Industry 4.0
Vol. 10 (2025), Issue 1, pg(s) 7-9

TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

Distributed Task Allocation Based on Hierarchical Interactions in Wasp Colonies

  • 1 Faculty of Information Technology, Polytechnic University of Tirana, Tirana, Albania

Abstract

Distributed systems are the fundamental building blocks of today’s internet and cloud data centers. Applications running on these systems are divided into multiple tasks, which are executed in parallel across different nodes. A key aspect of these systems is task allocation, or load balancing, which involves distributing tasks among the compute nodes. The performance of the application and the utilization of system resources heavily depend on the task allocation algorithm. In this paper, we address the task allocation problem in distributed systems where each node is connected to several neighbouring nodes, forming a graph-like structure. We propose a distributed task allocation algorithm inspired by the hierarchical interactions within wasp insect colonies. The key features of our algorithm include its reliance on simple probabilistic rules and a decentralized approach, which eliminates the need for a central coordinator. This makes the algorithm highly robust to failures and scalable. Through simulations, we compare our algorithm with another simple distributed approach and demonstrate its superiority, particularly in cases involving large data transfers. The probabilistic nature of our algorithm leads to fewer task transfers, resulting in more efficient task allocation.

Keywords

References

  1. Y. Jiang, IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp. 585–599 (2015)
  2. RV Lopes and D Menascé, IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 12, pp. 3412–3428 (2016)
  3. Z. Zhang and X. Zhang, The 2nd International Conference on Industrial Mechatronics and Automation, vol. 2, pp. 240–243 (2010)
  4. G. Theraulaz, S. Goss, J. Gervet and J. Deneubourg, First International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pages 346–355 (1991)
  5. E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm intelligence: from natural to artificial systems. USA: Oxford University Press, Inc., (1999)
  6. E. Elsedimy and F. Algarni, IET Networks, vol. 11, no. 2, pp. 43–57, (2022)
  7. Y. Raghav and V. Vyas, International Journal of Information Technology, vol. 15, 06 (2023)
  8. Arzoo and A. Kumar, in Information and Communication Technology for Competitive Strategies, vol 401, pages 9-20 (2021)
  9. J. Cao, in Fifth IEEE/ACM International Workshop on Grid Computing, pp. 388–395 (2004)
  10. S. A. Ludwig and A. Moallem, Journal of Grid Computing, vol. 9, no. 3, pp. 279–301 (2011)
  11. A.-L. Barabasi and R. Albert, Science, vol. 286, no. 5439, pp. 509– 512, (1999)
  12. V.A. Cicirello and S.F. Smith, Autonomous Agents and Multi-Agent Systems 8, 237–266 (2004)

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