Abstract
Cloud computing has now become the most effective platform for providing elastic and on-demand provisioning of high performance heterogeneous and homogeneous computing services basis on pay-per-use in the field high performance computing world. Task scheduling in cloud computing devotes researchers' attention to provide the optimal solution to this NP-Complete problem. An optimized task scheduling algorithm optimizes the cloud system's performance and generates the maximum profit for the cloud service provider. To overcome this issue in cloud computing, Authors developed a hybrid multi-faceted task scheduling algorithm in this research work. The proposed algorithm exploited the features of standard particle swarm optimization (PSO) and Ant Colony Optimization (ACO) technique. The PSO technique provides the best global optimal solution, whereas ACO offers the best local solution. To validate the results of the developed algorithm, performed a comparison of the makespan, cost, and resource utilization rate parameters against the well-known exiting four algorithms for the computer-generated tasks set in the cloud environment through a simulation experiment. The comparison results showed that the proposed algorithm reduces the makespan time and computation cost as well as increases resource utilization rate.
Similar content being viewed by others
References
Abdi A, Zarandi HR (2019) A meta heuristic-based task scheduling and mapping method to optimize main design challenges of heterogeneous multiprocessor embedded systems. Microelectron J 87:1–11
Ahmadian MM, Salehipour A, Cheng TCE (2020) A meta-heuristic to solve the just-in-time job-shop scheduling problem. Eur J Oper Res 288(1):14–29
Alarifi A, Dubey K, Amoon M, Altameem T, Abd El-Samie FE, Altameem A, Sharma SC, Nasr AA (2020) Energy-efficient hybrid framework for green cloud computing. IEEE Access 8:115356–115369
Alsaidy SA, Abbood A.D, Sahib MA (2020) Heuristic Initialization of PSO Task Scheduling Algorithm in Cloud Computing. Journal of King Saud University-Computer and Information Sciences.
Bansal M, Malik SK (2020) A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing. Sustain Comput Inf Syst 28:100429
Buyya, R., Ranjan, R. and Calheiros, R.N., 2009, June. Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities. In 2009 international conference on high performance computing & simulation. IEEE. (pp. 1–11)
Cheng B (2012) Hierarchical cloud service workflow scheduling optimization schema using heuristic generic algorithm. Przeglad Elektrotechniczny 88(2012):92–95
Dasgupta K, Mandal B, Dutta P, Mandal JK, Dam S (2013) A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technol 10:340–347
Deshpande P, Sharma SC, Peddoju SK, Abraham A (2018) Security and service assurance issues in cloud environment. Int J Syst Assur Eng Manag 9(1):194–207
Deshpande P (2020). Cloud of everything (CLeT): the next-generation computing paradigm. In Computing in Engineering and Technology . Springer, Singapore. (pp. 207–214)
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Dubey K, Kumar M, Sharma SC (2018) Modified HEFT algorithm for task scheduling in cloud environment. Procedia Comput Sci 125:725–732
Dubey K, Shams MY, Sharma SC, Alarifi A, Amoon M, Nasr AA (2019) A management system for servicing multi-organizations on community cloud model in secure cloud environment. IEEE Access 7:159535–159546
Dubey K, Kumar M, Chandra MA (2015) A priority-based job scheduling algorithm using IBA and EASY algorithm for cloud metaschedular. In 2015 International Conference on Advances in Computer Engineering and Applications (pp. 66–70). IEEE.
Dubey K, Sharma SC, Nasr AA (2020). A Simulated Annealing based Energy-Efficient VM Placement Policy in Cloud Computing. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1–5). IEEE.
Dubey K, Sharma SC (2020) An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework, J King Saud Univ – Compu. Inf Sci. https://doi.org/10.1016/j.jksuci.2020.11.001
Guo P, Liu M, Xue Z (2018) A PSO-Based Energy-Efficient Fault-Tolerant Static Scheduling Algorithm for Real-Time Tasks in Clouds. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 2537–2541). IEEE.
He Z, Dong J, Li Z, Guo W (2020) Research on Task Scheduling Strategy Optimization Based on ACO in Cloud Computing Environment. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 1615–1619). IEEE.
Hosseinioun P, Kheirabadi M, Tabbakh SRK, Ghaemi R (2020) A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm. J Parallel Distrib Comput 143:88–96
Jacob TP, Pradeep K (2019) A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization. Wireless Pers Commun 109(1):315–331
Kaleeswaran A, Ramasamy V, Vivekanandan P (2013) Dynamic scheduling of data using genetic algorithm in cloud computing. Int J Adv Eng Technol 5(2):327
Kennedy J, Eberhart R (1995) Particle swarms optimization. In: International Conference on neural networks, IEEE (1995). pp. 1942–1948
Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–21
Khan S, Sharma N (2013) Ant colony optimization for effective load balancing in cloud computing. Int J Emerg Trends Technol Comput Sci (IJETTCS) 2(6):72–82
Kumar M, Sharma SC (2018) PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain Comput Inf Syst 19:147–164
Lin W, Wang W, Wu W, Pang X, Liu B, Zhang Y (2018) A heuristic task scheduling algorithm based on server power efficiency model in cloud environments. Sustain Comput Inf Syst 20:56–65
Liu CY, Zou CM, Wu P (2014) A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. In 2014 13th Internassstional symposium on distributed computing and applications to business, engineering and science. IEEE (pp. 68–72)
Miao Z, Yong P, Mei Y, Quanjun Y, Xu X (2020) A discrete PSO-based static load balancing algorithm for distributed simulations in a cloud environment. Futur Gener Comput Syst 115:497–516
Nasr AA, Dubey K, El-Bahnasawy NA, Sharma SC, Attiya G, El-Sayed A (2019) HPFE: a new secure framework for serving multi-users with multi-tasks in public cloud without violating SLA. Neural Comput Appl 32(11):1–21
Pandey VC, Peddoju SK, Deshpande PS (2018) A statistical and distributed packet filter against DDoS attacks in Cloud environment. Sādhanā 43(3):1–9
Pradhan A, Kishoro Bisoy S (2020) A Novel Load Balancing Technique for Cloud Computing Platform based on PSO. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.10.016
Pradhan A, Bisoy SK, Das A (2021). A Survey on PSO Based Meta-Heuristic Scheduling Mechanism in Cloud Computing Environment. Journal of King Saud University-Computer and Information Sciences.
Sharma M, Garg R (2020) HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers. Eng Sci Technol Int J 23(1):211–224
Sreenivasulu G, Paramasivam I (2020). Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing. Evolutionary Intelligence, pp.1–8.
Srichandan S, Kumar TA, Bibhudatta S (2018) Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Comput Inform J 3(2):210–230
Teschemacher U, Reinhart G (2016) Enhancing constraint propagation in ACO-based schedulers for solving the job shop scheduling problem. Procedia CIRP 41:443–447
Tsai HC (2017) Unified particle swarm delivers high efficiency to particle swarm optimization. Appl Soft Comput 55:371–383
Umarani Srikanth G, Maheswari VU, Shanthi P, Siromoney A (2012) Tasks scheduling using ant colony optimization. J Comput Sci 8(8):1314–1320
Valdez F, Vazquez JC, Melin P, Castillo O (2017) Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution. Appl Soft Comput 52:1070–1083
Zhang H, Xie J, Ge J, Lu W, Zong B (2018) An entropy-based PSO for DAR task scheduling problem. Appl Soft Comput 73:862–873
Zhang X, Zhang D, Zheng W, Chen J (2019) An enhanced priority-based scheduling heuristic for DAG applications with temporal unpredictability in task execution and data transmission. Futur Gener Comput Syst 100:428–439
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors whose names are given in this article certify that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dubey, K., Sharma, S.C. A hybrid multi-faceted task scheduling algorithm for cloud computing environment. Int J Syst Assur Eng Manag 14 (Suppl 3), 774–788 (2023). https://doi.org/10.1007/s13198-021-01084-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01084-0