Quality and energy optimized scheduling technique for executing scientific workload in cloud computing environment

<p class="Abstract">Modern BigData data-intensive and scientific workload execution is challenging. The major issues are reliable processing, performance efficiency energy efficacy perquisite of processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their to accommodate users quality service (QoS) requirement, job performance, efficiency, resource accessibility. Extensive scheduling has been presented minimize consumption in cloud computing (CC) environment. However, the existing model induces higher amount interaction cost between inter-processors communications. Further, due poor utilization, routing inefficiency these fails meet QoS prerequisite. For overcoming research challenges, this paper optimized (QEOS) technique for executing by employing dynamic voltage frequency scaling (DVFS) technique. Experiment outcome shows QEOS attains good trade-off system multi-core when compared with model.</p>