Abstract
Cloud computing is an important computing paradigm based on large scale distributed infrastructures offering resources to consumers in a pay-as-you-go manner. An important aspect of cloud infrastructure management is the task scheduling problem. In this problem, tasks submitted by users and encapsulated in virtual machines are allocated to compute nodes in order to optimize some performance metric. In this paper a distributed task scheduling approach based on swarm intelligence is proposed, where schedulers distributed on different nodes make local task allocation decisions based on principles of ant colony optimization. Ant colony optimization is combined with queue load information for mitigating delayed reward problem that results from high load condition. Experimental evaluation in a simulated environment shows improved results compared to a distributed scheduling approach based on ant colony or queue load information only.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Armbrust, M., et al.: Above the clouds: a Berkeley view of cloud computing. Technical report, University of California at Berkeley, February 2009
Gawali, M.B., Shinde, S.K.: Task scheduling and resource allocation in cloud computing using a heuristic approach. J. Cloud Comput. 7(1), 1–6 (2018)
Pol, S.S., Singh, A.: Task scheduling algorithms in cloud computing: a survey. In: 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC), pp. 244–249. IEEE (2021)
Corne, D.W., Reynolds, A., Bonabeau, E.: Swarm intelligence. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 1599–1622. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-540-92910-9_48
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26, 29–41 (1996)
Tian, W., Xiong, Q., Cao, J.: An online parallel scheduling method with application to energy-efficiency in cloud computing. J. Supercomput. 66(3), 1773–1790 (2013). https://doi.org/10.1007/s11227-013-0974-z
Tian, W., et al.: On minimizing total energy consumption in the scheduling of virtual machine reservations. J. Netw. Comput. App. 113, 64–74 (2018)
Pradhan, P., Ku, P., Ray, B.N.: Modified round robin algorithm for resource allocation in cloud computing. Proc. Comput. Sci. 85, 878–890 (2016)
Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: 2011 Sixth Annual Chinagrid Conference, pp. 3–9. IEEE (2011)
Liu, X.-F., Zhan, Z.-H., Deng, J.D., Li, Y., Tianlong, G., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22(1), 113–128 (2018)
Elsedimy, E., Algarni, F.: MOTS-ACO: an improved ant colony optimiser for multi-objective task scheduling optimisation problem in cloud data centres. IET Netw. 11(2), 43–57 (2022)
Chen, W.-N., Zhang, J.: An ant colony optimization approach to a grid workflow scheduling problem with various QOS requirements. IEEE Trans. Syst. Man Cybern. Part C App. Rev. 39(1), 29–43 (2009)
Pacini, E., Mateos, C., Garino, C.G.: Balancing throughput and response time in online scientific clouds via ant colony optimization (sp2013/2013/00006). Adv. Eng. Softw. 84, 31–47 (2015)
Ludwig, S.A., Moallem, A.: Swarm intelligence approaches for grid load balancing. J. Grid Comput. 9(3), 279–301 (2011)
Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: An ant colony based load balancing strategy in cloud computing. In: Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.) Advanced Computing, Networking and Informatics- Volume 2. SIST, vol. 28, pp. 403–413. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07350-7_45
Nishant, K., Sharma, P., Krishna, V., Gupta, C., Singh, K.P., Rastogi, R.: Load balancing of nodes in cloud using ant colony optimization. In: 2012 UKSim 14th International Conference on Computer Modelling and Simulation, pp. 3–8. IEEE (2012)
Abdallah, S., Lesser, V.: Multiagent reinforcement learning and self-organization in a network of agents. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2007. ACM (2007)
Boyan, J., Littman, M.: Packet routing in dynamically changing networks: a reinforcement learning approach. In: Advances in Neural Information Processing Systems, vol. 6. Morgan-Kaufmann (1993)
Di Caro, G.: AntNet : distributed stigmergetic control for communications networks. J. Artif. Intell. Res. 9, 317–365 (1998)
Schoonderwoerd, R., Holland, O.E., Bruten, J.L., Rothkrantz, L.J.M.: Ant-based load balancing in telecommunications networks. Adapt. Behav. 5(2), 169–207 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Minarolli, D. (2023). A Distributed Task Scheduling Approach for Cloud Computing Based on Ant Colony Optimization and Queue Load Information. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2022. Lecture Notes in Networks and Systems, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-031-19945-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-19945-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19944-8
Online ISBN: 978-3-031-19945-5
eBook Packages: EngineeringEngineering (R0)