Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization. (5th July 2019)

• Record Type: Journal Article
• Authors: Hasan, Mohammed Zaki; Al‐Rizzo, Hussain
• Is Part Of: Concurrency and computation. Volume 32:Number 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-05
• Subjects: cloud computing -- Internet of Things -- quality of services -- robust optimization -- task scheduling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5442
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.