Total views : 497

A Dynamic Load Balancing Algorithm for Computational Grid using Ant Colony Optimization


  • Department of Computer Systems and Networks, Faculty of Informatics Engineering,Tishreen University, Lattakia, Syrian Arab Republic


Objective: To design and implement an algorithm for load balancing with convenient utilization of heterogeneous grid resources. Methods: In this paper, we introduce Ant based Dynamic Load Balancing Algorithm (ADLBA), a decentralized dynamic load balancing algorithm using Ant Colony Optimization (ACO), which selects the best resources to be allocated to the tasks considering economic cost, resources' capacity, and local load. Results: We used the Gridsim toolkit to evaluate the efficiency of ADLBA against the Randomized Algorithm (RA) with various number of tasks and resource allocation polices. Our study results show that ADLBA outperforms RA in terms of execution cost and total application execution time (makespan), and they also show that using time-shared allocation policy in the resources leads to better results in both algorithms. Conclusion: We found that ADLBA is suitable for grid users which aim to execute their applications quickly with lower cost.


Ant Colony Optimization (ACO), Grid Computing, Gridsim, Load Balancing, Makespan.

Full Text:

 |  (PDF views: 280)


  • Kaur P, Singh H. Dynamic Load balancing in grid environment using adaptive computing. International Conference on Recent Trends of Computer Technology in Academia (ICRTCTA-2012); 2012. p. 625–32.
  • Foster I, Kesselman C, Tuecke S. The anatomy of the grid: Enabling scalable virtual organizations. International Journal of High Performance Computing Applications. 2001 Aug;15(3):200–2.
  • Devi KN, Tamilarasi A. Dynamic scheduling in grid environent with the improvement of fault tolerant level. Indian Journal of Science and Technology. 2015 Apr; 8(8):507–15.
  • Chervenak A, Foster I, Kesselman C, Salisbury C, Tuecke S. The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets. Journal of Network and Computer Applications. 2000 Apr; 3(3):187–200.
  • Salleh S, Zomaya AY. Scheduling in parallel computing systems: Fuzzy and annealing techniques. New York: Springer Science and Business Media; 2012.
  • Sharma R, Soni VK, Mishra MK, Bhuyan P. A survey of job scheduling and resource management in grid computing. International Journal of Computer, Electrical, Automation, Control and Information Engineering. 2010; 4(4):736–41.
  • Seema K, Ku G, Kumar RM, Rahul S. Improved hybrid scheduling algorithm for grid infrastructure. Indian Journal of Science and Technology. 2015 Dec; 8(35):1–6.
  • Xu C, Lau FC. Load balancing in parallel computers: Theory and practice. New York: Springer Science and Business Media; 1997.
  • Zhu Y, Ni LM. A survey on grid scheduling systems. Hong Kong: University of science and Technology; 2003.
  • Kandagatla C. Survey and taxonomy of grid resource management systems. Austin: University of Texas; 2003. p. 1–12.
  • Zaki MJ, Li W, Parthasarathy S. Customized dynamic load balancing for a network of workstations. 1996 Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing; Syracuse, NY, USA. 1996. p. 282–91.
  • Von Laszewski G, Foster I, Gawor J, Lane P, Rehn N, Russell M. Designing grid-based problem solving environments and portals. 2001 Proceedings of the 34th Annual Hawaii International Conference on System Sciences; Maui, HI, USA. 2001.
  • Nath RK. Efficient load balancing algorithm in grid environment. Patiala: Thapar University; 2007.
  • Srivastava PK, Gupta S, Yadav DS. Improving performance in load balancing problem on the grid computing system. International Journal of Computer Applications. 2011 Feb; 16(1):6–10.
  • Dorigo M, Blum C. Ant colony optimization theory: A survey. Theoretical Computer Science. 2005 May; 344(23):243–78.
  • Ludwig SA, Moallem A. Swarm intelligence approaches for grid load balancing. Journal of Grid Computing. 2011 Sep; 9(3):279–301.
  • Goyal SK, Singh M. Adaptive and dynamic load balancing in grid using ant colony optimization. International Journal of Engineering and Technology. 2012; 4(4):167–74.
  • Rohil H, Kalyan S. A heuristic based load balancing algorithm. IJCEM. 2012 Nov; 15(6):56–61.
  • Sharma D, Sharma K, Dalal S. Optimized load balancing in grid computing using tentative ant colony algorithm. International Journal of Recent Research Aspects. 2014 Jun; 1(1):35–9.
  • Nadimi-Shahraki MH, Fard ES, Safi F. Efficient load balancing using ant colony optimization. Journal of Theoretical and Applied Information Technology. 2015 Jul; 77(2):253–8.
  • Buyya R, Murshed M. Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and computation: Practice and experience. 2002 Nov; 14(13‐15):1175–20.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.