Description

The distinctiveness and patterns of workloads in a Cloud computing atmosphere is important in order to progress resource management and operational circumstances as Quality of Service guarantees are maintained. Simulation models based on pragmatic parameters are also directly required for investigating the crash of this workload distinctiveness on novel system designs and operation policies. There is a need of analyses to sustain the progress of workload models that confine the intrinsic range of users and tasks, mostly suitable to the restricted ease of use of Cloud trace logs as well as the difficulty in analyzing such systems. In this document we nearby an extensive examination of the workload distinctiveness consequent from a production Cloud datacenter that features over 900 users submitting roughly 25 million tasks over a time period of a month. Our examination focuses on revealing and quantifying the variety of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload formed by such mechanism. Our derivative model is implemented by extending the capabilities of the CloudSim structure and is advance validated during experimental assessment and statistical suggestion tests.