User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing

Suresh, P.; Balasubramanie, P.

doi:10.1155/2013/439362

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…There are several published articles addressing system response time as the main factor inherent to users' satisfaction in distributed environments for HPC applications . In the literature, there are other studies that define users' satisfaction through techniques based on utility function .…”

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

“…In our proposed model, both utility function and makespan are considered to determine the resulting QoE. In a distributed environment, it is desired that resources be provided according to the application demand . This relationship is determined by the utility function, which assigns a value according to the combination of available resources, taking as a reference the requested amount of resources.…”

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

“…There are several published articles addressing system response time as the main factor inherent to users' satisfaction in distributed environments for HPC applications. [24][25][26][27][28][29][30][31] In the literature, there are other studies that define users' satisfaction through techniques based on utility function. [32][33][34] In addition, there are papers that adopt utility function as an aspect directly linked to QoE.…”

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

“…In a distributed environment, it is desired that resources be provided according to the application demand. 31 This relationship is determined by the utility function, which assigns a value according to the combination of available resources, taking as a reference the requested amount of resources. The correctness parameter is used as a benchmark to measure the proportion of fulfilled resources in relation to the requested value, considering the specified job category.…”

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

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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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Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

Section: The Proposed Quality Of Experience Metricmentioning

confidence: 99%

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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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“…A work based on user satisfaction and hierarchical load balancing is proposed [27] that considers user demands and load balancing. It minimizes the response time of the jobs and improves the utilization of the resources in grid environment.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

Keerthika¹,

Suresh²

2015

The Scientific World Journal

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Grid environment consists of millions of dynamic and heterogeneous resources. A grid environment which deals with computing resources is computational grid and is meant for applications that involve larger computations. A scheduling algorithm is said to be efficient if and only if it performs better resource allocation even in case of resource failure. Allocation of resources is a tedious issue since it has to consider several requirements such as system load, processing cost and time, user's deadline, and resource failure. This work attempts to design a resource allocation algorithm which is budget constrained and also targets load balancing, fault tolerance, and user satisfaction by considering the above requirements. The proposed Multiconstrained Load Balancing Fault Tolerant algorithm (MLFT) reduces the schedule makespan, schedule cost, and task failure rate and improves resource utilization. The proposed MLFT algorithm is evaluated using Gridsim toolkit and the results are compared with the recent algorithms which separately concentrate on all these factors. The comparison results ensure that the proposed algorithm works better than its counterparts.

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Variable threshold-based hierarchical load balancing technique in Grid

Rathore

Chana

2014

Engineering with Computers

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User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing

Cited by 9 publications

References 13 publications

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

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

A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

Variable threshold-based hierarchical load balancing technique in Grid

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