Scheduling and Optimization of Resources in Edge Computing Using Random Allocation and Max Fit Allocation

The cloud computing is the new technique used most today for computation and storage. Edge computing is also a new computing domain that gives computation of tasks and storage of data nearer to the data sources. Various applications of edge computing are IOT, healthcare, retail, manufacturing etc. Virtualization technologies have enabled cloud platforms to provide various services such as virtual machines to the users. Task scheduling is the major issues faced in both cloud and edge domains. Since the usage of machines is high, it is difficult to assign tasks manually. So, an efficient and optimized algorithm is required in the cloud as well as the edge environment. Here, a task scheduling is implemented based on Heuristic Optimization Technique to examine the performance measures using cloudsim. Task scheduling and optimization techniques dynamically allocate resources in the cloud environment. Similarly, in edge computing, scheduling the task is performed with different optimization techniques such as random fit, and max fit algorithms. These task scheduling methods are allocated to the VM based on the resources that are available at the VM. These algorithms are implemented, and it increased the VM utilization by 4.82%. The processing time taken is reduced by 2.67% and the percentage of failed tasks is reduced by 3.68%. The experiment result shows the importance of the scheduling in the cloud and edge environment, in terms of processing time, failed task and increase in VM utilization in edge devices.