Sami J. Habib
ABSTRACT
Lately, the business enterprise networks (BEN) are expected to support more online applications than what are initially designed for, due to the competitive nature of the business nowadays. The business applications are known to generate sudden increase in traffic workload, which poses challenges in providing guaranteed service to all the new and old clients. Thus, the network administrators are faced with many challenges in how to smartly manage BEN with the available resources and at the same time maintain satisfactory returns on the business. Over-provisioning of resources may not be a feasible solution, and thus, BEN can be improved by re-synthesizing the existing infrastructure by localizing the workflow. At the duration of the sudden increase in the workflow within BEN, we view the infrastructure as a fully connected graph and the aim is to convert the fully connected graph into a connected one by re-grouping the clients into new clusters. We have formulated the conversion problem as an optimization problem with an objective function to maximize the local traffic within the new clusters; moreover, we have utilized Ant Colony Optimization (ACO) to find the suitable conversion. The simulation results show that re-synthesizing a typical BEN comprising of 100 clients with a heavy traffic of 7.54 TB into a set of 5 clusters reduces the backbone traffic by 18%.
- Dorigo, M., Maniezzo, V., and Colorni, A. 1996. Ant System: Optimization by A Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1), 29--41. Google Scholar
Digital Library
- Mezuman, E., and Weiss, Y. 2012. Globally Optimizing Graph Partitioning Problems Using Message Passing. In the Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, April 21--23, La Palma, Canary Islands, Spain, 770--778.Google Scholar
- Bui, T. N., and Moon, B. R. 1996. Genetic Algorithm and Graph Partitioning. IEEE Transactions on Computers. 45(7), 841--855. Google ScholarDigital Library
- Johnson, D. S., Aragon, C. R., McGeoch, L. A., and Schevon, C. 1989. Optimization by Simulated Annealing: an Experimental Evaluation: Part I - Graph Partitioning, Operation Research, 38, 865--892. Google ScholarDigital Library
- Kohmoto, K., Katayam, K., and Narihisa, H. 2003. Performance of Genetic Algorithm for the Graph Partitioning Problem. Mathematics and Computer Modeling, 38(11-13), 1325--352. Google ScholarDigital Library
- Korosec, P., Jurilic, E. C., and Robic, B. 2004. Solving the Mesh-Partitioning Problem with an Ant-Colony Algorithm, Parallel Computing, 30 (5), 785--801. Google ScholarDigital Library
- Chu, S., Roddick, J. F., Su, C., and Pan, J. 2004. Constrained Ant Colony Optimization for Data Clustering. In the Proceedings of 8th Pacific Rim International Conference on Artificial Intelligence, August 9-13, Auckland, New Zealand.Google Scholar
- Taskova, K. Korosec, P., and Jurilic, E.C. 2008. A Distributed Multilevel Ant Colonies Approach. Informatica, 32, 307--317.Google Scholar
- Fu, H. 2008. A Novel Clustering Algorithm with ANT Colony Optimization. In the Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Application, December 19-20, Wuhan, China, 66-69. Google ScholarDigital Library
- Dai, W., Liu, S., and Liang, S. 2009. An improved Ant Colony Optimization Cluster Algorithm Based on Swarm Intelligence. Journal of Software, 4(4), 299--306.Google ScholarCross Ref
- Jiang, H., Yu, Q., and Gong, Y. 2010. An Improved Ant Colony Clustering Algorithm. In the Proceedings of 3rd International Conference on Biomedical Engineering and Informatics. October 16-18, Yantai, China, 2368 --2372.Google Scholar
- He, D., Liu, J., Liu, D., Jin, D. and Jia, Z.2011.Ant Colony Optimization for Community Detection in Large-Scale Complex Networks. In the Proceedings of Seventh International Conference on Natural Computation, July 26-28, Shanghai, China, 1151-1155.Google Scholar
- Habib, S. J., and Marimuthu, P. N. 2012. Optimizing Network Performance and Carbon Offset through Opportunistic Reclustering. Concurrency and Computation: Practice and Experience. 24(16), 1927--1939. Google ScholarDigital Library
- Chicco, G., Ionel, O. M., and Porumb, R. 2013. Formation of Load Pattern Clusters Exploiting Ant Colony Clustering Principles. In the Proceedings of EUROCON, July 1-4, Zagreb, Croatia, 1460-1467.Google Scholar
- Liyan, D., Sainan, Z., Geng, T., Yongli, L., and Guanyan, C. 2013. Ant Colony Clustering Algorithm Based on Swarm Intelligence. In the Proceedings of 6th International Conference on Intelligent Networks and Intelligent Systems, November 1-3, Shenyang, China, 123-126. Google ScholarDigital Library
- Mane, S. U., and Gaikwad, P. G. 2014. Nature Inspired Techniques for Data Clustering. In the Proceedings of International Conference on Circuits, Systems, Communication and Information Technology Applications, April 4-5, Maharashtra, India, 419-424.Google Scholar
- Ashraf, A., and Porres, I. 2014. Using Ant Colony System to Consolidate Multiple Web Applications in a Cloud Environment. In the Proceedings of 22nd Euro-micro International Conference on Parallel, Distributed and Network-Based Processing, February 12-14, Torino, Italy, 482--489. Google ScholarDigital Library
- Hussain, T., Marimuthu, P. N., and Habib, S. J. 2014. Supporting Multimedia Applications through Network Redesign. International Journal of Communication Systems. 27(3), 430--448.Google ScholarDigital Library
Index Terms
- Enterprise Workload Management through Ant Colony Optimization
Recommendations
Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures
A heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimization and harmony search scheme. HPSACO ...
Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering
Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with ...
An improved ant colony optimization and its application to vehicle routing problem with time windows
The ant colony optimization (ACO), inspired from the foraging behavior of ant species, is a swarm intelligence algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low ...
Comments