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Throughout the numerous processing in cloud computing, an essential concern is to scheduling jobs in parallel to cloud data centers. An aim of this paper is to design a Fuzzy logic system. Gang forming with scheduling policy improves the performance of computation cost and time of different cloud workloads. In this work a novel framework for cloud workload management is modelled. In which clustering of cloud workload is done based on Manhattan distance based fuzzy clustering and scheduling of workload is done based on Compress & Join Gang Polling Evaluation scheduling algorithm (C&JGPESA). In order to provide effective utilization of resources, Gang scheduling algorithm is used based on their performance to fit the same number of applications in less time slots. Finally proposed work comparison is compared with computation cost, make span and response time and it achieves around 60%, 97% and 80% greater performance than both existing works.

Fuzzy Resource Pre-processing and Compress and Join Gang Polling Evaluation Scheduling in Cloud Computing