Grid Networks based on AHP. International Journal of Applied Information Systems 8(7):38-43, May 2015. BibTeX

    	author = "Samira Hourali and Fatemeh Hourali",
    	title = "Article: A Novel Method to Improve the Traffic Cost and Energy Consumption of Grid Networks based on AHP",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 7,
    	pages = "38-43",
    	month = "May",
    	note = "Published by Foundation of Computer Science, New York, USA"


The problem of Virtual Machine (VM) placement in a compute grid infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the grid provider infrastructure. One of the most important objectives of the VM placement algorithm is determine the optimal location of virtual machines in physical servers, So that the minimum number of physical servers to be turned on for enhancing the overall performance of the grid environment. Efficient placement of VMs in PMs (Physical Machines) in grid environment improves resources utilization and energy consumption. In this paper, we employ AHP method to design an integrated VM placement algorithm, called AHP VM Placement (AHPVMP) which can reduce the number of running PMs and lower the energy consumption. Extensive simulation results in GridSim environment show that the proposed algorithm outperforms existing algorithms in terms of traffic cost, SLA, energy and migration.


  1. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic. 2009. Grid computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility, Elsevier Future Generation Computer Systems 25 (2009)599–616.
  2. A. Lenk, M. Klems, J. Nimis, S. Tai, T. Sandholm. 2009. What's inside the Grid? anarchitectural map of the Grid landscape, in: Proc. of the 2009 ICSE Workshop on Software Engineering Challenges of Grid Computing, , pp. 23–31.
  3. Armbrust M, Fox A, Griffith R, et al. 2009. Above the grids: A berkeley view of grid computing. Technical Report UCB/EECS-2009-28. Berkeley, CA, USA: EECS Department, University of California at Berkeley.
  4. S. Lee, R. Panigrahy, V. Prabhakaran, V. Ramasubramanian, K. Talwar, L. Uyeda, U. Wieder. 2011. Validating heuristics for virtual machines consolidation, Technical Report.
  5. Biran, O. et al. 2012. A stable network-aware VM placement for Grid Systems. Proceedings of the IEE CCGride'12, Ottawa.
  6. Meng, X. et al. 2010. Improving the scalability of data center networks with traffic-aware virtual machine placement. Proceedings of the 29 th Conference on Information Communications (INFOCOM'10).
  7. Gartner Says Energy-Related Costs Account for Approximately 12% of Overall Data Center Expenditures:http://www. gartner. com/it/page. jsp?id=1442113, 2011.
  8. K. H. Kim, A. Beloglazov, R. Buyya. 2009. Power-aware provisioning of grid resources for real-time services, in: Proc. of the 7th International Workshop on Middleware for Grids, Grids and e-Science, MGC 2009.
  9. D. S. Dias and L. H. M. K. Costa. 2012. Online traffic-aware virtual machine placement in data center networks. In Global Information Infrastructure and Networking Symposium (GIIS), pages 1-8.
  10. Ankit Anand, 2013. Adaptive Virtual Machine Placement supporting performance SLAs. A Project Report submitted in partial ful_lment of the requirements for the Degree of Master of Technology In Computational Science, Super Computer Education and Research Centre Indian Institute of Science Bangalore-560 012 (INDIA), 10-23.
  11. Aaron Carroll, Gernot Heiser, 2010. An Analysis of Power Consumption in a Smartphone, USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference ,21-25.
  12. Calcavecchia, N. M. ; Dipt. di Elettron. e Inf. , Politec. di Milano, Milan, Italy ; Biran, O. ; Hadad, E. ; Moatti, Y. 2012. VM Placement Strategies for Grid Scenarios. Grid Computing (GRID), 2012 IEEE 5th International Conference on, 852 – 859.
  13. Saaty T. L. 1980. The Analytic Hierarchy Process: Planning Setting Priorities, McGraw Hill Text, New York, USA.
  14. Winston, W. L. 1994. Operational Research: Application and Algorithms, International Thompson Publishing, Belmont, California, USA.
  15. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose, and Rajku- mar Buyya. 2011. "Gridsim: a toolkit for modeling and simulation of grid computing environments and evaluation of resource provisioning algorithms". Softw. Pract. Exper. , 41(1):23-50.
  16. Atiq Rehman and M. Hussain. 2011. Efficient grid data condentiality for daas. International Journal of Advanced Science and Technology, 35:1-10, October.
  17. A. Singh, M. Korupolu, and D. Mohapatra. 2008. Server-storage virtualization: Integration and load balancing in data centers. In High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference , pages 1-12.
  18. Ismael Solis Moreno and Jie Xu. 2011. Energy-efficiency in grid computing environments: Towards energy savings without performance degradation. IJCAC, 1(1):17-33.
  19. Hieu Trong Vu, Soonwook Hwang. 2014. A Traffic and Power-aware Algorithm for Virtual Machine Placement in Grid Data Center. International Journal of Grid & Distributed Computing, Vol. 7 Issue 1, 350-355.
  20. C. Isci, J. E. Hanson, I. Whalley, M. Steinder, J. O. Kephart. 2010. Runtime demand estimation for effective dynamic resource management, in: Proc. of the IEEE Network Operations and Management Symposium, NOMS.
  21. A. Beloglazov, R. Buyya. 2010. Energy efficient resource management in virtualized grid data centers, in: Proc. of the 10th IEEE/ACM International Conference on Cluster, Grid and Grid Computing.
  22. H. S. Abdelsalam, K. Maly, R. Mukkamala, M. Zubair, D. Kaminsky. 2009. Analysis of energy efficiency in Grids, in: Proc. of the Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns.
  23. G. Jung, M. A. Hiltunen, K. R. Joshi, R. D. Schlichting, C. Pu, Mistral. 2010. dynamically managing power, performance, and adaptation cost in Grid infrastructures, in: Proc. of the IEEE 30th International Conference on Distributed Computing Systems, ICDCS'10.
  24. Gartner Estimates ICT Industry Accounts for 2 Percent of Global CO2 Emissions: http://www. gartner. com/it/page. jsp?id=503867, 2011.


Energy Consumption, AHP, Migration, PM.