MATEC Web of Conferences
Volume 57, 20164th International Conference on Advancements in Engineering & Technology (ICAET-2016)
|Number of page(s)||3|
|Section||Information Systems & Computer Science Engineering|
|Published online||11 May 2016|
Efficient Job Provisioning for a Cloud Service Provider
1 Student, CSE Department, Yadavindra College of Engineering, Talwandi Sabo
2 Assistant professor, CSE Department, Yadavindra College of Engineering, Talwandi Sabo
Cloud Computing is a very fast emerging technology as every enterprise is moving fast towards this system. Cloud Computing is known as a provider of dynamic services. It optimizes a very large, scalable and virtualized resource. So lots of industries have joined this bandwagon nowadays. One of the major research issues is to maintain good Quality of Service (QoS) of a Cloud Service Provider (CSP). The QoS encompasses different parameters, like, smart job allocation strategy, efficient load balancing, response time optimization, reduction in wastage of bandwidth, accountability of the overall system, etc. The efficient allocation strategy of the independent computational jobs among different Virtual Machines (VM) in a Data center (DC) is a distinguishable challenge in the Cloud Computing domain and finding out an optimal job allocation strategy guided by a good scheduling heuristic for such an environment is a mape-k loop problem. So different heuristic approaches may be used for better result and in this result we paper we implement worst fit in mape-k and evaluated the results.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.