A review energy-efficient task scheduling algorithms in cloud computing

Atiewi, Saleh; Yussof, Salman; Ezanee, Mohd; Almiani, Muder

doi:10.1109/lisat.2016.7494108

Cited by 18 publications

(7 citation statements)

References 14 publications

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Section: Resultsmentioning

confidence: 99%

“…Makespan and cost respectively refer to the total execution time needed for task implementation [64,65] and the cost of implementing tasks on particular VMs. Task length, data transfer cost, and storage are determinants of [34,65]. Cloud service providers benefit from expansion of resource utilization, so permitting customers to rent resources of restricted availability ensures full use of resources and hence profit maximization [34,66].…”

Section: Resultsmentioning

confidence: 99%

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A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Ababneh

2021

Mathematical Problems in Engineering

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In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Ababneh

2021

Mathematical Problems in Engineering

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“…• Most of the existing task schedulers doesn't consider the task's requirements, e.g., number of tasks and the task's priority, and some of them consider only the waiting time and response time reduction. [3] In this paper, we introduce a hybrid task scheduling algorithm that utilizes the shortest job first (SJF) and round-robin (RR) algorithms. The hybrid task scheduling algorithm comprises two major stages.…”

Section: Motivationmentioning

confidence: 99%

“…Job scheduling, one of the most popular improvement issues, plays a key role in improving flexible and reliable systems. The main purpose is to establish a schedule of posts on adaptable resources according to adaptable time, which includes the identification of an appropriate sequence in which functions can be implemented within the framework of transaction logic constraints [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

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“…To design a market that captures realistic behavior, constraints, and operational logic of DaCes, it is important to understand how scheduling decisions are made in daily DaCe operations. Various energy-aware, online scheduling models have been proposed for DaCes [21]. Eco-aware (sustainability driven) scheduling algorithms have also been proposed [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model

Zhang¹,

Roald²,

Zavala³

2022

Preprint

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Data centers (DaCes) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and price/load allocations) affect DaCe operations. An obstacle that arises here is the lack of computationally-tractable DaCe scheduling models that can capture objectives, constraints, and information flows that arise at the interface of DaCes and the grid. To address this gap, we present a receding-horizon scheduling model (a mixed-integer programming model) that captures the resource management layer between the DaCe scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DaCes can provide significant temporal load-shifting flexibility in response to carbon emission signals and peak demand charges from electricity markets without sacrificing much scheduling performance. Models and case studies are shared as easy-to-use Julia code.

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A review energy-efficient task scheduling algorithms in cloud computing

Cited by 18 publications

References 14 publications

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model

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