Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review

Ghafari, R.; Kabutarkhani, F. Hassani; Mansouri, N.

doi:10.1007/s10586-021-03512-z

Cited by 51 publications

(24 citation statements)

References 226 publications

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“…In their paper, (Ghafari, et al, 2022) offered a comparative analysis of 67 scheduling methods in the cloud system to minimize energy consumption in cloud computing environment during task scheduling. The main idea of this study is to allow concerned people to decide the best scheduling algorithm that optimizes energy properly.…”

Section: Energy-efficient and Power-aware Based Algorithmsmentioning

confidence: 99%

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

2022

IKM

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Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms.

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Section: Energy-efficient and Power-aware Based Algorithmsmentioning

confidence: 99%

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

2022

IKM

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“…With task scheduling, the sequence of execution of tasks can be obtained, with the recent focus being on energy conservation to minimize cost and other QoS parameters. Ghafari et al [ 6 ] have discussed various task-scheduling algorithms by categorizing them into three categories. A multi-objective load-balancing algorithm combining the features of Firefly and PSO [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Singhal

Athithan

Alomar

et al. 2023

Sensors

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Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Examining the developments in scheduling algorithms research: A bibliometric approach

Omotehinwa¹

2022

Heliyon

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Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review

Cited by 51 publications

References 226 publications

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

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

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Examining the developments in scheduling algorithms research: A bibliometric approach

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