A dynamic load balancing approach using genetic algorithm in distributed systems

Dynamic load balancing(DLB) for parallel systems has been studied extensively; however, DLB for distributed systems is relatively new. To efficiently utilize computing resources provided by distributed systems, an underlying DLB scheme must address both heterogeneous and dynamic features of distributed systems. In this paper, we propose a DLB scheme for Structured Adaptive Mesh Refinement(SAMR) applications on distributed systems. While the proposed scheme can take into consideration (1) the heterogeneity of processors and (2) the heterogeneity and dynamic load of the networks, the focus of this paper is on the latter. The load-balancing processes are divided into two phases: global load balancing and local load balancing. We also provide a heuristic method to evaluate the computational gain and redistribution cost for global redistribution. Experiments show that by using our distributed DLB scheme, the execution time can be reduced by 9%- to using parallel DLB scheme which does not consider the heterogeneous and dynamic features of distributed systems.

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Dynamic Load Balancing in Distributed Systems in the Presence of Delays: A Regeneration-Theory Approach

IEEE Transactions on Parallel and Distributed Systems ◽

10.1109/tpds.2007.1009 ◽

2007 ◽

Vol 18 (4) ◽

pp. 485-497 ◽

Cited By ~ 61

Author(s):

Sagar Dhakal ◽

Majeed M. Hayat ◽

Jorge E. Pezoa ◽

Cundong Yang ◽

David A. Bader

Keyword(s):

Distributed Systems ◽

Load Balancing ◽

Dynamic Load ◽

Dynamic Load Balancing ◽

Theory Approach

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A Hybrid Dynamic Load Balancing Algorithm for Distributed Systems Using Genetic Algorithms

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2014070101 ◽

2014 ◽

Vol 5 (3) ◽

pp. 1-23 ◽

Cited By ~ 1

Author(s):

Mayuri A. Mehta ◽

Devesh C. Jinwala

Keyword(s):

Genetic Algorithms ◽

Distributed Systems ◽

Load Balancing ◽

Dynamic Load ◽

Hybrid Algorithm ◽

Sparse Matrix ◽

Dynamic Load Balancing ◽

Data Set ◽

Wide Range ◽

System States

Dynamic Load Balancing (DLB) is sine qua non in modern distributed systems to ensure the efficient utilization of computing resources therein. This paper proposes a novel framework for hybrid dynamic load balancing. Its framework uses a Genetic Algorithms (GA) based supernode selection approach within. The GA-based approach is useful in choosing optimally loaded nodes as the supernodes directly from data set, thereby essentially improving the speed of load balancing process. Applying the proposed GA-based approach, this work analyzes the performance of hybrid DLB algorithm under different system states such as lightly loaded, moderately loaded, and highly loaded. The performance is measured with respect to three parameters: average response time, average round trip time, and average completion time of the users. Further, it also evaluates the performance of hybrid algorithm utilizing OnLine Transaction Processing (OLTP) benchmark and Sparse Matrix Vector Multiplication (SPMV) benchmark applications to analyze its adaptability to I/O-intensive, memory-intensive, or/and CPU-intensive applications. The experimental results show that the hybrid algorithm significantly improves the performance under different system states and under a wide range of workloads compared to traditional decentralized algorithm.

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