Resource allocation optimization in cloud computing using the whale optimization algorithm

Document Type : Research Paper


1 Deportment of Computer Islamic Azad University Neyshabur Branch, Neyshabur, Iran

2 Iran University of Science and Technology, Tehran, Iran

3 Department of Mathematical Sciences, University of South Africa, UNISA0003, South Africa

4 Deportment of Computer Engineering, Mashhad Branch, Islamic Azad University Mashhad, Iran


Cloud computing is a massively distributed system in which existing resources interact with user-requested tasks to meet their requests. In such a system, the problem of optimizing Resource Allocation and Scheduling (RAS) is vital, because recourse allocation and scheduling deals with the mapping between recourses and user requests and also is responsible for optimal allocating of tasks to available resources. In the cloud environment, a user may face hundreds of computational resources to do his work. Therefore, manually recourse allocation and scheduling are impossible, and having a schedule between user requests and available recourses seems logical. In this paper, we used Whale Optimization Algorithm (WOA) to solve resource allocation and task scheduling problem in cloud computing to have optimal resource allocation and reduce the total runtime of requested services by users. The proposed algorithm is compared with the other existed algorithms. Results indicate the proper performance of the proposed algorithm than other ones.


[1] Q. Zhang LC and B. Raouf, Cloud computing: state-of-the-art and research challenges, J. Int. Serv. Appl. 1
(2010) 7-–18.
[2] AA. Soror, UF. Minhas, A. Aboulnaga, K. Salem, P. Kokosielis and S. Kamath, Deploying database appliances
in the cloud, IEEE Data Eng. Bull. 32 (2009) 13–20.
[3] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris and A. Ho, Xen and the art of virtualization, Proc.
Nineteenth ACM Symp. Oper. Syst. Princ. Bolton Landing, USA, (2003) 164–77.
[4] B. Furht and A. Escalante, Handbook of Cloud Computing, Springer, 2010.
[5] RP. Goldberg, Survey of virtual machine research, IEEE Comput. 7 (1974) 34–45.
[6] P. Rose, Survey of system virtualization techniques, Lisbon, Portugal: Theses (Electrical Engineering and Computer Science) MS non-thesis Research Papers (EECS), 2004.
[7] M. Malekloo, N. Kara and M. E. Barachi, An energy efficient and SLA compliant approach for resource allocation
and consolidation in cloud computing environments, Sustain. Comput. Inf. Syst. 17 (2018) 9–24.
[8] G. Baranwal, D. P. Vidyarthi, Fair multi-attribute combinatorial double Auction model for resource allocation in
cloud computing, J. Syst. Software 108 (2015) 60–76.
[9] T. Ma, Y. Chu, L. Zhao and O. Ankhbayar, Resource allocation and scheduling in cloud computing: Policy and
algorithm, IETE Tech. Rev. 31 (2014) 4–16.
[10] B. Shrimali and H. Patel, Multi-objective optimization oriented policy for performance and energy efficient resource
allocation in cloud environment, J. King Saud Univ. Comput. Inf. Sci. 32(7) (2020) 860–869.
[11] P. Pradhan, P. K. Behera and B. N. B. Ray, Modified round Robin algorithm for resource allocation in cloud
computing, Procedia Comput. Sci. 85 (2016) 878-890.
[12] W. Lin, J. Z. Wang, C. Liang and D. Qi, A Threshold-based dynamic resource allocation scheme for cloud
computing, Procedia Engin. 23 (2011) 695–703.
[13] E. Kheiri, M. G. Arani, R. Kheiri and A. Taghizadeh, An efficient approach based on genetic algorithm for
multi-tenant resource allocation in SaaS applications, Int. J. Software Engin. Appl. 10 (2016) 47–68.
[14] T. Jena and J. R. Mohanty, GA-based customer-conscious resource allocation and task scheduling in multi-cloud
computing, Arab. J. Sci. Engin. 43 (2018) 4115—4130.
[15] L. Boloni and D. Turgut, Value of information based scheduling of cloud computing resources, Future Gen.
Comput. Syst. 71 (2017) 212–220.
[16] H. Hallawi, J. Mehnen and H. He, Multi-capacity combinatorial ordering GA in application to cloud resources
allocation and efficient virtual machines consolidation, Future Gen. Comput. Syst. 69 (2017) 1–10.
[17] P. Samimi, Y. Teimouri and M. Mukhtar, A combinatorial double auction resource allocation model in cloud
computing, Inf. Sci. 357 (2016) 201–216.
[18] Z. Li, T. Chu, I. V. Kolmanovsky, X. Yin and X. Yin, Cloud resource allocation for cloud-based automotive
applications, Mech. 50 (2018) 356–365.
[19] S. T. Maguluri, R. Srikant and L. Ying, Heavy traffic optimal resource allocation algorithms for cloud computing
clusters, Perf. Eval. 81 (2014) 20-39.
[20] T. Xiaoying, H. Dan, G. Yuchun and C. Changjia, Dynamic resource allocation in cloud download service, J.
China Univer. Posts Telecom. 24 (2017) 53–59.
[21] A. B. A. Muthu and S. Enoch, Optimized scheduling and resource allocation using evolutionary algorithms in
cloud environment, Int. J. Intel. Engin. Syst. 2017 (2017) 125-133, .
[22] S. S. SHEULY, S. Bankarusamy, S. Begum and M. Behnam, Resource allocation in industrial cloud computing
using artificial intelligence algorithms, The 13th Scandinavian Conf. Artif. Intel. (2017) 1–9.
[23] M. Ficco, C. Esposito, F. Palmieri and A. Castiglione, A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation, Future Gen. Comput. Syst. 78 (2018) 343–352.
[24] W. HU, K. LI, J. XU and J. XU, Cloud-computing-based resource allocation research on the perspective of improved
ant colony algorithm, Int. Conf. Comput. Sci. Mech. Autom. (2015) 76- 80.
[25] S. Mirjalili and A. Lewis, The whale optimization algorithm, Adv. Eng. Software 95 (2016) 51–67.
[26] M. Shojafar, M. Kardgar, A. R. Hosseinabadi, Sh. Shamshirband and A. Abraham, TETS: A Genetic-based
Scheduler in Cloud Computing to Decrease Energy and Makespan, The 15th Int. Conf. Hybrid Intel. Syst. Chapter
Advances in Intelligent Systems and Computing 420, Seoul, South Korea, Springer, 420 (2016) 103–115.
[27] A. R. Hosseinabadi, H. Siar, Sh. Shamshirband, M. Shojafar, M. H. Nizam and Md. Nasir, Using the gravitational
emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in small
and medium enterprises, Ann. Oper. Res. 229(1) (2015) 451–474.
[28] Sh. Shamshirband, M. Shojafar, A. R. Hosseinabadi, M. Kardgar, M. H. Nizam Md. Nasir and R. Ahmad, OSGA:genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises,
Ann. Oper. Res. 229(1) (2015) 743–758.
[29] A. R. Hosseinabadi, A. B. Farahabadi, M. S. Rostami and A. F. Lateran, Presentation of a new and beneficial
method through problem solving timing of open shop by random algorithm gravitational emulation local search,
Int. J. Comput. Sci. 10 (2013) 745–752.
[30] M. Kim and I. Y. Ko, An efficient resource allocation approach based on a genetic algorithm for composite services
in IoT environments, IEEE Int. Conf. Web Serv. 2015 543–550.
[31] C. W. Tsai, SEIRA: An effective algorithm for IoT resource allocation problem, Comput. Commun. 119 (2018)
[32] E. B. Tirkolaee, A. Goli, M. Bakhshi and I. Mahdavi, Robust multi-trip vehicle routing problem of perishable
products with intermediate depots and time win-dows, Numerical Algebra Cont. Optim. 7 (2017) 417–433.
[33] E. B. Tirkolaee, A. Alinaghian, A. R. Hosseinabadi, M. B. Sasi and A. K. Sangaiah, An improved ant colony
optimization for the multi-trip capacitated arc routing problem, Comput. Elect. Engin. 77 (2019) 457–470.
[34] A. R. Hosseinabadi, N. S. H. Rostami, M. Kardgar, S. S. Mirkamali and A. Abraham, A new efficient approach
for solving the capacitated vehicle routing problem using the gravitational emulation local search algorithm, Appl.
Math. Model. 49 (2017) 663–679.
Volume 12, Special Issue
December 2021
Pages 343-360
  • Receive Date: 23 January 2021
  • Revise Date: 08 April 2021
  • Accept Date: 28 May 2021
  • First Publish Date: 23 June 2021