Task scheduling techniques in cloud computing: A literature survey

Arunarani, A. R.; Manjula, D.; Sugumaran, Vijayan

doi:10.1016/j.future.2018.09.014

Cited by 360 publications

(168 citation statements)

References 37 publications

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“…Moreover, the total load on the VM ( ) is calculated based on the total tasks that are scheduled on a particular VM which is described as summation of load for all the tasks that are scheduled during the specified time. This is determined as given by the Equation (5).…”

Section: Load Balance Constraint Modelmentioning

confidence: 99%

“…Task scheduling process in cloud computing needs to be carefully planned in order to improve the efficiency of the whole cloud computing system [5]. If the user submitted tasks are not scheduled properly to the proper Virtual Machines (VMs), performance reduces in terms of cloud provider's profit which makes system not able to meet the client's requirements [6].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, various types of scheduling algorithms were presented by various researchers. This includes static scheduling, dynamic scheduling, heuristic scheduling and various types of hybrid algorithms [5]. Most of the research works reveals that the task scheduling problem in cloud computing is a type of NP-complete where heuristic algorithms are more suitable, due to its dynamic and heterogeneous nature [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

M¹,

S²

2019

IJITEE

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The efficiency of the cloud-based systems is greatly relying on the task scheduling algorithm which affects the performance parameters such as makespan, response time, degree of imbalance and cost. In recent years, the energy efficiency is also considered as another challenging issue which affects the efficiency of cloud computing systems. This paper proposes a Hybrid Starling Social Spider Algorithm (Starling-SSA) for Energy and Load Aware Task Scheduling in cloud computing. The Starling-SSA is designed as a hybrid algorithm inspired by the intelligent behavior of social spider and the collective response behavior of starling birds. The foraging behavior of spider is implemented to identify the best VMs for the given task with minimum makespan and degree of imbalance. In addition to this, the distance factor is incorporated inspired by starling flock distance in order identify the closeness of VM pairs and avoids the VMs that are far away, thereby VMs can be limited during the searching process. This will greatly reduce energy consumption by taking only VMs that are belongs to the distance factor. The performance metrics such as makespan, degree of imbalance and energy efficiency are evaluated against the existing algorithms such as EATS, CBAT and HC-ACO. The results presents a significant improvements when comparing to the existing algorithms

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Section: Load Balance Constraint Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

M¹,

S²

2019

IJITEE

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“…Job scheduling mechanisms try to allocate cloud resources to users' jobs in an optimal way [3,[9][10][11][12]. Cloud resources execute the submitted jobs, and the output is sent back to cloud users [4,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Chemical reaction optimization (CRO) for cloud job scheduling

Zain

Yousif

2019

SN Appl. Sci.

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Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users' submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules' interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy ( PE ) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms.

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“…In fact, various metaheuristics as well as their variations have been used to solve scheduling problems in many fields [14], [15], [16], [17], [18], [19], [20], which also include the cloud computing. As summarized by the latest survey [21], currently metahuristics used in cloud task scheduling mainly include the genetic algorithm (GA) [22] and swarm intelligence algorithms, such as the ant colony optimization (ACO) [23] and the particle swarm optimization (PSO) [24]. These optimization algorithms are derived from the simulations of biological population evolutions, and they can solve complex global optimization problems through cooperation and competition among individuals [25].…”

mentioning

confidence: 99%

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

200

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Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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Task scheduling techniques in cloud computing: A literature survey

Cited by 360 publications

References 37 publications

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

Chemical reaction optimization (CRO) for cloud job scheduling

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

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