A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT
Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, a multi-objective load balancing algorithm has been proposed to avoid deadlocks and to provide proper utilization of all the virtual machines (VMs) while processing the requests received from the users by VM classification. The capacity of virtual machine is computed based on multiple parameters like MIPS, RAM and bandwidth. Heterogeneous virtual machines of different MIPS and processing power in multiple data centers with different hosts have been created in cloud simulator. The VMâ€™s are divided into 2 clusters using K-Means clustering mechanism in terms of processor MIPS, memory and bandwidth. The cloudlets are divided into two categories like High QOS and Low QOS based on the instruction size. The cloudlet whose task size is greater than the threshold value will enter into High QOS and cloudlet whose task size is lesser than the threshold value will enter into Low QOS. Submit the job of the user to the datacenter broker. The job of the user is submitted to the broker and it will first find the suitable VM according to the requirements of the cloudlet and will match VM depending upon its availability. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. This modified algorithm has an edge over the original approach in which each cloudlet build their own individual result set and it is later on built into a complete solution.
 S. Kapoor and D. C. Dabas, "Cluster Based Load Balancing in Cloud Computing," IEEE, 2015.
 S. Garg, R. Kumar and H. Chauhan, "Efficient Utilization of Virtual Machines in Cloud Computing using Synchronized Throttled Load Balancing," 1st International Conference on Next Generation Computing Technologies (NGCT-2015), pp. 77-80, 2015.
 R. Panwar and D. B. Mallick, "Load Balancing in Cloud Computing Using Dynamic Load Management Algorithm," IEEE, pp. 773-778, 2015.
 M. Belkhouraf, A. Kartit, H. Ouahmane, H. K. Idrissi, Z. Kartit and M. E. Marraki, "A secured load balancing architecture for cloud computing based on multiple clusters," IEEE, 2015.
 L. Kang and X. Ting, "Application of Adaptive Load Balancing Algorithm Based on Minimum Traffic in Cloud Computing Architecture," IEEE, 2015.
 N. K. Chien, N. H. Son and H. D. Loc, "Load Balancing Algorithm Based on Estimating Finish Time of Services in Cloud Computing," ICACT, pp. 228-233, 2016.
 H. H. Bhatt and H. A. Bheda, "Enhance Load Balancing using Flexible Load Sharing in Cloud Computing," IEEE, pp. 72-76, 2015.
 S. S. MOHARANA, R. D. RAMESH and D. POWAR, "ANALYSIS OF LOAD BALANCERS IN CLOUD COMPUTING," International Journal of Computer Science and Engineering (IJCSE) , pp. 102-107, 2013.
 M. P. V. Patel, H. D. Patel and . P. J. Patel, "A Survey On Load Balancing In Cloud Computing," International Journal of Engineering Research & Technology (IJERT), pp. 1-5, 2012.
 R. Kaur and P. Luthra, "LOAD BALANCING IN CLOUD COMPUTING," Int. J. of Network Security, pp. 1-11, 2013.
 Kumar Nishant, , P. Sharma, V. Krishna, Nitin and R. Rastogi, "Load Balancing of Nodes in Cloud Using Ant Colony Optimization," IEEE, pp. 3-9, 2012.
 Y. Xu, L. Wu, L. Guo,, Z. Chen, L. Yang and Z. Shi, "An Intelligent Load Balancing Algorithm Towards Efficient Cloud Computing," AI for Data Center Management and Cloud Computing: Papers from the 2011 AAAI Workshop (WS-11-08), pp. 27-32, 2011.
 A. K. Sidhu and S. Kinger, "Analysis of Load Balancing Techniques in Cloud Computing," International Journal of Computers & Technology Volume 4 No. 2, March-April, 2013, ISSN 2277-3061, pp. 737-741, 2013.
 O. M. Elzeki, M. Z. Reshad and M. A. Elsoud, "Improved Max-Min Algorithm in Cloud Computing," International Journal of Computer Applications (0975 â€“ 8887), pp. 22-27, 2012.
 B. Kruekaew and W. Kimpan, "Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony," Proceedings of the International Multi Conference of Engineers and Computer Scientists 2014 Vol I,IMECS 2014, 2014.
 R.-S. Chang, J.-S. Chang and P.-S. Lin, "An ant algorithm for balanced job scheduling in grids," Future Generation Computer Systems 25 (2009) 20â€“27, pp. 21-27, 2009.
 Z. Chaczko, V. Mahadevan, S. Aslanzadeh and C. Mcdermid, "Availability and Load Balancing in Cloud Computing," International Conference on Computer and Software Modeling IPCSIT vol.14 (2011) Â© (2011) IACSIT Press, Singapore, pp. 134-140, 2011.
 R. K. S, S. V and V. M, "Enhanced Load Balancing Approach to Avoid Deadlocks in Cloud," Special Issue of International Journal of Computer Applications (0975 â€“ 8887) on Advanced Computing and Communication Technologies for HPC Applications - ACCTHPCA, June 2012, pp. 31-35, 2012.
 Kumar Nishant, P. Sharma, V. Krishna, N. and R. Rastogi, "Load Balancing of Nodes in Cloud Using Ant Colony Optimization," IEEE, pp. 3-9, 2012.
 Ankit Kumar, Mala Kalra,â€ Load Balancing in Cloud Data Center Using Modified Active Monitoring Load Balancerâ€, IEEE pp. 1-5, 2016.
 Saraswathi AT, Kalaashri.Y.RA, Dr.S. Padmavathi, â€œDynamic Resource Allocation Scheme in Cloud Computingâ€, ELSEVIER, pp. 30-36, 2015
Authors retain the copyright of their manuscripts, and all Open Access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.