International Journal of Computer Networks and Applications

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A Load Balancing Aware Task Scheduling using Hybrid Firefly Salp Swarm Algorithm in Cloud Computing


Affiliations
1 Department of Computer Science and Engineering, Banasthali Vidyapith, Niwai
2 Department of Computer Science and Engineering, Banasthali Vidyapith, Niwai, India
 

Cloud computing is an evolutionary computational model which provides on-demand scalable and flexible resources by the pay-per-use concept. Due to the flexibility of cloud, several organizations are setting up more data centers and switching their businesses to the cloud technology. These industries need a proper load balancing to ensure the efficient resources utilization, which reduces resource wastage and helps to optimize costs. Optimal resource allocation can be achieved through efficient task scheduling and load-balancing. An efficient scheduling with load-balancing allocates resources in a balanced way and optimizes the quality of service (QoS) parameters. Task migration is the best way to balance the load. This paper hybridizes the Salp Swarm Algorithm (SSA) with the Firefly Algorithm (FFA), named as Hybrid Firefly Salp Swarm Algorithm (HFFSSA). This approach utilizes FFA's operators to enhance the exploitation capability of SSA by functioning as a local search. Further, a load balancing (LB) heuristic is proposed and incorporated with HFFSSA, named as Load Balancing Salp Swarm Algorithm (LBFFSSA). For verification, the presented work is evaluated by two experimental series. First HFFSSA is tested on global benchmark functions, where it shows its superiority over other existing metaheuristic approaches such as Firefly Algorithm (FFA), Grey Wolf Algorithm (GWO), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA). In the second series, the LB-FFSSA is evaluated on real datasets (Planet Lab and NASA) using CloudSim Simulator; again, results outperform similar metaheuristics. The simulation results show that LB-FFSSA significantly reduces makespan and improves resource utilization. Furthermore, the proposed algorithm minimizes the Load imbalance Factor (LIF) by migrating the task from an over utilized virtual machine to an underutilized one. It also shows improvement in waiting time and throughput. Simulation results prove that proposed model improves by an average up to 32.3%, LIF by 50.4%, throughput by 42.1%, resource utilization by 40%, and waiting time by 50%.

Keywords

Cloud Computing, Hybrid Task Scheduling, Firefly (FFA), Salp Swarm Algorithm (SSA), Task Migration, Load Balancing.
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  • Mell, Peter, and Timothy Grance. "Cloud computing: recommendations of the national institute of standards and technology." NIST, Spec. Pub (2011): 800-145.

  • Dillon, Tharam, Chen Wu, and Elizabeth Chang. "Cloud computing: issues and challenges." 2010 24th IEEE international conference on advanced information networking and applications. Ieee, 2010.

  • Moharana, Shanti Swaroop, Rajadeepan D. Ramesh, and Digamber Powar. "Analysis of load balancers in cloud computing." International Journal of Computer Science and Engineering 2.2 (2013): 101-108.

  • Mahmud, Shahid, Rahat Iqbal, and Faiyaz Doctor. "Cloud enabled data analytics and visualization framework for health-shocks prediction." Future Generation Computer Systems 65 (2016): 169-181.

  • Masdari, Mohammad, et al. "A survey of PSO-based scheduling algorithms in cloud computing." Journal of Network and Systems Management 25.1 (2017): 122-158.

  • Kalra, Mala, and Sarbjeet Singh. "A review of metaheuristic scheduling techniques in cloud computing." Egyptian informatics journal 16.3 (2015): 275-295.

  • Zhou, Jincheng, et al. "Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing." Journal of Cloud Computing 12.1 (2023): 1-21.

  • Sakellariou, Rizos, and Henan Zhao. "A hybrid heuristic for DAG scheduling on heterogeneous systems." 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.. IEEE, 2004.

  • Aissi, Hassene, Cristina Bazgan, and Daniel Vanderpooten. "Complexity of the min–max and min–max regret assignment problems." Operations research letters 33.6 (2005): 634-640.

  • Pradhan, Pandaba, Prafulla Ku Behera, and B. N. B. Ray. "Modified round robin algorithm for resource allocation in cloud computing." Procedia Computer Science 85 (2016): 878-890.

  • Eberhart, Russell, and James Kennedy. "A new optimizer using particle swarm theory." MHS'95. Proceedings of the sixth international symposium on micro machine and human science. Ieee, 1995.

  • Mirjalili, Seyedali, et al. "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems." Advances in engineering software 114 (2017): 163-191.

  • 13Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.

  • Shishira, S. R., A. Kandasamy, and K. Chandrasekaran. "Survey on meta heuristic optimization techniques in cloud computing." 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, 2016.

  • Thakur, Avnish, and Major Singh Goraya. "A taxonomic survey on load balancing in cloud." Journal of Network and Computer Applications 98 (2017): 43-57.

  • Ghomi, Einollah Jafarnejad, Amir Masoud Rahmani, and Nooruldeen Nasih Qader. "Load-balancing algorithms in cloud computing: A survey." Journal of Network and Computer Applications 88 (2017): 50-71.

  • Madni, Syed Hamid Hussain, et al. "An appraisal of meta-heuristic resource allocation techniques for IaaS cloud." (2016).

  • Faris, Hossam, et al. "Salp swarm algorithm: theory, literature review, and application in extreme learning machines." Nature-inspired optimizers: theories, literature reviews and applications (2020): 185-199.

  • Jain, Richa, and Neelam Sharma. "A QoS aware binary salp swarm algorithm for effective task scheduling in cloud computing." Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2019, Volume 2. Springer Singapore, 2021.

  • Jain, Richa, and Neelam Sharma. "A deadline-constrained time-cost-effective salp swarm algorithm for resource optimization in cloud computing." International Journal of Applied Metaheuristic Computing (IJAMC) 13.1 (2022): 1-21.

  • Jain, Richa, and Neelam Sharma. "A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing." Cluster Computing (2022): 1-24.

  • Abualigah, Laith, et al. "Salp swarm algorithm: a comprehensive survey." Neural Computing and Applications 32 (2020): 11195-11215.

  • Yang, Xin-She. "Firefly algorithms for multimodal optimization." Stochastic Algorithms: Foundations and Applications: 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings 5. Springer Berlin Heidelberg, 2009.

  • Park, KyoungSoo, and Vivek S. Pai. "CoMon: a mostly-scalable monitoring system for PlanetLab." ACM SIGOPS Operating Systems Review 40.1 (2006): 65-74.

  • Feitelson, Dror G., and Bill Nitzberg. "Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860." Job Scheduling Strategies for Parallel Processing: IPPS'95 Workshop Santa Barbara, CA, USA, April 25, 1995 Proceedings 1. Springer Berlin Heidelberg, 1995.

  • https://aws.amazon.com/ec2/instance-types/processing. Springer, Berlin, Heidelberg, 1995. ( Accessed on 24 September, 2022)

  • Ullman, J. D., NP-complete scheduling problems. Journal of Computer and System sciences, 10(3), 384-393. (1975).

  • Singh, P., Dutta, M., & Aggarwal, N., A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 52, 1-51. (2017).

  • Tsai, C. W., & Rodrigues, J. J., Metaheuristic scheduling for cloud: A survey. IEEE Systems Journal, 8(1), 279-291. (2013).

  • Kalra, M., & Singh, S. A review of metaheuristic scheduling techniques in cloud computing. Egyptian informatics journal, 16(3), 275-295. (2015).

  • Jain, P., & Sharma, S. K. A systematic review of nature inspired load balancing algorithm in heterogeneous cloud computing environment. In 2017 conference on information and communication technology (CICT) (pp. 1-7). IEEE. (2017).

  • Ghomi, E. J., Rahmani, A. M., & Qader, N. N., Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications, 88, 50-71. (2017).

  • Thanka, M. R., Uma Maheswari, P., & Edwin, E. B, An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment. Cluster Computing, 22, 10905-10913.(2019).

  • Ramesh, D., Dey, S., & Bhukya, R, Heuristic and fair-queuing based VM load balancing strategy for cloud data centers: A hybrid approach. Multiagent and Grid Systems, 15(1), 19-38. (2019)

  • Mapetu, J. P. B., Chen, Z., & Kong, L. , Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing. Applied Intelligence, 49, 3308-3330. (2019)

  • Adhikari, M., Nandy, S., & Amgoth, T. , Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. Journal of Network and Computer Applications, 128, 64-77. (2019)

  • Alguliyev, R. M., Imamverdiyev, Y. N., & Abdullayeva, F. J. , PSO-based load balancing method in cloud computing. Automatic Control and Computer Sciences, 53, 45-55. (2019)

  • Hanine, M., & Benlahmar, E. H, A load-balancing approach using an improved simulated annealing algorithm. Journal of Information Processing Systems, 16(1), 132-144. (2020)

  • Kruekaew, B., & Kimpan, W, Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing. International Journal of Computational Intelligence Systems, 13(1), 496-510.. (2020)

  • Zhou, Z., Li, F., Zhu, H., Xie, H., Abawajy, J. H., & Chowdhury, M. U. , An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Computing and Applications, 32, 1531-1541. (2020)

  • Neelima, P., & Reddy, A. R. M, An efficient load balancing system using adaptive dragonfly algorithm in cloud computing. Cluster Computing, 23, 2891-2899. (2020)

  • George, Shelly Shiju, and R. Suji Pramila. "Fractional IWSOA-LB: Fractional Improved Whale Social Optimization Based VM Migration Strategy for Load Balancing in Cloud Computing." International Journal of Wireless Information Networks 30.1 (2023): 58-74.

  • Ramya, K., and Senthilselvi Ayothi. "Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment." Transactions on Emerging Telecommunications Technologies 34.5 (2023): e4760.

  • Abdelmaboud, Abdelzahir, et al. "Quality of service approaches in cloud computing: A systematic mapping study." Journal of Systems and Software 101 (2015): 159-179.

  • Manupati, Vijaya Kumar, et al. "Near optimal process plan selection for multiple jobs in networked based manufacturing using multi-objective evolutionary algorithms." Computers & Industrial Engineering 66.1 (2013): 63-76.

  • Jain, Richa, Neelam Sharma, and Pankaj Jain. "A systematic analysis of nature inspired workflow scheduling algorithm in heterogeneous cloud environment." 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT). IEEE, 2017.

  • Poli, Riccardo, James Kennedy, and Tim Blackwell. "Particle swarm optimization-An overview. Swarm Intelligence. Volume 1, Issue 1." (2007): 33-57.

  • Buyya, Rajkumar, Rajiv Ranjan, and Rodrigo N. Calheiros. "Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities." 2009 international conference on high performance computing & simulation. IEEE, 2009.


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  • A Load Balancing Aware Task Scheduling using Hybrid Firefly Salp Swarm Algorithm in Cloud Computing

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Authors

Pankaj Jain
Department of Computer Science and Engineering, Banasthali Vidyapith, Niwai
Sanjay Kumar Sharma
Department of Computer Science and Engineering, Banasthali Vidyapith, Niwai, India

Abstract


Cloud computing is an evolutionary computational model which provides on-demand scalable and flexible resources by the pay-per-use concept. Due to the flexibility of cloud, several organizations are setting up more data centers and switching their businesses to the cloud technology. These industries need a proper load balancing to ensure the efficient resources utilization, which reduces resource wastage and helps to optimize costs. Optimal resource allocation can be achieved through efficient task scheduling and load-balancing. An efficient scheduling with load-balancing allocates resources in a balanced way and optimizes the quality of service (QoS) parameters. Task migration is the best way to balance the load. This paper hybridizes the Salp Swarm Algorithm (SSA) with the Firefly Algorithm (FFA), named as Hybrid Firefly Salp Swarm Algorithm (HFFSSA). This approach utilizes FFA's operators to enhance the exploitation capability of SSA by functioning as a local search. Further, a load balancing (LB) heuristic is proposed and incorporated with HFFSSA, named as Load Balancing Salp Swarm Algorithm (LBFFSSA). For verification, the presented work is evaluated by two experimental series. First HFFSSA is tested on global benchmark functions, where it shows its superiority over other existing metaheuristic approaches such as Firefly Algorithm (FFA), Grey Wolf Algorithm (GWO), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA). In the second series, the LB-FFSSA is evaluated on real datasets (Planet Lab and NASA) using CloudSim Simulator; again, results outperform similar metaheuristics. The simulation results show that LB-FFSSA significantly reduces makespan and improves resource utilization. Furthermore, the proposed algorithm minimizes the Load imbalance Factor (LIF) by migrating the task from an over utilized virtual machine to an underutilized one. It also shows improvement in waiting time and throughput. Simulation results prove that proposed model improves by an average up to 32.3%, LIF by 50.4%, throughput by 42.1%, resource utilization by 40%, and waiting time by 50%.

Keywords


Cloud Computing, Hybrid Task Scheduling, Firefly (FFA), Salp Swarm Algorithm (SSA), Task Migration, Load Balancing.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F223686