With the contempo slump and the immutable crush to deliver more services at a lower cost. Delivery model offers lower cost, and can make quick construction services. IT economics are changing rapidly, and large companies, in particular, looking for new ways to secure capital at a lower cost to maintain the viability of the company. Task scheduling problems are first class related to the overall efficiency of cloud computing facilities. Most developed algorithms for automation planning approach in one parameter of quality of service (QoS). However, if we consider more than one QoS parameter then the problem becomes more challenging. To address the problem, we need to introduce a scheduling strategy for multi-workflows with multiple QoS constrained for cloud computing. We need to introduce an optimized algorithm for task scheduling in cloud computing and its implementation. Furthermore, Load Balancing is a method to distribute workload across one or more servers, network interfaces, hard drives, or other computing resources. Use these components with the load balancing, on the one chamber, grow well in redundancy.
This study contributes in the existing literature of how to improve task scheduling. In this paper we allocate appropriate services for processing the workflow tasks and schedule the tasks on the services according to the requirements and the cloud environment. Efficient task scheduling method can meet users' demands, and Improve the utilization always come across a good deal of the world environment.
M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, A. Stoica, and Zaharia, "Above the clouds: A berkeley view of cloud computing," UC Berkeley Reliable Adaptive Distributed Systems Laboratory, Technical Report No. UCB/EECS-2009-28. Available: http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.html. [Accessed Feb 2009], 2009.
M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, A. Stoica, and Zaharia, "A view of cloud computing," Communications of the ACM, vol. 53, pp. 50-58, 2010.
W. Brian Arthur, "Competing technologies, increasing returns, and lock-in by historical events," The Economic Journal, vol. 99, pp. 116-131, 1989.
R. Buyva and M. Murshed, "Grid sim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and computation," Practice and Experience, vol. 14, pp. 1175-1220, 2003.
Buyya, "Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility," Future Generation Computer Systems, vol. 25, pp. 599-616, 2009.
R. Buyya, R. Ranjan, and R. N. Calheiros, "Modeling and simulation of scalable cloud computing environments and the cloud sim toolkit: challenges and opportunities. Siglm: Signature-driven load management for cloud computing infrastructures," presented at the IEEE, International Conference, 2009.
N. B. Sarfraz and A. B. Mervat, "Exploit of open source hypervisors for managing the virtual machines on cloud," (IJAEST) International Journal of Advanced Engineering Sciences and Technologies, vol. 9, pp. 055 - 060, 2011.
T. Sandeep, "Tasks scheduling optimization for the cloud computing systems," (IJAEST) International Journal of Advanced Engineering Sciences and Technologies, vol. 5, pp. 111 – 115, 2011.
S. C. Chengdu, "A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing," presented at the International Symposium on Parallel and Distributed Processing with Applications, IEEE, 2009.
C. Qi, "An optimized algorithm for task scheduling based on activity based costing in cloud computing," presented at the IEEE Explore, Beijing Conference, 2009.
D. Anderson, "BOINC: A system for public-resource computing and storage," in Proceedings of the Fifth IEEE/ACM International Workshop on Grid Computing, 2004, pp. 4–10.