Proactive framework for energy efficient job scheduling in cloud computing

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 Datacenter (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 an Mape-k loop problem. So different heuristic approaches may be used for better result and in this work we implement worst fit in Mape-k and evaluated the results.