RESOURCE DISCOVERY IN DISTRIBUTED EXASCALE SYSTEMS USING A MULTI-AGENT MODEL: CATEGORIZATION OF AGENTS BASED ON THEIR CHARACTERISTICS
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Volume 6 (1), June 2023, Pages 113-120
Fakhraddin Abdullayev
Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Resource discovery is a crucial component in high-performance computing (HPC) systems. This paper presents a multi-agent model for resource discovery in distributed exascale systems. Agents are categorized based on resource types and behavior-specific characteristics. The model enables efficient identification and acquisition of memory, process, file, and IO resources. Through a comprehensive exploration, we highlight the potential of our approach in addressing resource discovery challenges in exascale computing systems, paving the way for optimized resource utilization and enhanced system performance.
Keywords:
HPC, Resource Discovery, Agents, Dynamic and Interactive Event, Exascale Systems.
DOI: https://doi.org/10.32010/26166127.2023.6.1.113.120
Reference
Adibi, E., & Khaneghah, E. M. (2018). Challenges of resource discovery to support distributed exascale computing environment. Azerbaijan Journal of High Performance Computing, 1(2), 168-178.
Adibi, E., & Mousavi Khaneghah, E. (2020). ExaRD: introducing a framework for empowerment of resource discovery to support distributed exascale computing systems with high consistency. Cluster Computing, 23, 3349-3369.
Castano, S., Ferrara, A., Montanelli, S., & Racca, G. (2004, April). Matching techniques for resource discovery in distributed systems using heterogeneous ontology descriptions. In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. (Vol. 1, pp. 360-366). IEEE.
Dimakopoulos, V. V., & Pitoura, E. (2003, August). A peer-to-peer approach to resource discovery in multi-agent systems. In International Workshop on Cooperative Information Agents (pp. 62-77). Berlin, Heidelberg: Springer Berlin Heidelberg.
Ding, S., Yuan, J., & Hu, L. (2005, March). A heuristic algorithm for agent-based grid resource discovery. In 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (pp. 222-225). IEEE.
Kambayashi, Y., & Harada, Y. (2007). A resource discovery method based on multi-agents in P2P systems. In Agent and Multi-Agent Systems: Technologies and Applications: First KES International Symposium, KES-AMSTA 2007, Wroclaw, Poland, May 31–June 1, 2007. Proceedings 1 (pp. 364-374). Springer Berlin Heidelberg.
Khaneghah, E. M., Aliev, A. R., Bakhishoff, U., & Adibi, E. (2018). The influence of exascale on resource discovery and defining an indicator. Azerbaijan Journal of High Performance Computing, 1(1), 3-19.
Raj, P., & Sekhar, C. (2020). Comparative Study on CPU GPU and TPU. Int. Journal of Computer Science and Information Technology for Education, 5, 31-38.
Selvaraj, C., & Anand, S. (2012). A survey on security issues of reputation management systems for peer-to-peer networks. Computer science review, 6(4), 145-160.
Tan, Y., & Zheng, Z. (2009, May). A multi-agent based resource discovery scheme for p2p systems. In 2009 International Workshop on Intelligent Systems and Applications (pp. 1-4). IEEE.
Tan, Y., Han, J., & Wu, Y. (2010). A multi-agent based efficient resource discovery mechanism for grid systems. Journal of Computational Information Systems, 6(11), 3623-3631.
Yamasaki, J., & Kambayashi, Y. (2010, November). Design an Implementation of Bee Hive in a Mult-agent Based Resource Discovery Method in P2P Systems. In 2010 First International Conference on Networking and Computing (pp. 292-293). IEEE.
Zarrin, J., Aguiar, R. L., & Barraca, J. P. (2018). Resource discovery for distributed computing systems: A comprehensive survey. Journal of parallel and distributed computing, 113, 127-166.
