Optimized efficient job scheduling resource (OEJSR) approach using cuckoo and grey wolf job optimization to enhance resource search in cloud environment

Abstract

AbstractCloud computing is a technique for developing systems that rely on dynamic sharing to enable the integration of several systems to provide services. For the purpose of completing their intended work, cloud users may literally access the cloud resources over the internet. The tasks that cloud users submit and the time it takes to do them determine the effectiveness and efficiency of cloud computing services. Task scheduling is essential to improving a cloud system’s efficacy and performance since it optimizes resource allocation and utilization. In this context, cloud computing offers a variety of advantages, including cost savings, security, mobility, flexibility, disaster recovery, quality control, automated software updates, and sustainability. Therefore, the requirement to control resource allocation has increased along with the number of cloud users. However, cloud task scheduling requires a quick and intelligent algorithm that can identify available resources and plan out tasks that different people desire. Therefore, a quick, effective work scheduling method is needed for improved resource allocation and scheduling. Grey Wolf Job Optimization (GWO) and Cuckoo Search Optimization (CSO) are used for the Optimized Efficient Job Scheduling Resource (OEJSR). The "grey wolf optimization" (GWO) ensemble with OEJSR has provided the best resource allocation models. The prior research was compared using computation time, make span, iteration-based performance, fitness, and success rate. Studies demonstrate the superiority of the suggested approach.

Keywords

• Cloud computing
• resource allocation
• scheduling
• cuckoo
• grey wolf
• Swadesh Kumar Singh, Gokaraju Rangaraju Institute of Engineering and Technology, India