Variable threshold-based hierarchical load balancing technique in Grid

Rathore, Neeraj Kumar; Chana, Inderveer

doi:10.1007/s00366-014-0364-z

Cited by 35 publications

(17 citation statements)

References 21 publications

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“…Minimizing the makespan of a grid environment, the throughput of the environment increases. To minimize the total makespan of a grid, suitable job Grid tasks arrival rate ( G ) [5,20] Local tasks arrival rate ( L ) [1, 5] Processor service rate (µ) [5,10] Grid queue size (M G ) [5,20] Local queue size (M L ) [5,10] Number of processors (N) [1,4] Failure rate of an idle processor (γ i ) scheduling algorithms should be developed. A suitable job scheduling algorithm not only should consider the characteristics of an environment to be able to appropriately dispatch jobs to the resources, but also it should take into account the specification of jobs.…”

Section: Discussionmentioning

confidence: 99%

“…To achieve this, the load was divided into different categories, such as lightly loaded, under-lightly loaded, overloaded, and normally loaded. It is worthwhile to mention that the method proposed in [5] eliminates the scalability complexity of a site by a dynamic threshold value, while most of the existing load balancing techniques use a static threshold value which causes problems in a large-scale grid environment.…”

Section: Task Schedulingmentioning

confidence: 99%

“…After analyzing the previously proposed methods, a new approach has been devised in [33] which uses the hierarchical load balancing technique and fault tolerance with check-pointing-based job migration technique. The same authors have proposed a novel technique to tackle hierarchical load balancing in grid environments while maintaining the resource utilization and response time for dynamic and decentralized grids [5]. The proposed method was analyzed based on variable threshold value.…”

Section: Task Schedulingmentioning

confidence: 99%

“…In grids, resources are connected to a Grid Manager (GM) which receives grid jobs from grid users and dispatches them among the resources. The manager is responsible for sustaining the overall grid activities of scheduling and rescheduling [3][4][5]. Depending on the structure of the grid, GM can be considered as a single resource or even a distributed system composed of some resources [6,7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance aware scheduling considering resource availability in grid computing

Entezari‐Maleki

Bagheri

Mehri

et al. 2016

Engineering with Computers

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Section: Discussionmentioning

confidence: 99%

Section: Task Schedulingmentioning

confidence: 99%

Section: Task Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance aware scheduling considering resource availability in grid computing

Entezari‐Maleki

Bagheri

Mehri

et al. 2016

Engineering with Computers

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show abstract

“…GridSim [19][20][21][22] is used to simulate an SGC environment, which is defined as a framework for modelling and simulation of Grid computing infrastructures and services.…”

Section: Performance Evaluationmentioning

confidence: 99%

Job migration with fault tolerance based QoS scheduling using hash table functionality in social Grid computing

Rathore

Chana

2014

Journal of Intelligent &Amp; Fuzzy Systems

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In this paper, a fault tolerance based QoS (Quality of Services) scheduling using HTF (Hash Table Functionality) in Social Grid Computing (SGC) is introduced, where HTF is used as the underlying mechanism in SGC to logically manage the locations of the devices. Fault tolerance based QoS scheduling consists of four sub-scheduling algorithms: Unauthorized-user filtering, Grid service delivery, QoS provisioning, and Replication and load-balancing. Under the proposed scheduling, a device is used as a resource for providing Grid services, faults caused due to reason like user mobility are tolerated and user requirements for QoS are considered. Simulations include the scheduling by with and without HTF. The simulation results show that our proposed scheduling algorithm enhances Grid service execution time, finish time, reliability and reduces the Grid service error rate.

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Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Kashyap

Kumar

2022

Concurrency and Computation

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SUMMARY Load balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.

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Variable threshold-based hierarchical load balancing technique in Grid

Cited by 35 publications

References 21 publications

Performance aware scheduling considering resource availability in grid computing

Performance aware scheduling considering resource availability in grid computing

Job migration with fault tolerance based QoS scheduling using hash table functionality in social Grid computing

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

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