A Dynamic Feedback-based Load Balancing Methodology

Full Text (PDF, 697KB), PP.57-65

Views: 0 Downloads: 0


Xin Zhang 1,* Jinli LI 1 Xin FENG 1

1. Changchun University of Science and Technology

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2017.12.07

Received: 8 Oct. 2017 / Revised: 23 Oct. 2017 / Accepted: 8 Nov. 2017 / Published: 8 Dec. 2017

Index Terms

Load balancing, dynamic feedback, server cluster, distributed computing


With the recent growth of Internet-based application services, the concurrent accessing requests arriving at the particular servers offering application services are growing significantly. It is one of the critical strategies that employing load balancing to cope with the massive concurrent accessing requests and improve the access performance is. To build up a better online service to users, load balancing solutions achieve to deal with the massive incoming concurrent requests in parallel through assigning and scheduling the work executed by the members within one server cluster. In this paper, we propose a dynamic feedback-based load balancing methodology. The method analyzes the real-time load and response status of each single cluster member through periodically collecting its work condition information to evaluate the current load pressure by comparing the learned load balancing performance with the preset threshold. In this way, since the load arriving at the cluster could be distributed dynamically with the optimized manner, the load balancing performance could thus be maintained so that the service throughput capacity would correspondingly be improved and the response delay to service requests would be reduced. The proposed result is contributed to strengthening the concurrent access capacity of server clusters. According to the experiment report, the overall performance of server system employing the proposed solution is better.

Cite This Paper

Xin ZHANG, Jinli LI, Xin FENG, "A Dynamic Feedback-based Load Balancing Methodology", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.12, pp. 57-65, 2017. DOI:10.5815/ijmecs.2017.12.07


[1]Al-Anbagi I, Erol-Kantarci M, Mouftah H T. A low latency data transmission scheme for smart grid condition monitoring applications[C]// Electrical Power and Energy Conference. IEEE, 2013:20-25.
[2]Zhong C, Cai D, Yang F. Divisible loads scheduling using concurrent sending data on multi-core cluster[J]. Journal of Computer Research & Development, 2014, 51(6):1281-1294.
[3]Wang S C, Yan K Q, Liao W P, et al. Towards a Load Balancing in a three-level cloud computing network[C]// IEEE International Conference on Computer Science and Information Technology. IEEE, 2010:108-113.
[4]Bhogal K S, Lewis P D, Okunseinde F O, et al. Computer data communications in a high speed, low latency data communications environment[J]. 2015.
[6]Stoltzfus A, O’Meara B, Whitacre J, et al. Sharing and re-use of phylogenetic trees (and associated data) to facilitate synthesis[J]. Bmc Research Notes, 2012, 5(1):1-15.
[7]Leber A W, Knez A, Von Z F, et al. Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound.[J]. Journal of the American College of Cardiology, 2005, 46(1):147.
[8]Cardellini V, Colajanni M, Yu P S. Dynamic Load Balancing on Web-Server Systems[J]. Internet Computing IEEE, 1999, 3(3):28-39.
[9]Andrews J, Singh S, Ye Q, et al. An Overview of Load Balancing in HetNets: Old Myths and Open Problems[J]. IEEE Wireless Communications, 2013, 21(2):18-25.
[10]Peng X. Research and Realization of Task Scheduling in Coupling Distributed System[J]. Computer Technology & Development, 2013.
[11]Bao-Tong D U, Bing L I, Yang R. Concurrent service composition approach based on QoS fuzzy dominance[J]. Computer Engineering & Design, 2015.
[12]Debankur M, Borst S C, Van L J S H, et al. Universality of load balancing schemes on the diffusion scale[J]. Journal of Applied Probability, 2016, 53(4):1111-1124.
[13]Kai H, Shen H. Locality-Preserving Clustering and Discovery of Resources in Wide-Area Distributed Computational Grids[J]. IEEE Transactions on Computers, 2012, 61(4):458-473.
[14]Zhang N, Yan Y, Xu S, et al. A distributed data storage and processing framework for next-generation residential distribution systems[J]. Electric Power Systems Research, 2014, 116(11):174-181.
[15]Yang W C, Huang W T. A load transfer scheme of radial distribution feeders considering distributed generation[C]// Cybernetics and Intelligent Systems. IEEE, 2010:243-248.