An enhanced load balancing mechanism based on deadline control on GridSim

Hao, Yongsheng; Liu, Guanfeng; Wen, Ning

doi:10.1016/j.future.2011.10.010

Cited by 51 publications

(49 citation statements)

References 16 publications

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“…The ORT aims to optimize mean response time for non-real-time tasks, and the OMR is to achieve the optimized mean deadline-miss rate for soft realtime tasks. To evaluate the performance of various co-allocation policies, the researchers conduct extensive experiments in large-scale grid testbed DAS-2 [10][11][12]23]. Based on their experimental results, they draw an important conclusion that workload-aware co-allocation policies are more effective to reduce the mean response time and obtain better load-balance.…”

Section: Related Workmentioning

confidence: 99%

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Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

Liu¹,

Han²

2014

IJFGCN

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Section: Related Workmentioning

confidence: 99%

“…We use GridSim [23], a distributed resource management and scheduling simulator, to evaluate the performance of HPCM. A multicluster computing grid model is constructed, which consists of twelve computing sites (CE_1~CE_12).…”

Section: Experimental Settingsmentioning

confidence: 99%

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

Liu¹,

Han²

2014

IJFGCN

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“…The load-balancing mechanism attempts to improve the response time of the user's submitted applications by ensuring maximal utilization of available resources. The main goal of this type of algorithm is to prevent, if possible, the condition in which some processors are overloaded with a set of tasks while others are lightly loaded or even idle [7].…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the unique characteristics of the Grid computing environment such as heterogeneity, autonomy, scalability, adaptability, dynamic behaviour, application diversity, resource non-dedication, resource selection and computation-data separation. Thus, it is a challenging problem to design an efficient and effective load-balancing scheme for Grid environments which can integrate all the above said factors [7].…”

Section: Introductionmentioning

confidence: 99%

“…The method proposed by us is an improvement over the existing enhanced GridSim with deadline control (EGDC) [7]. Our proposed approach called "Enhanced GridSim with Load balancing based on Deadline Failure Recovery" (EGDFR) performs load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets.…”

Section: Introductionmentioning

confidence: 99%

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An improved load-balancing mechanism based on deadline failure recovery on GridSim

Patel

Tripathy

2015

Engineering with Computers

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failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed loadbalancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.

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A QoC‐based model for performance and QoE trade‐off in distributed systems

D'Amato

Dantas

2017

Concurrency and Computation

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Summary This paper proposes a quality of context (QoC)–based model to enhance trade‐off between system performance and users quality of experience (QoE) in distributed systems. The QoE considers the users subjective perceptions of quality on technology services and depends on context attributes related to the users expectations. In addition, context information can be used to improve performance in context‐aware systems. Therefore, the QoC‐based model proposed in this paper aims to establish performance and QoE trade‐off for resource allocation in distributed systems. With the massive data growth over the past years, the demand for computing capacity has been notably growing in a proportional way. In view of that, the demand for distributed systems has also increased significantly due to the advantages of distributed computing such as cost‐benefit gains, large processing capacity, and an extensive capability for resources sharing. In this sense, the large‐scale utilization of distributed systems has generated problems concerning resource allocation because of the heterogeneous characteristics and continuous evolution of distributed computational devices, resulting in several challenges in terms of environment performance and users' satisfaction trade‐off. The model conceived in this paper provides for an experimental evaluation considering performance and QoE trade‐off by means of simulations. Simulations were performed in order to study complex distributed systems scenarios. Experimental results showed that the proposed QoC model policy has an enhanced performance in comparison with classical approaches.

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An enhanced load balancing mechanism based on deadline control on GridSim

Cited by 51 publications

References 16 publications

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

An improved load-balancing mechanism based on deadline failure recovery on GridSim

A QoC‐based model for performance and QoE trade‐off in distributed systems

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