A Hybrid Approach Combining Ant Colony Optimization and Simulated Annealing for Cloud Resource Scheduling
DOI:
https://doi.org/10.63623/9rv3x042Keywords:
Cloud computing, Resource scheduling, Ant colony, Simulated annealing, CostAbstract
Cloud computing is imperative to schedule efficiently for tasks and resources to assure performance, reduction of costs, and service-level agreement. Traditional methods cannot balance this complexity, resulting in the conception of a hybrid model that will be based on the integration of ACO with SA. Here, the former algorithm applies the collective intelligence of ACO, coupled with positive feedback, to achieve better quality solutions and escapes the local optimum of the latter. This algorithm runs in two phases: The initial solution is generated using the ACO, and the solution is refined using SA. Simulated experiments within a simulated cloud environment of CloudSim showed that this hybrid approach succeeds in minimizing makespan, reducing energy consumption, and optimizing the cost for different workloads. The ACO-SA algorithm heralds a promising direction toward highly efficient management of cloud resources and opens up further research directions in hybridizing other complementary algorithms.
References
[1]Kommisetty PDNK, Abhireddy N. Cloud Migration Strategies: Ensuring Seamless Integration and Scalability in Dynamic Business Environments. International Journal of Engineering and Computer Science. 2024, 13(04), 26146-26156. DOI: 10.18535/ijecs/v13i04.4812
[2]Singh S, Chana I. QRSF: QoS-aware resource scheduling framework in cloud computing. The Journal of Supercomputing. 2015, 71, 241-292. DOI: 10.1007/s11227-014-1295-6
[3]Khallouli W, Huang JW. Cluster resource scheduling in cloud computing: literature review and research challenges. The Journal of supercomputing. 2022, 78(05), 6898-6943. DOI: 10.1007/s11227-021-04138-z
[4]Madni SHH, Latiff MSA, Coulibaly Y, Abdulhamid SM. Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities. Journal of Network and Computer Applications. 2016, 68, 173-200. DOI: 10.1016/j.jnca.2016.04.016
[5]Blum C. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews. 2005, 2(04), 353-373. DOI: 10.1016/j.plrev.2005.10.001
[6]Suman B, Kumar P. A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society. 2006, 57(10), 1143-1160. DOI: 10.1057/palgrave.jors.2602068
[7]Singh P, Dutta M, Aggarwal N. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems. 2017, 52(1), 1-51. DOI: 10.1007/s10115-017-1044-2
[8]Agarwal M, Srivastava GMS. A genetic algorithm inspired task scheduling in cloud computing. 2016 International Conference on Computing, Communication and Automation (ICCCA). 2016. DOI: 10.1109/CCAA.2016.7813746
[9]Abdelaziz A, Anastasiadou M, Castelli M. A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment. Applied Sciences. 2020, 10(18), 6538. DOI: 10.3390/app10186538
[10]Tawfeek MA, El-Sisi A, Keshk AE, Torkey FA. Cloud task scheduling based on ant colony optimization. 2013 8th International Conference on Computer Engineering & Systems (ICCES). 2013. DOI: 10.1109/ICCES.2013.6707172
[11]Pradhan P, Behera PK, Ray BNB. Modified round robin algorithm for resource allocation in cloud computing. Procedia Computer Science. 2016, 85, 878-890. DOI: 10.1016/j.procs.2016.05.278
[12]Gulbaz R, Siddiqui AB, Anjum N, Alotaibi AA, Althobaiti T, et al. Balancer genetic algorithm—A novel task scheduling optimization approach in cloud computing. Applied Sciences. 2021, 11(14), 6244. DOI: 10.3390/app11146244
[13]Tsai JT, Fang JC, Chou JH. Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Computers & Operations Research. 2013, 40(12), 3045-3055. DOI: 10.1016/j.cor.2013.06.012
[14]Keshanchi B, Souri A, Navimipour NJ. An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. Journal of Systems and Software. 2017, 124, 1-21. DOI: 10.1016/j.jss.2016.07.006
[15]Mansouri N, Zade BMH, Javidi MM. Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Computers & Industrial Engineering. 2019, 130, 597-633. DOI: 10.1016/j.cie.2019.03.006
[16]Shukri SE, Al-Sayyed R, Hudaib A, Mirjalili S. Enhanced multi-verse optimizer for task scheduling in cloud computing environments. Expert Systems with Applications. 2021, 168(4), 114230. DOI: 10.1016/j.eswa.2020.114230.
[17]Velliangiri S, Karthikeyan P, Arul Xavier VM, Baswaraj D. Hybrid electro search with genetic algorithm for task scheduling in cloud computing. Ain Shams Engineering Journal. 2021, 12(1), 631-639. DOI: 10.1016/j.asej.2020.07.003.
[18]Abd Elaziz M, Attiya I. An improved Henry gas solubility optimization algorithm for task scheduling in cloud computing. Artificial Intelligence Review. 2021, 54(5), 3599-3637. DOI: 10.1007/s10462-020-09933-3.
[19]Bal PK, Mohapatra PK, Das TK, Srinivasan K, Hu YC. A joint resource allocation, security with efficient task scheduling in cloud computing using hybrid machine learning techniques. Sensors. 2022, 22(3), 1242. DOI: 10.3390/s22031242.
[20]Rajakumari K, Kumar M, Verma G, Balu S, Sharma DK, et al. Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing. Computer Systems Science & Engineering. 2022, 40(2), 581-592. DOI: 10.32604/csse.2022.019175.
[21]Imene L, Sihem S, Okba K, Mohamed B. A third generation genetic algorithm NSGAIII for task scheduling in cloud computing. Journal of King Saud University-Computer and Information Sciences. 2022, 34(9), 7515-7529. DOI: 10.1016/j.jksuci.2022.03.017.
[22]Saravanan G, Neelakandan S, Ezhumalai P, Maurya S. Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing. Journal of Cloud Computing. 2023, 12(1), 24. DOI: 10.1186/s13677-023-00401-1.
[23]Chandrashekar C, Krishnadoss P, Kedalu Poornachary V, Ananthakrishnan B, Rangasamy K. HWACOA scheduler: Hybrid weighted ant colony optimization algorithm for task scheduling in cloud computing. Applied Sciences. 2023, 13(6), 3433. DOI: 10.3390/app13063433.
[24]Praveen SP, Ghasempoor H, Shahabi N, Izanloo F. A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing. Mathematical Problems in Engineering. 2023, 2023(1), 6516482. DOI: 10.1155/2023/6516482.
[25]Sinha A, Banerjee P, Roy S, Rathore N, Singh NP, et al. Improved Dynamic Johnson Sequencing Algorithm (DJS) in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading. Journal of Systems Science and Systems Engineering. 2024, 33(4), 391-424. DOI: 10.1007/s11518-024-5606-z.
[26]Kaliappan S, Paranthaman V, Raj Kamal MD, AVV S, Muthukannan M. A novel approach of particle swarm and ANT colony optimization for task scheduling in cloud. 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2024, 272-278. DOI: 10.1109/Confluence60223.2024.10463398.
[27]Kumar M, Sharma SC. Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. International Journal of Computers and Applications. 2020, 42(1), 108-117. DOI: 10.1080/1206212X.2017.1404823.
[28]Zhou XM, Zhang GX, Sun J, Zhou JL, Wei TQ, et al. Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT. Future Generation Computer Systems. 2019, 93(5), 278-289. DOI: 10.1016/j.future.2018.10.046.
[29]Al-Shaikh A, Khattab H, Sharieh A, Sleit A. Resource utilization in cloud computing as an optimization problem. International Journal of Advanced Computer Science and Applications. 2016, 7(6). DOI: 10.14569/IJACSA.2016.070643.
[30]Vakilinia S, Heidarpour B, Cheriet M. Energy efficient resource allocation in cloud computing environments. IEEE Access. 2016, 4, 8544-8557. DOI: 10.1109/ACCESS.2016.2633558.
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