An Enhanced Task Scheduling in Cloud Computing Based on Hybrid Approach

Alworafi, Mokhtar A.; Dhari, Atyaf; El-Booz, Sheren A.; Nasr, Aida A.; Arpitha, Adela; Suresha, M

doi:10.1007/978-981-13-2514-4_2

Cited by 21 publications

(22 citation statements)

References 16 publications

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“…For instance, it used the speed of the ACO algorithm in finding good solutions swiftly and shared interaction of bees and sharing information by waggle dancing from Artificial Bee Colony (ABC). Simulation results showed a good improvement in makespan, execution time, and so on [38].…”

Section: Related Workmentioning

confidence: 88%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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Section: Related Workmentioning

confidence: 88%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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“…Also, LCFP may face starvation problem. Alworafi et al [16] introduced Hybrid-SJF-LJF (HSLJF) algorithm which combines LJF and SJF algorithms. HSLJF sorts the tasks in ascending order, then selects one task based on SJF and another task based on LSF.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, the performance of the proposed algorithm, GTSwDVG, is compared with four algorithms: SJF, which uses the shortest job first criterion, LCFP [9], which is based on the Longest Cloudlet Fastest Processing Element (LCFP), HSLJF [16], which combines shortest job first and longest job first criteria and CSSA [14], which uses one of the strategies of bee families called the social spider strategy.…”

Section: Simulationmentioning

confidence: 99%

“…In recent years, a lot of studies have been appeared to schedule tasks on VMs [9][10][11][12][13][14][15][16]. The main goal of these algorithms is searching for the solution that minimizes the execution cost and makespan by using intelligent approaches such as Genetic algorithm (GA), chaotic social spider algorithm (CSSA), ant colony optimization (ACO), fuzzy theory, Simulated Annealing (SA), particle swarm optimization (PSO) or hybrid algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

Ahmed¹,

Alzohairy²,

Raslan³

et al. 2021

IJC

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Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.

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“…To handle the starvation problem the researchers in [111] proposed a hybrid shortest-longest scheduling algorithm. They considered the capabilities of each VM and the length of the task to allocate the tasks to the most convenient VMs, so as to overcome the starvation problem and also satisfying and considering both the provider and user requirements.…”

Section: Schedulingmentioning

confidence: 99%

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Al-Arasi¹,

Saif²

2020

EAI Endorsed Transactions on Cloud Systems

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Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.

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An Enhanced Task Scheduling in Cloud Computing Based on Hybrid Approach

Cited by 21 publications

References 16 publications

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

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