Hybrid scheduling algorithms in cloud computing: a review

Abstract

<p><span>Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms.</span></p>

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

“…Among the promising techniques, which are also used to solve other NP-Hard problems are: heuristics, metaheuristics, greedy, and genetic approaches. These techniques are also applied to task scheduling in parallel and distributed systems (Thaman & Singh, 2016), (Arora & Banyal, 2022) (Amini Motlagh, et al, 2020) have expounded a systematic literature review (SLR)-based analysis on the task scheduling approaches. The authors classified task scheduling approaches into three categories, namely: (a) single cloud environments that evaluate cost-aware, energy-aware, multi-objective, and QoS-aware approaches in task scheduling; (b) multicloud environment that evaluates cost-aware, multi-objective, and QoS-aware task scheduling; and (c) mobile cloud environment that is energy-aware and QoS-aware task scheduling.…”

Examining the developments in scheduling algorithms research: A bibliometric approach

“…They have largely taken a narrow path that either focus on specific topics or area of applications of scheduling algorithms. These studies ( Almansour and Allah, 2019 ; Arora and Banyal, 2022 ; Arunarani et al., 2019 ; Ghafari et al., 2022 ; Kumar et al., 2019 ; Sana and Li, 2021 ) focused on the extensive review of scheduling techniques in cloud computing ( Agrawal et al., 2021 ; Davis and Burns, 2011 ; Olofintuyi et al., 2020 ), focused on the review of operating system scheduling algorithms while other studies such as ( Maipan-Uku et al., 2017 ; Prajapati and Shah, 2014 ; Yousif et al., 2015 ) focused on scheduling in grid computing. A general overview of scheduling algorithms is necessary at this time to understand the trends over time and to discover a new research focus.…”