Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Abstract

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure. User requests are submitted through an interface to these servers that provide service to them in an on-demand basis. The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category. Task scheduling in cloud poses numerous challenges impacting the cloud performance. If not handled properly, user satisfaction becomes questionable. More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment. The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution. An improvised seagull optimization algorithm which combines the features of the Cuckoo search (CS) and seagull optimization algorithm (SOA) had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment. The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment. Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization (MO-ACO), ACO and Min-Min algorithms. The proposed SOA-CS technique had produced an improvement of 1.06%, 4.2%, and 2.4% for makespan and had reduced the overall cost to the extent of 1.74%, 3.93% and 2.77% when compared with PSO, ACO, IDEA algorithms respectively when 300 vms are considered. The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

“…Although the disadvantages of the cat swarm algorithm like the requirement of more iterations to achieve effective optimization still exist, these studies also guide future research. In addition, particle swarm algorithm [8][9][10], seagull optimization algorithm [11], and other emerging intelligent algorithms [12][13][14][15], have been proposed to optimize the cloud-computing resource scheduling. Among these algorithms, PSO may be more mature and have more application, while it is easy to fall into local optimum.…”

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

“…As a very effective means to solve optimization problems, intelligent optimization algorithm has been widely concerned by researchers. At present, many advanced intelligent optimization algorithms have been produced, and scholars have tried to apply them to their own research fields and made some beneficial explorations, such as: brain storm optimization [21], differential evolution algorithm [22], biogeography algorithm [23], multi-verse optimizer [24], grey wolf optimizer [25] and Seagull optimization algorithm [26], etc. At the same time, it should be noted that most of the current intelligent algorithms were first proposed for continuous optimization problems, which need to be discretized before they can be applied to discrete optimization problems such as satellite mission planning.…”