HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

• Published in: Software, practice & experience Vol. 50; no. 12; pp. 2231 - 2251
• Main Authors: Bhardwaj, Amit Kumar, Gajpal, Yuvraj, Surti, Chirag, Gill, Sukhpal Singh
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2020; Wiley Subscription Services, Inc
• Subjects: Algorithms; Cloud computing; Completion time; Heuristic; Heuristic task scheduling; heuristics; Integer programming; Linear programming; Mathematical programming; metaheuristic; Mixed integer; Operating costs; Optimization; Precedence constraints; Programming languages; resource optimization; Resource utilization; Scheduling
• ISSN: 0038-0644; 1097-024X; 1097-024X
• DOI: 10.1002/spe.2890

Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.