The Anatomy Study of Load Balancing in Distributed System

Yan, Kuo-Qin; Wang, S.c.; Chang, Cheng-Shang; Tseng, L. Y.

doi:10.1109/pdcat.2006.108

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…It compares its load with that of its locality domain nodes and selects the nearest overloaded processor [17,25]. Despite the fact that this approach tries to overcome the problem of long chains at the overloaded processor, it may fail to guarantee global load balancing.…”

Section: Processor Selectionmentioning

confidence: 99%

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A Dynamic Load Balancing Framework for Real-time Applications in Message Passing Systems

Kabbany

Wanas²,

Hegazi

et al. 2010

Int J Parallel Prog

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Load balancing algorithms are designed essentially to equally distribute the load on processors and maximize their utilities while minimizing the total task execution time. In order to achieve these goals, the load-balancing mechanism should be "fair" in distributing the load across the different processors. This implies that the difference between the heaviest-loaded and the lightest-loaded processors should be minimized. Therefore, the load information on each processor must be updated such that the load-balancing mechanism can be more effective. In this work, we present an application independent dynamic algorithm for scheduling tasks and loadbalancing in message passing systems. We propose a DAG-based Dynamic Load Balancing algorithm for Real time applications (DAG-DLBR) that is designed to work dynamically to cope with possible changes in the load that might occur during runtime. This algorithm addresses the challenge of devising a load balancing scheme which judicially deals with the hybrid execution of existing real-time application (represented by a Direct Acyclic Graph (DAG)) together with newly arriving jobs. The main objective of this algorithm is to reduce response times of the newly arriving jobs while maintaining the time constrains of the existing DAG. To evaluate the performance of the DAG-DLBR algorithm, a comparison with the performance of two common dynamic load balancing algorithms is presented. This comparison is performed by evaluating, experimentally, the execution time of different load balancing algorithms on a homogenous real parallel machine. In addition, the values of load imbalance, the execution time, and the communication overhead time are evaluated analytically using different benchmarks as test-bed workloads. These workloads cover a wide range of dynamic applications with different task types. Experimental results illustrate the improved performance of the DAG-DLBR algorithm compared to both distributed and hierarchal based algorithms by at least 12 and 19%, respectively. This improvement is true for all workloads, even with highly dependent workload. The DAG-DLBR algorithm achieves lower computation time than its corresponding values of both the distributed and the hierarchical-based algorithms for 4, 8, 12 and 16 processors.

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Section: Processor Selectionmentioning

confidence: 99%

“…Although this approach uses very short control messages among processors scheduler bottlenecks may occur in large systems. Moreover, if the scheduler fails load balancing is no longer possible [10,17]. Hendrickson and Devine [18] summarized some of the applications suitable for this approach.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Load Balancing Framework for Real-time Applications in Message Passing Systems

Kabbany

Wanas²,

Hegazi

et al. 2010

Int J Parallel Prog

View full text Add to dashboard Cite

show abstract

“…This study proposed a hybrid load balancing policy, which selects effective node sets in the stage of static load balancing to lower the odds of selecting ineffective nodes and makes use of the stage of dynamic load balancing. When a node status changes, a new substitute can be located in the shortest time to maintain the execution performance of the system [1] It has four components : 1. Transfer policy that determines whether a node is in a suitable state to participate in a task transfer 2.…”

Section: Iiiclassification Of Load Balancing Algorithmsmentioning

confidence: 99%

Multi-agent Optimized Load Balancing Using Spanning Tree for Mobile Services

Sinha¹,

Dhore²

2010

IJCA

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The Anatomy Study of Load Balancing in Distributed System

Cited by 2 publications

References 4 publications

A Dynamic Load Balancing Framework for Real-time Applications in Message Passing Systems

A Dynamic Load Balancing Framework for Real-time Applications in Message Passing Systems

Multi-agent Optimized Load Balancing Using Spanning Tree for Mobile Services

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