Efficient and Scalable ACO-Based Task Scheduling for Green Cloud Computing Environment

Ari, Ado Adamou Abba; Damakoa, Irépran; Titouna, Chafiq; Labraoui, Nabila; Guéroui, Abdelhak Mourad

doi:10.1109/smartcloud.2017.17

Cited by 19 publications

(7 citation statements)

References 21 publications

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“…Based on the recent characterization of virtualization environments, many proposals have been presented to address these challenges. Some services delivery control theories are presented in [6][7][8] but these do not consider a system's constraints; while, on the other hand, the studies presented in [9] and admission-control techniques involving static resource allocation reject a large proportion of requests in order to avoid overloading. Admission-control literature, as presented in [10] also does not guarantee system stability or robustness.…”

Section: Introductionmentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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Cloud computing is a paradigm that ensures the flexible, convenient and on-demand provisioning of a shared pool of configurable network and computing resources. Its services can be offered by either private or public infrastructures, depending on who owns the operational infrastructure. Much research has been conducted to improve a cloud’s resource provisioning techniques. Unfortunately, sometimes an abrupt increase in the demand for cloud services results in resource shortages affecting both providers and consumers. This uncertainty of resource demands by users can lead to catastrophic failures of cloud systems, thus reducing the number of accepted service requests. In this paper, we present Bouncer—a workload admission control scheme for cloud services. Bouncer works by ensuring that cloud services do not exceed the cloud infrastructure’s threshold capacity. By adopting an application-aware approach, we implemented Bouncer on software-defined network (SDN) infrastructure. Furthermore, we conduct an extensive study to evaluate our framework’s performance. Our evaluation shows that Bouncer significantly outperforms the conventional service admission control schemes, which are still state of the art.

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Section: Introductionmentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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“…The above results show that the use of the Genetic Algorithm in cloud computing tasks can reduce task completion time, reduce energy consumption, and improve resource utilization. Literature [6] used Ant Colony Optimization to handle the node load in the cloud in order to bring users a better user experience; Literature [7] used Ant Colony Optimization in the green cloud computing. The above results show that cloud computing effectively reduces the completion time, improves the efficiency of the node load, and reduces operating costs.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Jia

Shi

2021

Wireless Communications and Mobile Computing

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The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

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“…Moreover, the ant colony optimization (ACO) has become one of the most popular task scheduling methods [49][50][51][52][53]. Keshk et al [54] proposed a task scheduling algorithm based on the ACO technique in the cloud computing environment.…”

Section: Related Workmentioning

confidence: 99%

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

100

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In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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Efficient and Scalable ACO-Based Task Scheduling for Green Cloud Computing Environment

Cited by 19 publications

References 21 publications

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

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