Advanced Multi Objective Task Scheduling Using Pelican Optimization Algorithm and Sand Cat Swarm Optimization Algorithm

Abstract

Cloud computing (CC), which has picked up notoriety as a computing innovation, gives energetic and adaptable computing assets. Compelling assignment planning plays a pivotal part in CC since it optimizes the dispersion of errands among accessible assets for best execution. Allotment of computing assignments in a cloud environment may be a complex prepare influenced by a few variables such as accessible organize transmission capacity, length and taken a toll contemplation. In this manner, it is critical to optimize the accessible transfer speed to guarantee productive planning of errands in CC. This consider presents a novel swarm approach that combines Pelican Optimization Calculation (POA) and Sand Cat Swarm Optimization (SCSO) to optimize errand planning in a CC environment. The recently created strategy moreover employments a security strategy called Polymorphic Progressed Encryption Standard (P-AES) to scramble cloud information amid programming. The consider assesses the execution of the proposed calculation in terms of lopsidedness degree, length, asset utilization, fetched, normal holding up time, reaction time, throughput, inactivity, execution time, speed, and transfer speed utilization. The recreation is performed with a Python apparatus and can successfully handle numerous errands from 1000 to 5000. The proposed calculation gives a modern viewpoint on the utilize of swarming sub-algorithms in optimizing the planning of CC assignments. The integration of POA and SCSO empowers proficient errand planning of the proposed calculation by abusing the qualities of both calculations. The proposed approach gives an imaginative arrangement to assignment planning challenges in cloud situations and gives a more productive and secure way to optimize cloud administrations. Generally, this study provides valuable data on assignment planning optimization in CC and gives a viable approach to move forward the execution of CC administrations.