A Fuzzy based Pathfinder Optimization Technique for Performance-Effective Task Scheduling in Cloud

Document Type : Research Article


1 Department of Computer Science, Shahid Bahonar University of kerman, Kerman, Iran

2 Shahid Bahonar University of Kerman

3 Department of Computer Science ,Shahid Bahonar University,Kerman,Iran


Cloud computing provides a shared pool of resources in a distributed environment and supports the features of utility-based computing. Task scheduling is a largely studied research topic in cloud computing which targets utilizing cloud resources for tasks by considering the objectives specified in QoS. Optimal task scheduling is an NP-hard problem that is time-consuming to solve with precise methods and depends on many factors, such as completion time, latency, cost, energy consumption, throughput, and load balance on the machines. Therefore, using meta-heuristic algorithms is a good selection. This paper uses the Pathfinder optimization Algorithm (PFA)  for the task scheduling problem; although when the dimension of a problem is extremely increased, the performance of this algorithm decreases. In the last iterations, fluctuation rate (A) and vibration vector (ε) converg to 0, and finding a new solution is impossible. We used fuzzy logic to overcome this shortcoming and named the new algorithm Fuzzy-PFA (FPFA). In this paper, makespan, energy consumption, throughput, tardiness, and the degree of imbalance are considered as objective functions. Our goal is to minimize the makespan, energy consumption, tardiness, and degree of imbalance while maximizing throughput. Finally, different algorithms such as Firefly Algorithm (FA), Bat Algorithm (BA), Particle Swarm Optimization (PSO), and PFA are used for comparison. The experimental results indicate that the proposed scheduling algorithm can improve up to 34.2%, 16.2%, 15.9%, and 3.5% the objective function in comparison with FA, BA, PSO, and PFA, respectively.


Main Subjects

[1] G. Ismayilov, H.R. Topcuoglu, Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing, Future Generation Computer Systems, 102 (2020) 307-322.
[2] P.S. Rawat, P. Dimri, P. Gupta, G.P. Saroha, Resource provisioning in scalable cloud using bio-inspired artificial neural network model, Applied Soft Computing, 99 (2021) 106876.
[3] A. Ramegowda, J. Agarkhed, S.R. Patil, Adaptive task scheduling method in multi-tenant cloud computing, International Journal of Information Technology, 12(4) (2020) 1093-1102.
[4] D.K. Shukla, D. Kumar, D.S. Kushwaha, Task scheduling to reduce energy consumption and makespan of cloud computing using NSGA-II, Materials Today: Proceedings,  (2021).
[5] W. Shu, K. Cai, N.N. Xiong, Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing, Future Generation Computer Systems, 124 (2021) 12-20.
[6] E.H. Houssein, A.G. Gad, Y.M. Wazery, P.N. Suganthan, Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends, Swarm and Evolutionary Computation, 62 (2021) 100841.
[7] M. Adhikari, S. Nandy, T. Amgoth, Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud, Journal of Network and Computer Applications, 128 (2019) 64-77.
[8] H. Yapici, N. Cetinkaya, A new meta-heuristic optimizer: Pathfinder algorithm, Applied Soft Computing, 78 (2019) 545-568.
[9] C.R. Miranda, F.P.V.d. Campos, M.V. Ribeiro, Energy reliability in macro base stations: A feasible solution based on a type-1 Mamdani fuzzy system, Electric Power Systems Research, 195 (2021) 107126.
[10] Y. Zhang, Q. Hu, Z. Meng, A. Ralescu, Fuzzy dynamic timetable scheduling for public transit, Fuzzy Sets and Systems, 395 (2020) 235-253.
[11] C.R. Vela, S. Afsar, J.J. Palacios, I. González-Rodríguez, J. Puente, Evolutionary tabu search for flexible due-date satisfaction in fuzzy job shop scheduling, Computers & Operations Research, 119 (2020) 104931.
[12] N. Mansouri, B. Mohammad Hasani Zade, M.M. Javidi, Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory, Computers & Industrial Engineering, 130 (2019) 597-633.
[13] R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F.D. Rose, R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Softw. Pract. Exper., 41(1) (2011) 23–50.
[14] Z. Jia, J. Yan, J.Y.T. Leung, K. Li, H. Chen, Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities, Applied Soft Computing, 75 (2019) 548-561.
[15] H.S. Ali, R.R. Rout, P. Parimi, S.K. Das, Real-Time Task Scheduling in Fog-Cloud Computing Framework for IoT Applications: A Fuzzy Logic based Approach, in:  2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2021, pp. 556-564.
[16] M. Emin Baysal, A. Sarucan, K. Büyüközkan, O. Engin, Distributed Fuzzy Permutation Flow Shop Scheduling Problem: A Bee Colony Algorithm, in: C. Kahraman, S. Cevik Onar, B. Oztaysi, I.U. Sari, S. Cebi, A.C. Tolga (Eds.) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions, Springer International Publishing, Cham, 2021, pp. 1440-1446.
[17] K. Chrysafiadi, A Fuzzy Task Scheduling Method, in: G.A. Tsihrintzis, M. Virvou (Eds.) Advances in Core Computer Science-Based Technologies: Papers in Honor of Professor Nikolaos Alexandris, Springer International Publishing, Cham, 2021, pp. 305-323.
[18] M. Melnik, D. Nasonov, Workflow scheduling using Neural Networks and Reinforcement Learning, Procedia Computer Science, 156 (2019) 29-36.
[19] M. Sharma, R. Garg, An artificial neural network based approach for energy efficient task scheduling in cloud data centers, Sustainable Computing: Informatics and Systems, 26 (2020) 100373.
[20] J.P.B. Mapetu, Z. Chen, L. Kong, Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing, Applied Intelligence, 49(9) (2019) 3308-3330.
[21] R. Medara, R.S. Singh, Amit, Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization, Simulation Modelling Practice and Theory, 110 (2021) 102323.
[22] W. Chen, E. Deelman, Workflowsim: A toolkit for simulating scientific workflows in distributed environments, in:  2012 IEEE 8th international conference on E-science, IEEE, 2012, pp. 1-8.
[23] S.A. Alsaidy, A.D. Abbood, M.A. Sahib, Heuristic initialization of PSO task scheduling algorithm in cloud computing, Journal of King Saud University - Computer and Information Sciences,  (2020).
[24] M.S. Sanaj, P.M. Joe Prathap, An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment, Materials Today: Proceedings, 37 (2021) 3199-3208.
[25] Y. Gu, C. Budati, Energy-aware workflow scheduling and optimization in clouds using bat algorithm, Future Generation Computer Systems, 113 (2020) 106-112.
[26] M. Adhikari, T. Amgoth, S.N. Srirama, Multi-objective scheduling strategy for scientific workflows in cloud environment: A Firefly-based approach, Applied Soft Computing, 93 (2020) 106411.
[27] S. Su, H. Yu, Minimizing tardiness in data aggregation scheduling with due date consideration for single-hop wireless sensor networks, Wireless Networks, 21(4) (2015) 1259-1273.
[28] D.E. Akyol, G.M. Bayhan, Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach, The International Journal of Advanced Manufacturing Technology, 37(5-6) (2008) 576-588.
[29] D. Alboaneen, H. Tianfield, Y. Zhang, B. Pranggono, A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers, Future Generation Computer Systems, 115 (2021) 201-212.
[30] L.A. Zadeh, Fuzzy sets, in:  Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, World Scientific, 1996, pp. 394-432.
[31] L.-X. Wang, Adaptive fuzzy systems and control: design and stability analysis, Prentice-Hall, Inc., 1994.
[32] L.S. Riza, C. Bergmeir, F. Herrera, J.M. Benítez, frbs: Fuzzy rule-based systems for classification and regression in R, Journal of statistical software, 65(6) (2015) 1-30.
[33] A. Arslan, M. Kaya, Determination of fuzzy logic membership functions using genetic algorithms, Fuzzy Sets and Systems, 118(2) (2001) 297-306.
[34] N.E. Nawa, T. Furuhashi, Fuzzy system parameters discovery by bacterial evolutionary algorithm, IEEE Transactions on Fuzzy Systems, 7(5) (1999) 608-616.
[35] M. Babanezhad, A.T. Nakhjiri, A. Marjani, M. Rezakazemi, S. Shirazian, Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature, Scientific Reports, 10(1) (2020) 1-13.
[36] H.N. Ghafil, K. Jármai, Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications, Applied Soft Computing, 93 (2020) 106392.