Integer particle swarm optimization based task scheduling for device-edge-cloud cooperative computing to improve SLA satisfaction

Abstract

Task scheduling helps to improve the resource efficiency and the user satisfaction for Device-Edge-Cloud Cooperative Computing (DE3C), by properly mapping requested tasks to hybrid device-edge-cloud resources. In this paper, we focused on the task scheduling problem for optimizing the Service-Level Agreement (SLA) satisfaction and the resource efficiency in DE3C environments. Existing works only focused on one or two of three sub-problems (offloading decision, task assignment and task ordering), leading to a sub-optimal solution. To address this issue, we first formulated the problem as a binary nonlinear programming, and proposed an integer particle swarm optimization method (IPSO) to solve the problem in a reasonable time. With integer coding of task assignment to computing cores, our proposed method exploited IPSO to jointly solve the problems of offloading decision and task assignment, and integrated earliest deadline first scheme into the IPSO to solve the task ordering problem for each core. Extensive experimental results showed that our method achieved upto 953% and 964% better performance than that of several classical and state-of-the-art task scheduling methods in SLA satisfaction and resource efficiency, respectively.

A Hybrid metaheuristic Algorithm for Edge Site Deployment with User Coverage Maximization and Cost Minimization

“…Objective ( 9) is minimizing the number of selected candidate places for deploying edge sites. Constraints (11) require that every user must be covered by at least one selected candidate place or deployed edge site. Constraints (11) represent that decision variables are binary.…”

“…Constraints (11) require that every user must be covered by at least one selected candidate place or deployed edge site. Constraints (11) represent that decision variables are binary. Therefore, the ESDP instance is binary linear programming which has been proven as NP-complete [15].…”

“…While, due to the distribution and heterogeneity of limited edge resources, it is a very challenging work to provide high quality services for all users. Many works have focused on addressing the challenge in various aspects including edge server placement [6], [7], edge caching [8], [9], task offloading [10], [11] and so on. While, all of works assume that edge sites have been deployed, where edge resources are placed and used for processing requests.…”

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

“…In simulated EECC systems, where the simulation parameters are set referring to [18], [21], [23], [15] and reality, there are ten devices, five ES, and ten types of CS. The core number of devices, ES, and CS are set randomly in ranges of [2,8], [4,32], and [1,8], respectively.…”

“…• Particle Swarm Optimization (PSO) [18] uses the idea of particle movement. PSO initializes a population with multiple particle (individual), and iteratively moves each particle toward its personal best position and the global best position for the particle position updates (the population evolution).…”