Data center task scheduling through Biogeography-Based Optimization model with the aim of reducing makespan

Abbasi-Tadi, Ali; Khayyambashi, Mohammad Reza; Khosravi-Farsani, Hadi

doi:10.1109/iccke.2016.7802113

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…These models are useful in deployments that are not too complicated, have a high level of efficiency, and include larger task datasets. Other optimization models include those developed based on biogeography by the authors, Abbasi-Tadi et al (2016), dynamic voltage and frequency scaling-based bag-of-tasks scheduling in paperwork by Zhang et al (2015), energy-aware task scheduling employing multiple numbers of clouds by Mishra et al (2020), and scheduling of map and reduce applications for SLA aware load balancing by Zeng et al (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Ts2lbdp

Shelke

Shahapurkar²

2022

International Journal of Intelligent Information Technologies

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Few scheduling, models focus on service-level agreement (SLA) enforcement, which limits their real-time applicability. Thus, this research work proposes the design of an improved task-side service level (TS2L) agreement model for efficient task-scheduling using elephant herd optimization (EHO) with deadline (BDP) awareness. TS2LBDP incorporates pattern analysis using ensemble hierarchical, k-means, and fuzzy C means (FCM) clustering methods. A combination of these modules assists in improving task diversity and scheduling efficiency. SLA-based distribution enhances scheduling fairness and reduces mean waiting time for different clients. The proposed model was tested on parallel workload archive wherein different cloud workload logs and their respective VM configurations are described. It was observed that the proposed model improves scheduling efficiency by 20%, task diversity by 45%, and deadline hit ratio by 1%. It is scalable and can be used for several types of cloud deployments.

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Section: Literature Reviewmentioning

confidence: 99%

Ts2lbdp

Shelke

Shahapurkar²

2022

International Journal of Intelligent Information Technologies

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“…Several researchers have developed algorithms to solve the cloudlets scheduling issue [4][5][6][7][8]. However, most of the existing algorithms consider schedule length, and disregard several constraints that may affect the scheduling process like memory and processing load constraints.…”

Section: I In Nt Tr Ro Od Du Uc Ct Ti Io On Nmentioning

confidence: 99%

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Nasr

El‐Bahnasawy

Attiya

et al. 2019

Menoufia Journal of Electronic Engineering Research

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A A P Pr ro op po os se ed d M Me et ta a--H He eu ur ri is st ti ic c A Ap pp pr ro oa ac ch h f fo or r C Cl lo ou ud dl le et ts s S Sc ch he ed du ul li in ng g i in n C Cl lo ou ud d C Co om mp pu ut ti in ng g E En nv vi ir ro on nm me en nt t A Ab bs st tr ra ac ct tThis paper presents a new hybrid approach, called ACOSA, for cloudlets scheduling to enhance the scheduler behavior in Cloud computing (CC) environment and to overcome the results oscillation problem of the existing meta-heuristic scheduling algorithms. The proposed approach combines both the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithm to improve both quality of solutions and time complexity of the scheduling algorithm. The proposed approach is evaluated by using the well-known CloudSim, and the results are compared with the ant colony and simulated annealing separately in terms of schedule length, load balancing, and time complexity. It decreases the schedule length by 29.75% with SA and 12.25% with ACO. The ACOSA provides higher load balancing degree. It improves the balancing degree ratio by 36.36% than SA and 12.13% than ACO algorithms.

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OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

2020

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SummaryWith the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta‐heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta‐)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well‐known (non‐)evolutionary approaches, such as round‐robin, max‐min, MGGS, and TBSLB‐PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan.

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Data center task scheduling through Biogeography-Based Optimization model with the aim of reducing makespan

Cited by 4 publications

References 17 publications

Ts2lbdp

Ts2lbdp

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

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