RCOA Scheduler: Rider Cuckoo Optimization Algorithm for Task Scheduling in Cloud Computing

Abstract

Cloud computing, a term which has become very popular on the internet platform which has evolved over time and gained more attention due to various features that are being introduced like multiplex numerous users on the identical physical structure, computing of resource on request and many more. Cloud computing is considered to be an enormous pool having essential resources which the users can make use of to complete their task through the internet. Therefore, in order to complete the tasks requested by the users, a scheduling algorithm is required making scheduling a vital part of cloud computing. Though numerous solutions with respect to scheduling techniques have been proposed in in recent literature, researcher's continuously carryout performance upgradation by considering a mixture of QoS parameters thereby enhancing the cloud performance. In this regard, we had introduced an optimal and ideal task scheduling algorithm and challenge the other prevailing algorithms in terms of QoS parameters like makespan and energy. The proposed algorithm increases the performance of the scheduling in cloud by making use of an enhanced meta-heuristic hybrid algorithm called Rider Cuckoo Optimization Algorithm (RCOA), which is an advanced version of the already existing Rider Optimization Algorithm. The proposed RCOA technique had produced an improvement of 5.6%, 4.36% and 2.27% for makespan and had reduced the energy to the tune of 16.8%, 19.18%, and 16.15% when compared with Particle Swarm Optimization (PSO), Cuckoo Search (CS), Rider Optimization Algorithm (ROA) algorithms respectively when used with 25 VMs. Also the proposed approach attains the objective of optimal scheduling swiftly and achieves convergence in a small duration of time.

Dynamic Cost-Optimized Resources Management and Task Scheduling with Deadline Constraint for Mobile Crowd Sensing Environment

“…simulation, a widely used simulation technique. In the GOCJ dataset, tasks length lies between range from 15x10 3 -900 x10 3 MIs, then, dataset is categorized as: jobs with small size (15 x10 3 -55 x10 3 MIs), medium size jobs (59 x10 3 -99 x10 3 MIs), large size jobs (101 x10 3 -135 x10 3 MIs), extra size jobs (150 x10 3 -337.5 x10 3 MIs), and hugesize jobs(525x10 3 -900x10 3 MIs) as shown in Fig. 7 [16,30], and available on: https://data.mendeley.com/datasets/b7bp6xhrcd/1…”

“…The scheduling process is simple when there are few user tasks and resources. The scheduling becomes complicated if a user sends out several requests to get the appropriate level of service [2,3].…”