Research on Task Scheduling for Internet of Things Cloud Computing Based on Improved Chicken Swarm Optimization Algorithm
DOI:
https://doi.org/10.13052/jicts2245-800X.1212Keywords:
Cloud computing, task scheduling, chicken swarm optimization, Internet of ThingsAbstract
Aiming at the shortcomings of long completion time and high consumption cost of cloud computing batch task scheduling in IoT, an Improved Chicken Swarm Optimization Algorithm (ICSO) for task scheduling in cloud computing scenarios is proposed. Specifically, in order to solve the problems of slow convergence and falling into local optimum of the chicken swarm optimization algorithm, we adopt the nonlinear decreasing technique of the rooster and the weighting technique of the hen, optimize the following coefficients of the chicks, and apply ICSO to cloud computing task scheduling. In simulation experiments, we conducted a large number of experiments using four standard benchmark functions with different number of tasks and the results show that ICSO algorithm reduces 25.8%, 9.3%, 8.8%, 7.5% in small task time compared to CSO, DCSO, GCSO, ABCSO in large task time by 30.8%, 8.3%, 7.8%, 6.3%, 11.8%, 10.3%, 8.8%, 7.5% savings in small task cost and 25.8%, 11.2%, 10.8%, 9.3% savings in large task cost. This method effectively reduces task scheduling time and cost consumption. Meanwhile, we tested it in combination with an IoT-based cloud platform and achieved very satisfying Results.
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References
Kim W. Cloud computing: Today and tomorrow[J]. J. Object Technol, 2009, 8(1): 65–72.
Sadeeq M M, Abdulkareem N M, Zeebaree S R M, et al. IoT and Cloud computing issues, challenges and opportunities: A review[J]. Qubahan Academic Journal, 2021, 1(2): 1–7.
Arunarani A R, Manjula D, Sugumaran V. Task scheduling techniques in cloud computing: A literature survey[J]. Future Generation Computer Systems, 2019, 91: 407–415.
Armbrust M, Fox A, Griffith R, et al. A view of cloud computing[J]. Communications of the ACM, 2010, 53(4): 50–58.
Gao J, Wang H, Shen H. Task failure prediction in cloud data centers using deep learning[J]. IEEE transactions on services computing, 2020, 15(3): 1411–1422.
Kalra M, Singh S. A review of metaheuristic scheduling techniques in cloud computing[J]. Egyptian informatics journal, 2015, 16(3): 275–295.
Meng X, Liu Y, Gao X, et al. A new bio-inspired algorithm: chicken swarm optimization[C]. International conference in swarm intelligence. Springer, Cham, 2014: 86–94.
Arunarani A R, Manjula D, Sugumaran V. Task scheduling techniques in cloud computing: A literature survey[J]. Future Generation Computer Systems, 2019, 91: 407–415.
Cheng L, Kotoulas S. Efficient skew handling for outer joins in a cloud computing environment. IEEE Transactions on Cloud Computing[J]. 2015 Oct 7;6(2): 558–571.
Cheng F, Huang Y, Tanpure B, Sawalani P, Cheng L, Liu C. Cost-aware job scheduling for cloud instances using deep reinforcement learning. Cluster Computing[J]. 2022, 25(1): 619–631.
Patra M K, Misra S, Sahoo B, et al. GWO-Based Simulated Annealing Approach for Load Balancing in Cloud for Hosting Container as a Service[J]. Applied Sciences, 2022, 12(21): 11115.
Bezdan T, Zivkovic M, Bacanin N, et al. Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm[J]. Journal of Intelligent & Fuzzy Systems, 2022, 42(1): 411–423.
Chen X, Cheng L, Liu C, et al. A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems[J]. IEEE Systems Journal. 2020, 14(3): 3117–3128.
Mangalampalli S, Swain S K, Mangalampalli V K. Multi Objective Task Scheduling in Cloud Computing Using Cat Swarm Optimization Algorithm[J]. Arabian Journal for Science and Engineering, 2022, 47(2): 1821–1830.
Velliangiri S, Karthikeyan P, Xavier V M A, et al. Hybrid electro search with genetic algorithm for task scheduling in cloud computing[J]. Ain Shams Engineering Journal, 2021, 12(1): 631–639.
Abualigah L, Diabat A. A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments[J]. Cluster Computing, 2021, 24(1): 205–223.
Muthulakshmi B, Somasundaram K. A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment[J]. Cluster Computing, 2019, 22(5): 10769–10777.
Manikandan N, Gobalakrishnan N, Pradeep K. Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment[J]. Computer Communications, 2022, 187: 35–44.
Chen X, Long D. Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm[J]. Cluster Computing, 2019, 22(2): 2761–2769.
Liu J, Cheng L. SwiftS: a dependency-aware and resource efficient scheduling for high throughput in clouds. InIEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) 2021 May 10 (pp. 1–2). IEEE.
Mao Y, Sharma V, Zheng W, Cheng L, Guan Q, Li A. Elastic resource management for deep learning applications in a container cluster[J]. IEEE Transactions on Cloud Computing. 2022, 11(2): 2204–2216.
Jemmali M, Denden M, Boulila W, et al. A Novel Model Based on Window-Pass Preferences for Data Emergency Aware Scheduling in Computer Networks[J]. IEEE Transactions on Industrial Informatics, 2022, 18(11): 7880–7888.
Patrick N V, Misra S, Adetiba E, et al. An Incremental Load Balancing Algorithm in Federated Cloud Environment[M]//Data, Engineering and Applications: Select Proceedings of IDEA 2021. Singapore: Springer Nature Singapore, 2022: 395–408.
Yu D H. Research on The Cloud Computing Task Scheduling Strategy Based on Multidimensional QoS Constraints[D]. Chongqing University of Posts and Telecommunications, 2021
Wang Z, Zhang W, Guo Y, et al. A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites[J]. Applied Soft Compu-ting, 2023, 133: 109920.
Basha A J, Aswini S, Aarthini S, et al. Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network[J]. Computer Systems Science & Engineering, 2023, 44(2): 1451–1466.
Pushpa R, Siddappa M. Fractional Artificial Bee Chicken Swarm Optimization technique for QoS aware virtual machine placement in cloud[J]. Concurrency and Computation: Practice and Experience, 2023, 35(4): e7532.
