Metaheuristic task scheduling algorithms for cloud computing environments

Abstract

Cloud computing has the advantage of providing flexibility, high-performance, pay-as-you-use, and on-demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP-complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE-SA and GA-SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.

A chaos opposition‐based dwarf mongoose approach for workflow scheduling in cloud

“…If the family forages relatively far in the scout group area, they will come find a suitable sleeping mound. Formula models a scout mongoose (19).…”

“…Reference 19 task‐scheduling issues in cloud environments are developed by means of hybrid metaheuristic algorithms. Authors have created greedy metaheuristic algorithms that are integrated with genetic approach (GA), differential evolution method (DE), and simulated annealing technique (SA).…”

Special issue on High Performance Computing Conference (BASARIM‐2020)

“…Tasks are diverse and resources are heterogeneous in this setting, making task scheduling a good example of NP‐complete problems. With this in mind, Aktan et al 5 propose new metaheuristic and hybrid metaheuristic algorithms to solve problems of task scheduling in cloud computing environments. These include metaheuristic algorithms based on a genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA), and these are furthermore combined with a greedy approach (GR).…”

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

“…13 Moreover, the majority of existing TS works fail to capture the potential features of CC including dynamism, elasticity, and heterogeneity in the utilization of computing resources which results in ignoring the needs of user QoS. 14 Thus, meta-heuristic-based optimization algorithms that can potentially handle huge search space are necessary for scheduling tasks in large-scale applications. Hence, further enhancement can be achieved by including hybrid meta-heuristic optimization algorithms during the process of determining TS solutions for obtaining subsequent improved solutions.…”

“…These limitations of meta‐heuristic algorithms have maximized the probability of resulting in sub‐optimal solutions that directly influence service provisioning performance with respect to the satisfaction of required QoS objectives 13 . Moreover, the majority of existing TS works fail to capture the potential features of CC including dynamism, elasticity, and heterogeneity in the utilization of computing resources which results in ignoring the needs of user QoS 14 . Thus, meta‐heuristic‐based optimization algorithms that can potentially handle huge search space are necessary for scheduling tasks in large‐scale applications.…”