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A Hybrid-based Multi-criteria Approach, for Efficient Workflow Tasks Assignment in Cloud Computing Environment

by J. Kok Konjaang, Emmanuel Bugingo, David Sanka Laar, Elisha A. Ayakpagi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 101
Year of Publication: 2026
Authors: J. Kok Konjaang, Emmanuel Bugingo, David Sanka Laar, Elisha A. Ayakpagi
10.5120/ijca08bc2f902b55

J. Kok Konjaang, Emmanuel Bugingo, David Sanka Laar, Elisha A. Ayakpagi . A Hybrid-based Multi-criteria Approach, for Efficient Workflow Tasks Assignment in Cloud Computing Environment. International Journal of Computer Applications. 187, 101 ( May 2026), 24-40. DOI=10.5120/ijca08bc2f902b55

@article{ 10.5120/ijca08bc2f902b55,
author = { J. Kok Konjaang, Emmanuel Bugingo, David Sanka Laar, Elisha A. Ayakpagi },
title = { A Hybrid-based Multi-criteria Approach, for Efficient Workflow Tasks Assignment in Cloud Computing Environment },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 101 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 24-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number101/a-hybrid-based-multi-criteria-approach-for-efficient-workflow-tasks-assignment-in-cloud-computing-environment/ },
doi = { 10.5120/ijca08bc2f902b55 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-17T02:28:57.210849+05:30
%A J. Kok Konjaang
%A Emmanuel Bugingo
%A David Sanka Laar
%A Elisha A. Ayakpagi
%T A Hybrid-based Multi-criteria Approach, for Efficient Workflow Tasks Assignment in Cloud Computing Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 101
%P 24-40
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing has emerged as a distribution hub, that offers ubiquitous access, to a shared pool of cloud resources for modeling, executing, and performing big data analyses of businesses as well as scientific workflow applications. The workflow scheduling process is to allocate resources for users’ tasks in a way that satisfies the constraints while optimizing some objectives. One of the most challenging problems in workflow scheduling is determining an optimal number of VMs configurations that can schedule streaming tasks to speed up the execution time while incurring a relatively low cost and energy consumption. Several methodologies have been proposed with the aim of generating good schedules to improve workflow execution. However, most of these existing methodologies on workflow scheduling are improving on relatively simple objectives (cost and makespan) without looking at the bigger problem (VM provisioning), which is mostly the prime causes of high execution cost, makespan, and energy consumption. In this paper, we proposed a novel hybrid-based multi-criteria deadline-constrained scheduler, known as MOW-PSO. MOW-PSO is a fusion of PSO and MOWOS, aiming to determine an optimal number of VMs configuration for a proper task to VM mapping, that finds a suitable solution to the three important yet, conflicting scheduling objectives; energy consumption, execution makespan, and cost without violating user-defined deadlines and budget constraints. Our approach is evaluated using five different representative workflows with four different workload patterns through WorkflowSim. The results prove the effectiveness of our proposed approach over the state-of-the-art algorithms.

References
  1. J. K. Konjaang, Optimizing workflow scheduling for high-performance computing, Ph.D. thesis, Univer sity College Dublin. School of Computer Science (2025).
  2. G. Di Luna, R. Baldoni, Non trivial computations in anonymous dynamic networks, in: 19th International Conference on Principles of Distributed Systems (OPODIS 2015), Schloss Dagstuhl-LeibnizZentrum fuer Informatik, 2016.
  3. F. Kuhn, R. Oshman, Dynamic networks: models and algorithms, ACM SIGACT News 42 (1) (2011) 465 82–96.
  4. A. Casteigts, P. Flocchini, W. Quattrociocchi, N. Santoro, Time-varying graphs and dynamic networks, International Journal of Parallel, Emergent and Distributed Systems 27 (5) (2012).
  5. W. Ahmad, B. Alam, S. Ahuja, S. Malik, A dynamic vm provisioning and de-provisioning based costefficient deadline-aware scheduling algorithm for big data workflow applications in a cloud environment, Cluster Computing 24 (1) (2021) 249– 278.
  6. M. A. Rodriguez, R. Buyya, Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds, IEEE transactions on cloud computing 2 (2) (2014) 222–235.
  7. M. Mao, M. Humphrey, Auto-scaling to minimize cost and meet application deadlines in cloud workflows, in: SC’11: Proceedings of 2011 International Conference for High Performance Computing, Networking, and Analysis, IEEE, 2011, pp. 1–12.
  8. J. Ndamlabin Mboula, V. Kamla, M. Hilman, C. Tayou Djamegni, Energy-efficient workflow scheduling based on workflow structures under deadline and budget constraints in the cloud, arXiv e-prints (2022) arXiv–2201.
  9. Casas, J. Taheri, R. Ranjan, L. Wang, A. Y. Zomaya, A balanced scheduler with data reuse and replication for scientific workflows in cloud computing Future Generation Computer Systems 74 (2017) 168–178
  10. G. Singh, C. Kesselman, E. Deelman, Application- level resource provisioning on the grid, in: 2006 Secon IEEE International Conference on e-Science and Grid Computing (e-Science’06), IEEE, 2006, pp. 83–83.
  11. S. Chaisiri, B.-S. Lee, D. Niyato, Optimization of resource provisioning cost in cloud computing, IEEE transactions on services Computing 5 (2) (2011) 164–177
  12. M. Al-Ayyoub, Y. Jararweh, M. Daraghmeh, Q. Althebyan, Multi-agent based dynamic resource pro visioning and monitoring for cloud computing systems infrastructure, Cluster Computing 18 (2) (2015) 919–932.
  13. X. Li, Z. Cai, Elastic resource provisioning for cloud workflow applications, IEEE Transactions on Automation Science and Engineering 14 (2) (2015) 1195–1210
  14. J. E. Ndamlabin Mboula, V. C. Kamla, C. Tayou Djam´egni, Dynamic provisioning with structure inspired selection and limitation of vms based cost- time efficient workflow scheduling in the cloud, Cluster Computing 24 (3) (2021) 2697–2721.
  15. A. Beloglazov, R. Buyya, Y. C. Lee, A. Zomaya, A taxonomy and survey of energy-efficient data centers and cloud computing systems, in: Advances in computers, Vol. 82, Elsevier, 2011, pp. 47–111.495
  16. R. Buyya, Introduction to the ieee transactions on cloud computing, IEEE Transactions on cloud computing 1 (1) (2013) 3–21.
  17. C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, M. A. Kozuch, Heterogeneity and dynamicity of clouds at scale: Google trace analysis, in: Proceedings of the third ACM symposium on cloud computing, 2012, pp. 1–13.500
  18. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, R. Buyya, Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and experience 41 (1) (2011) 23– 50.
  19. M. A. Rodriguez, R. Buyya, Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms, Future Generation Computer Systems 79 (2018) 739–750.
  20. E. Deelman, G. Singh, M. Livny, B. Berriman, J. Good, The cost of doing science on the cloud: the montage example, in: SC’08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, Ieee, 2008, pp. 1–12.
  21. K. R. Jackson, L. Ramakrishnan, K. Muriki, S. Canon, S. Cholia, J. Shalf, H. J. Wasserman, N. J. Wright, Performance analysis of high performance computing applications on the amazon web services cloud, in: 2010 IEEE second international conference on cloud computing technology and science, IEEE, 2010, pp. 159–168.
  22. Y. Gao, S. Zhang, J. Zhou, A hybrid algorithm for multi-objective scientific workflow scheduling in iaas cloud, IEEE Access 7 (2019) 125783–125795.
  23. R. Madduri, K. Chard, R. Chard, L. Lacinski, A. Rodriguez, D. Sulakhe, D. Kelly, U. Dave, I. Foster, The globus galaxies platform: delivering science gateways as a service, Concurrency and Computation: Practice and Experience 27 (16) (2015) 4344–4360.
  24. J. K. Konjaang, J. Murphy, L. Murphy, Energy- efficient virtual-machine mapping algorithm (evima) for workflow tasks with deadlines in a cloud environment, Journal of Network and Computer Applications 203 (2022) 103400.
  25. V. Singh, I. Gupta, P. K. Jana, An energy efficient algorithm for workflow scheduling in iaas cloud, Journal of Grid Computing 18 (3) (2020) 357–376.
  26. J. Hartmanis, Computers and intractability: a guide to the theory of np-completeness (michael r. garey and david s. johnson), Siam Review 24 (1) (1982) 90
  27. S. Abrishami, M. Naghibzadeh, D. H. Epema, Deadline- constrained workflow scheduling algorithms for infrastructure as a service clouds, Future generation computer systems 29 (1) (2013) 158–169.
  28. X. Ye, S. Liu, Y. Yin, Y. Jin, User-oriented many- objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm, Knowledge-Based Systems 135 (2017) 113–124.
  29. K. Nishant, P. Sharma, V. Krishna, C. Gupta, K. P. Singh, R. Rastogi, et al., Load balancing of nodes in cloud using ant colony optimization, in: 2012 UKSim 14th international conference on computer modelling and simulation, IEEE, 2012, pp. 3–8.
  30. L. Singh, S. Singh, A genetic algorithm for scheduling workflow applications in unreliable cloud environment, in: International conference on security in computer networks and distributed systems, Springer, 2014, pp. 139–150.
  31. G. Yao, Y. Ding, L. Ren, K. Hao, L. Chen, An immune system-inspired rescheduling algorithm for workflow in cloud systems, Knowledge-Based Systems 99 (2016) 39–50.
  32. Y. C. Lee, H. Han, A. Y. Zomaya, M. Yousif, Resource-efficient workflow scheduling in clouds, Knowledge-Based Systems 80 (2015) 153–162.
  33. H. Zhang, J. Shi, B. Deng, G. Jia, G. Han, L. Shu, Mcte: minimizes task completion time and execution cost to optimize scheduling performance for smart grid cloud, IEEE Access 7 (2019) 134793– 134803.
  34. A. Choudhary, I. Gupta, V. Singh, P. K. Jana, A gsa based hybrid algorithm for bi-objective workflow scheduling in cloud computing, Future Generation Computer Systems 83 (2018) 14–26.
  35. J. Kakkottakath Valappil Thekkepuryil, D. P. Suseelan, P. M. Keerikkattil, An effective meta- heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment, Cluster Computing 24 (3) (2021) 2367–2384.545
  36. M. Kalra, S. Singh, Multi-objective energy aware scheduling of deadline constrained workflows in clouds using hybrid approach, Wireless Personal Communications 116 (3) (2021) 1743–1764.
  37. J. K. Konjaang, L. Xu, Multi-objective workflow optimization strategy (mowos) for cloud computing, Journal of Cloud Computing 10 (1) (2021) 1–19.
  38. Y. Xu, K. Li, L. He, L. Zhang, K. Li, A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems, IEEE Transactions on parallel and distributed systems 26 (12) (2014) 3208–3222.
  39. H. M. Fard, Multi-objective scheduling for scientific workflow applications in grid and cloud infrastructures, Ph.D. thesis, University of Innsbruck (2015).
  40. C. Vecchiola, R. N. Calheiros, D. Karunamoorthy, R. Buyya, Deadline-driven provisioning of resources for scientific applications in hybrid clouds with aneka, Future Generation Computer Systems 28 (1) (2012) 58–65.
  41. B. Javadi, J. Abawajy, R. O. Sinnott, Hybrid cloud resource provisioning policy in the presence of resource failures, in: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, IEEE, 2012, pp. 10–17.
  42. C. Li, L. Y. Li, Optimal resource provisioning for cloud computing environment, The Journal of Super computing 62 (2) (2012) 989–1022.
  43. S. He, L. Guo, Y. Guo, C. Wu, M. Ghanem, R. Han, Elastic application container: A lightweight approach for cloud resource provisioning, in: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, IEEE, 2012, pp. 15–22.
  44. L. Zhang, Z. Li, C. Wu, Dynamic resource provisioning in cloud computing: A randomized auction approach, in: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, IEEE, 2014, pp. 33–441.
  45. H. M. Fard, R. Prodan, J. J. D. Barrionuevo, T. Fahringer, A multi-objective approach for workflow scheduling in heterogeneous environments, in: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), IEEE, 2012, pp. 300–309.
  46. R. A. Haidri, C. P. Katti, P. C. Saxena, Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing, Journal of King Saud University- Computer and Information Sciences 32 (6) (2020) 666–683.
  47. F. Abazari, M. Analoui, H. Takabi, S. Fu, Mows: multi-objective workflow scheduling in cloud computing based on heuristic algorithm, Simulation Modelling Practice and Theory 93 (2019) 119–132.
  48. N. Rehani, R. Garg, Meta-heuristic based reliable and green workflow scheduling in cloud computing, International Journal of System Assurance Engineering and Management 9 (4) (2018) 811–820.
  49. H. Y. Shishido, J. C. Estrella, C. F. M. Toledo, M. S. Arantes, Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds, Computers & Electrical Engineering 69 (2018) 378–394.
  50. M. Melnik, T. Trofimenko, Polyrhythmic harmony search for workflow scheduling, Procedia Computer Science 66 (2015) 468–476.
  51. Z. Wu, Z. Ni, L. Gu, X. Liu, A revised discrete particle swarm optimization for cloud workflow scheduling, in: 2010 international conference on computational intelligence and security, IEEE, 2010, pp. 184–188.
  52. Z. Pooranian, M. Shojafar, J. H. Abawajy, A. Abraham, An efficient meta-heuristic algorithm for grid computing, Journal of Combinatorial Optimization 30 (3) (2015) 413–434.
  53. Z. Beheshti, S. M. H. Shamsuddin, A review of population-based meta-heuristic algorithms, Int. J. Adv. Soft Comput. Appl 5 (1) (2013) 1–35.
  54. S. H. H. Madni, M. S. Abd Latiff, M. Abdullahi, S. M. Abdulhamid, M. J. Usman, Performance comparison of heuristic algorithms for task scheduling in iaas cloud computing environment, PloS one 12 (5) (2017) e0176321.
  55. N. Soltani, B. Soleimani, B. Barekatain, Heuristic algorithms for task scheduling in cloud computing: A survey., International Journal of Computer Network & Information Security 9 (8).
  56. M. A. Rodriguez, R. Buyya, A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments, Concurrency and Computation: Practice and Experience 29 (8) (2017) e4041.
  57. G. Dhiman, Ssc: A hybrid nature-inspired meta- heuristic optimization algorithm for engineering applications, Knowledge-Based Systems 222 (2021) 106926.
  58. J. K. Konjaang, L. Xu, Meta-heuristic approaches for effective scheduling in infrastructure as a service cloud: a systematic review, Journal of Network and Systems Management 29 (2) (2021) 1–57.
  59. Y. Wu, A survey on population-based meta-heuristic algorithms for motion planning of aircraft, Swarm and Evolutionary Computation 62 (2021) 100844.
  60. A. A. Nasr, N. A. El-Bahnasawy, G. Attiya, A. El- Sayed, A new online scheduling approach for enhancing qos in cloud, Future Computing and Informatics Journal 3 (2) (2018) 424–435.
  61. X.-H. Hu, J.-C. Ouyang, Z.-H. Yang, Z.-H. Chen, An ipso algorithm for grid task scheduling based on satisfaction rate, in: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, Vol. 1, IEEE, 2009, pp. 262–265.
  62. W.-N. Chen, J. Zhang, An ant colony optimization approach to a grid workflow scheduling problem with various qos requirements, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39 (1) (2008) 29–43.
  63. J.-J. Liang, P. N. Suganthan, Dynamic multi-swarm particle swarm optimizer, in: Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., IEEE, 2005, pp. 124–129.
  64. A. M. Manasrah, H. Ba Ali, Workflow scheduling using hybrid ga-pso algorithm in cloud computing, Wireless Communications and Mobile Computing 2018.
  65. V. Arabnejad, K. Bubendorfer, B. Ng, Budget and deadline aware e-science workflow scheduling in clouds, IEEE Transactions on Parallel and Distributed systems 30 (1) (2018) 29–44.
  66. V. Singh, I. Gupta, P. K. Jana, A novel cost- efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources, Future Generation Computer Systems 79 (2018) 95–110.
  67. G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, K. Vahi, Characterizing and profiling scientific workflows, Future generation computer systems 29 (3) (2013) 682–692.
  68. effective and low-complexity task scheduling for het erogeneous computing, IEEE transactions on parallel and distributed systems 13 (3) (2002) 260–274.
  69. S. Elmougy, S. Sarhan, M. Joundy, A novel hybrid of shortest job first and round robin with dynamic variable quantum time task scheduling technique, Journal of Cloud computing 6 (1) (2017) 1–12.
  70. M. H. Shirvani, A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems, Engineering Applications of Artificial Intelligence 90 (2020) 103501.
  71. N. Arora, R. K. Banyal, A particle grey wolf hybrid algorithm for workflow scheduling in cloud computing, Wireless Personal Communications 122 (4) (2022) 3313–3345.
  72. X. Zhou, G. Zhang, J. Sun, J. Zhou, T. Wei, S. Hu, Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft, Future Generation Computer Systems 93 (2019) 278–289.
  73. J. Mboula, V. Kamla, M. Hilman, C. T. Djamegni, Energy-efficient workflow scheduling based on workflow structures under deadline and budget constraints in the cloud, arXiv preprint arXiv:2201.05429.
  74. O. Michail, I. Chatzigiannakis, P. G. Spirakis, Naming and counting in anonymous unknown dynamic networks, in: Symposium on Self- Stabilizing Systems, Springer, 2013, pp. 281–295.
  75. J. E. N. Mboula, V. C. Kamla, C. T. Djamegni, Cost- time trade-off efficient workflow scheduling in cloud, Simulation Modelling Practice and Theory 103 (2020) 102107.
  76. W.-J. Wang, Y.-S. Chang, W.-T. Lo, Y.-K. Lee, Adaptive scheduling for parallel tasks with qos satis faction for hybrid cloud environments, The Journal of Supercomputing 66 (2013) 783–811.
  77. R. Garg, M. Mittal, L. H. Son, Reliability and energy efficient workflow scheduling in cloud environment, Cluster Computing 22 (4) (2019) 1283–1297.
  78. D. Subramoney, C. N. Nyirenda, Multi-swarm pso algorithm for static workflow scheduling in cloud- fog environments, IEEe Access 10 (2022) 117199– 117214.
  79. G. B. Berriman, E. Deelman, J. C. Good, J. C. Jacob, D. S. Katz, C. Kesselman, A. C. Laity, T. A. Prince, G. Singh, M.-H. Su, Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand, in: Optimizing scientific return for astronomy through information technologies, Vol. 5493, SPIE, 2004, pp. 221–232.
  80. S. Yassir, Z. Mostapha, T. Claude, Workflow scheduling issues and techniques in cloud computing: A systematic literature review, in: International Conference of Cloud Computing Technologies and Applications, Springer, 2017, pp. 241–263.
  81. R. Graves, T. H. Jordan, S. Callaghan, E. Deelman, E. Field, G. Juve, C. Kesselman, P. Maechling, G. Mehta, K. Milner, et al., Cybershake: A physics- based seismic hazard model for southern california, Pure and Applied Geophysics 168 (3) (2011) 367– 381.
  82. S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, K. Vahi, Characterization of scientific workflows, in: 2008 third workshop on workflows in support of large-scale science, IEEE, 2008, pp. 1–10.
  83. S. Saeedi, R. Khorsand, S. G. Bidgoli, M. Ramezanpour, Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing, Computers & Industrial Engineering 147 (2020) 106649.
  84. O. Ghandour, S. El Kafhali, M. Hanini, Computing resources scalability performance analysis in cloud computing data center, Journal of Grid Computing 21 (4) (2023) 61.
  85. M. R. Palankar, A. Iamnitchi, M. Ripeanu, S. Garfinkel, Amazon s3 for science grids: a viable solution?, in: Proceedings of the 2008 international workshop on Data-aware distributed computing, 2008, pp.55–64.
  86. J. Sahni, D. P. Vidyarthi, A cost-effective deadline- constrained dynamic scheduling algorithm for scientific workflows in a cloud environment, IEEE Transactions on Cloud Computing 6 (1) (2015) 2– 18.
  87. M. Adhikari, T. Amgoth, An intelligent water drops- based workflow scheduling for iaas cloud, Applied Soft Computing 77 (2019) 547–566.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Heuristics Meta-heuristics Hybrid VM Provisioning Workflows Scheduling

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