Improving Expected Time Matrix based Queuing Theory for Cache Arrived Probabilistic Execution Model to Reduce Server Utilization for Customer Idle Time

A. Andrew Michael and M. Thiagarajan

Line of data arrival in queuing theory is a developmental approach for time constrains in server utilization. Due to his maximum utilization on data arrival at the movement which have an asset at random resources are unordered in queuing, and compute task must be driven at all times by inbound wait tasks. This leads to massive waste of energy. To share resources for different purposes need an efficient queuing theory approach based on idle response of server in customer utilization. In our paper, we use a cache point of repeat request to assign the task in queuing theory, to propose an efficient resource allocation technique over customer cache arrived probabilistic on a queuing (Max-A/S/Min-R/J/D):(Max-cache/Min/J/A) theory model. The proposed for cache arrived probabilistic Execution model (CAPEM) in queuing theory to improve the utilization of customer request on fast response rate of arrival request with support of Expected time matrix (ETM) this manage the tasks Symmetrically balanced load planning the tasks based on the cache server. The queuing task represent improving the of energy-saving vacation, computing model to reduce the time and make faster response to meet user needs, this proposed workflow-based system that allows you to improve the request utilization of Qos by working customer request with us at a minimum waiting time. It has been proposed using some of the effective respect for theory sorting to improve the customer process and the waiting time. The experimental results show that our model of global indexing indicates that the use rate increases, and reduces latency.

Volume 12 | Issue 5

Pages: 54-64

DOI: 10.5373/JARDCS/V12I5/20201689