Exploring Virtual Machine Scheduling Algorithms:  A Meta-Analysis

Salman Mahmood; Nor Adnan Yahaya

doi:10.33317/ssurj.561

Authors

Salman Mahmood Malaysia University of Science and Technology
Nor Adnan Yahaya Malaysia University of Science and Technology

DOI:

https://doi.org/10.33317/ssurj.561

Keywords:

Energy efficiency, HHeuristics-based scheduling, Load balancing, Machine learning-based scheduling, Optimization-based scheduling, Performance optimization, Quality of Service (QoS), Resource management, Resource utilization, Scalability, Scheduling algorithms, Virtual machines (VMs)

Abstract

This review paper provides a comprehensive assessment of scheduling methods for cloud computing, with an emphasis on optimizing resource allocation in cloud computing systems. The PRISMA methodology was utilized to identify 2,487 articles for this comprehensive review of scheduling methods in cloud computing systems. Following a rigorous screening process, 30 papers published between 2018 and 2023 were selected for inclusion in the review. These papers were analyzed in-depth to provide an extensive overview of the current state of scheduling methods in cloud computing, along with the challenges and opportunities for improving resource allocation. The review evaluates various scheduling approaches, including heuristics, optimization, and machine learning-based methods, discussing their strengths and limitations and comparing results from multiple studies. The paper also highlights the latest trends and future directions in cloud computing scheduling research, offering insights for practitioners and researchers in this field.

Author Biography

Nor Adnan Yahaya, Malaysia University of Science and Technology

Nor Adnan Yahaya obtained his PhD in Computer Science from Northwestern University, USA. He is a Professor at the Department of Information Technology, Malaysia University of Science and Technology. His current research activities are focused on the development of tools and innovative applications related to emerging Web technologies such as web aggregation, web services, web agents, and the Semantic Web.

References

Al Hasani, I. M. M., Kazmi, S. I. A., Shah, R. A., Hasan, R., & Hussain, S. (2022). IoT based Fire Alerting Smart System. Sir Syed University Research Journal of Engineering & Technology, 12(2), 46-50. DOI: https://doi.org/10.33317/ssurj.410

Chawla, Y., & Bhonsle, M. (2012). A study on scheduling methods in cloud computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(3), 12-17.

Madni, S. H. H., Abd Latiff, M. S., Abdullahi, M., Abdulhamid, S. I. M., & Usman, M. J. (2017). Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PloS one, 12(5), e0176321. DOI: https://doi.org/10.1371/journal.pone.0176321

Smanchat, S., & Viriyapant, K. (2015). Taxonomies of workflow scheduling problem and techniques in the cloud. Future Generation Computer Systems, 52, 1-12. DOI: https://doi.org/10.1016/j.future.2015.04.019

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. Retrieved from: DOI: https://doi.org/10.6028/NIST.SP.800-145

https://csrc.nist.gov/publications/detail/sp/800-145/final

Mishra, N. K., & Mishra, N. (2016). CELBT: An Algorithm for Efficient Cost based Load Balancing in Cloud Environment. International Journal of Computer Applications, 134(1). DOI: https://doi.org/10.5120/ijca2016907459

Liu, J., Pacitti, E., Valduriez, P., & Mattoso, M. (2015). A survey of data-intensive scientific workflow management. Journal of Grid Computing, 13, 457-493. DOI: https://doi.org/10.1007/s10723-015-9329-8

Pilavare, M. S., & Desai, A. (2015, March). A novel approach towards improving performance of load balancing using genetic algorithm in cloud computing. In 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/ICIIECS.2015.7193124

Mandal, T., & Acharyya, S. (2015, December). Optimal task scheduling in cloud computing environment: meta heuristic approaches. In 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT) (pp. 24-28). IEEE. DOI: https://doi.org/10.1109/EICT.2015.7391916

Rubrico, J. I. U., Ota, J., Higashi, T., & Tamura, H. (2008). Metaheuristic scheduling of multiple picking agents for warehouse management. Industrial Robot: An International Journal, 35(1), 58-68. DOI: https://doi.org/10.1108/01439910810843298

Topcuoglu, H., Hariri, S., & Wu, M. Y. (2002). Performance effective and low-complexity task scheduling for heterogeneous computing. IEEE transactions on parallel and distributed systems, 13(3), 260-274. DOI: https://doi.org/10.1109/71.993206

Wang, G., & Yu, H. C. (2013). Task scheduling algorithm based on improved Min-Min algorithm in cloud computing environment. In Applied Mechanics and Materials (Vol. 303, pp. 2429-2432). Trans Tech Publications Ltd. DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.2429

Tsai, C. W., Huang, W. C., Chiang, M. H., Chiang, M. C., & Yang, C. S. (2014). A hyper-heuristic scheduling algorithm for cloud. IEEE Transactions on Cloud Computing, 2(2), 236-250. DOI: https://doi.org/10.1109/TCC.2014.2315797

Devipriya, S., & Ramesh, C. (2013, December). Improved max-min heuristic model for task scheduling in cloud. In 2013 international conference on green computing, communication and conservation of energy (ICGCE) (pp. 883-888). IEEE. DOI: https://doi.org/10.1109/ICGCE.2013.6823559

Barry, D. K., & Dick, D. (2013). Web Services, Service-Oriented Architectures, and Cloud Computing: The Savvy Manager's Guide. DOI: https://doi.org/10.1016/B978-0-12-398357-2.00027-0

Tsafrir, D., Etsion, Y., & Feitelson, D. G. (2007). Backfilling using system-generated predictions rather than user runtime estimates. IEEE Transactions on Parallel and Distributed Systems, 18(6), 789-803. DOI: https://doi.org/10.1109/TPDS.2007.70606

Brent, R. P. (1989). Efficient implementation of the first-fit strategy for dynamic storage allocation. ACM Transactions on Programming Languages and Systems (TOPLAS), 11(3), 388-403. DOI: https://doi.org/10.1145/65979.65981

Fang, Y., Wang, F., & Ge, J. (2010). A task scheduling algorithm based on load balancing in cloud computing. In Web Information Systems and Mining: International Conference, WISM 2010, Sanya, China, October 23-24, 2010. Proceedings (pp. 271-277). Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-642-16515-3_34

Voß, S., & Fink, A. (2012). Hybridizing reactive tabu search with simulated annealing. In Learning and Intelligent Optimization: 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers (pp. 509-512). Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-642-34413-8_53

Miao, Y. (2014). Resource scheduling simulation design of firefly algorithm based on chaos optimization in cloud computing. International Journal of Grid and Distributed Computing, 7(6), DOI: https://doi.org/10.14257/ijgdc.2014.7.6.18

-228.

Gu, B., & Pan, F. (2013). Modified gravitational search algorithm with particle memory ability and its application. International Journal of Innovative Computing, Information and Control, 9(11), 4531-4544.

Roy, P. K. (2013). Solution of unit commitment problem using gravitational search algorithm. International Journal of Electrical Power & Energy Systems, 53, 85-94. DOI: https://doi.org/10.1016/j.ijepes.2013.04.001

Durillo, J. J., Prodan, R., Camarasu-Pop, S., Glattard, T., & Suter, F. (2014). Bi-objective workflow scheduling in production clouds: Early simulation results and outlook. Retrieved from: https://earchivo.uc3m.es/handle/10016/21872

Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y. C., Talbi, E. G., Zomaya, A. Y., & Tuyttens, D. (2011). A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal of Parallel and Distributed Computing, 71(11), 1497-1508. DOI: https://doi.org/10.1016/j.jpdc.2011.04.007

Su, S., Li, J., Huang, Q., Huang, X., Shuang, K., & Wang, J. (2013). Cost-efficient task scheduling for executing large programs in the cloud. Parallel Computing, 39(4-5), 177-188. DOI: https://doi.org/10.1016/j.parco.2013.03.002

Yi, S., Wang, Z., Ma, S., Che, Z., Liang, F., & Huang, Y. (2010, June). Combinational backfilling for parallel job scheduling. In 2010 2nd International Conference on Education Technology and Computer (Vol. 2, pp. V2-112). IEEE.

Bansal, N., Awasthi, A., & Bansal, S. (2016). Task Scheduling Algorithms with Multiple Factor in Cloud Computing Environment. Information Systems Design and Intelligent Applications, 619. DOI: https://doi.org/10.1007/978-81-322-2755-7_64

Poola, D., Garg, S. K., Buyya, R., Yang, Y., & Ramamohanarao, K. (2014, May). Robust scheduling of scientific workflows with deadline and budget constraints in clouds. In 2014 IEEE 28th international conference on advanced information networking and applications (pp. 858-865). IEEE. DOI: https://doi.org/10.1109/AINA.2014.105

Arabnejad, H., & Barbosa, J. G. (2015, October). Multi-workflow QoS-constrained scheduling for utility computing. In 2015 IEEE 18th International Conference on Computational Science and Engineering (pp. 137-144). IEEE. DOI: https://doi.org/10.1109/CSE.2015.29

Rekha, S., & Kalaiselvi, C. (2019). Review of Scheduling Methodologies of Virtual Machines (VMs) In Heterogeneous Cloud Computing. International Journal of Scientific & Technology Research, 8(09).

Sotiriadis, S., Bessis, N., & Buyya, R. (2018). Self managed virtual machine scheduling in cloud systems. Information Sciences, 433, 381-400. DOI: https://doi.org/10.1016/j.ins.2017.07.006

Junaid, M., Sohail, A., Ahmed, A., Baz, A., Khan, I. A., & Alhakami, H. (2020). A hybrid model for load balancing in cloud using file type formatting. IEEE Access, 8, 118135-118155. DOI: https://doi.org/10.1109/ACCESS.2020.3003825

Tiwari, P. K., Rani, G., Jain, T., Mundra, A., & Gupta, R. K. (2019). Load Balancing in Cloud Computing. Critical Approaches to Information Retrieval Research, 294. DOI: https://doi.org/10.4018/978-1-7998-1021-6.ch016

Ghobaei-Arani, M., Rahmanian, A. A., Aslanpour, M. S., & Dashti, S. E. (2018). CSA-WSC: cuckoo search algorithm for web service composition in cloud environments. Soft Computing, 22(24), 8353-8378. DOI: https://doi.org/10.1007/s00500-017-2783-4

Safari, M., & Khorsand, R. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simulation Modelling Practice and Theory, 87, 311-326. DOI: https://doi.org/10.1016/j.simpat.2018.07.006

Hamdani, M., Aklouf, Y., & Chaalal, H. (2020, June). A Comparative Study on Load Balancing Algorithms in Cloud Environment. In Proceedings of the 10th International Conference on Information Systems and Technologies (pp. 1-4). DOI: https://doi.org/10.1145/3447568.3448466

Ghobaei-Arani, M., Khorsand, R., & Ramezanpour, M. (2019). An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. Journal of Network and Computer Applications, 142, 76-97. DOI: https://doi.org/10.1016/j.jnca.2019.06.002

Ghobaei-Arani, M., Souri, A., Baker, T., & Hussien, A. (2019). ControCity: an autonomous approach for controlling elasticity using buffer Management in Cloud Computing Environment. IEEE Access, 7, 106912-106924. DOI: https://doi.org/10.1109/ACCESS.2019.2932462

Ghobaei‐Arani, M., Souri, A., Safara, F., & Norouzi, M. (2020). An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing. Transactions on Emerging Telecommunications Technologies, 31(2), e3770. DOI: https://doi.org/10.1002/ett.3770

Rafieyan, E., Khorsand, R., & Ramezanpour, M. (2020). An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing. Computers & Industrial Engineering, 140, 106272. DOI: https://doi.org/10.1016/j.cie.2020.106272

Khorsand, R., & Ramezanpour, M. (2020). An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing. International Journal of DOI: https://doi.org/10.1002/dac.4379

Communication Systems, 33(9), e4379.

Safari, M., & Khorsand, R. (2018). PL-DVFS: combining Power aware List-based scheduling algorithm with DVFS technique for real-time tasks in Cloud Computing. The Journal of Supercomputing, 74, 5578-5600. DOI: https://doi.org/10.1007/s11227-018-2498-z

Khorsand, R., Ghobaei‐Arani, M., & Ramezanpour, M. (2019). A self‐learning fuzzy approach for proactive resource provisioning in cloud environment. Software: Practice and Experience, 49(11), 1618-1642. DOI: https://doi.org/10.1002/spe.2737

Strumberger, I., Tuba, E., Bacanin, N., Beko, M., & Tuba, M. (2020). Modified and hybridized monarch butterfly algorithms for multi-objective optimization. Advances in intelligent systems and computing (923), pp. 449–458. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-14347-3_44

Adhikari, M., Nandy, S., & Amgoth, T. (2019). Meta heuristic based task deployment mechanism for load balancing in IaaS cloud. Journal of Network and Computer Applications, 128, 64-77. DOI: https://doi.org/10.1016/j.jnca.2018.12.010

Kaur, A., & Kaur, B. (2022). Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment. Journal of King Saud University-Computer and Information Sciences, 34(3), 813-824. DOI: https://doi.org/10.1016/j.jksuci.2019.02.010

Strumberger, I., Bacanin, N., Tuba, M., & Tuba, E. (2019). Resource scheduling in cloud computing based on a hybridized whale optimization algorithm. Applied Sciences, 9(22), 4893. DOI: https://doi.org/10.3390/app9224893

Torabi, S., & Safi-Esfahani, F. (2018). A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing. The Journal of Supercomputing, 74(6), 2581-2626. DOI: https://doi.org/10.1007/s11227-018-2291-z

Attiya, I., Abd Elaziz, M., & Xiong, S. (2020). Job scheduling in cloud computing using a modified harris hawks optimization and simulated annealing algorithm. Computational intelligence and neuroscience, 2020. DOI: https://doi.org/10.1155/2020/3504642

Li, C., Li, J., Chen, H., & Heidari, A. A. (2021). Memetic Harris Hawks Optimization: Developments and perspectives on project scheduling and QoS-aware web service composition. Expert Systems with Applications, 171, 114529. DOI: https://doi.org/10.1016/j.eswa.2020.114529

Patel, D., Gupta, R. K., & Pateriya, R. K. (2019). Energy-aware prediction-based load balancing approach with VM migration for the cloud environment. Data, Engineering and Applications:Volume 2, 59-74. DOI: https://doi.org/10.1007/978-981-13-6351-1_6

Kumar, Y., & Singh, P. K. (2018). Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Applied Intelligence, 48, 2681- DOI: https://doi.org/10.1007/s10489-017-1096-8

Anwar, N., & Deng, H. (2018). A hybrid metaheuristic for multi objective scientific workflow scheduling in a cloud environment. Applied sciences, 8(4), 538. DOI: https://doi.org/10.3390/app8040538

Zhong, W., Zhuang, Y., Sun, J., & Gu, J. (2018). A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine. Applied Intelligence, 48, 4072-4083. DOI: https://doi.org/10.1007/s10489-018-1194-2

Ashouraei, M., Khezr, S. N., Benlamri, R., & Navimipour, N. J. (2018, August). A new SLA-aware load balancing method in the cloud using an improved parallel task scheduling algorithm. In 2018 IEEE 6th international conference on future internet of things and cloud (FiCloud) (pp. 71-76). IEEE. DOI: https://doi.org/10.1109/FiCloud.2018.00018

Sharma, N., & Maurya, S. (2019, February). SLA-based agile VM management in cloud & datacenter. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 252-257). IEEE. DOI: https://doi.org/10.1109/COMITCon.2019.8862260

Toutouh, J., & Alba, E. (2015). Metaheuristics for energy-efficient data routing in vehicular networks. International Journal of Metaheuristics, 4(1), 27-56. DOI: https://doi.org/10.1504/IJMHEUR.2015.071750

Mohanty, S., Patra, P. K., Ray, M., & Mohapatra, S. (2018). A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing. International Journal of Knowledge-Based Organizations (IJKBO), 8(1), 29-49. DOI: https://doi.org/10.4018/IJKBO.2018010103

Hajimirzaei, B., & Navimipour, N. J. (2019). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express, 5(1), 56-59. DOI: https://doi.org/10.1016/j.icte.2018.01.014

Tuli, S., Gill, S. S., Garraghan, P., Buyya, R., Casale, G., & Jennings, N. (2021). START: Straggler prediction and mitigation for cloud computing environments using encoder lstm networks. IEEE Transactions on Services Computing. DOI: https://doi.org/10.1109/TSC.2021.3129897

Mathew, M. (2018). Virtualization and Scheduling In Cloud Computing Environment – A Study. IOSR Journals 20(4), pp. 23–32.

Varma, N. M. K., & Choi, E. (2016). Study and comparison of virtual machine scheduling algorithms in open source clouds. In Advanced Multimedia and Ubiquitous Engineering: FutureTech & MUE (pp. 349-355). Springer Singapore. DOI: https://doi.org/10.1007/978-981-10-1536-6_46

Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., & Zagorodnov, D. (2009, July). Eucalyptus: an open-source cloud computing infrastructure. In Journal of Physics: Conference Series (Vol. 180, No. 1, p. 012051). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/180/1/012051

Basthikodi, M., Faizabadi, A. R., & Ahmed, W. (2019). HPC Based Algorithmic Species Extraction Tool for Automatic Parallelization of Program Code. International Journal of Recent Technology and Engineering, 8, 1004-1009. DOI: https://doi.org/10.35940/ijrte.B1188.0782S319

Basthikodi, M., & Ahmed, W. (2016, December). Classifying a program code for parallel computing against hpcc. In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 512-516). IEEE. DOI: https://doi.org/10.1109/PDGC.2016.7913248

Varma, N. M. K., Min, D., & Choi, E. (2011, November). Diagnosing CPU utilization in the Xen virtual machine environment. In 2011 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT) (pp. 58-63). IEEE.

Roschke, S., Cheng, F., & Meinel, C. (2009, December). Intrusion detection in the cloud. In 2009 eighth IEEE international conference on dependable, autonomic and secure computing (pp. 729-734). IEEE. DOI: https://doi.org/10.1109/DASC.2009.94

Mazzariello, C., Bifulco, R., & Canonico, R. (2010, August). Integrating a network ids into an open source cloud computing environment. In 2010 sixth international conference on information assurance and security (pp. 265-270). IEEE. DOI: https://doi.org/10.1109/ISIAS.2010.5604069

Garfinkel, T., & Rosenblum, M. (2003, February). A virtual machine introspection based architecture for intrusion detection. In Ndss (Vol. 3, No. 2003, pp. 191-206).Retrieved from :http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.11.8367&rep=rep1&type=pdf%5Cnhttp://www.isoc.org/isoc/conferences/ndss/03/proceedings/papers/13.pdf

Ibrahim, A. S., Hamlyn-Harris, J., Grundy, J., & Almorsy, M. (2011, September). Cloudsec: a security monitoring appliance for virtual machines in the iaas cloud model. In 2011 5th International Conference on Network and System Security (pp. 113-120). IEEE. DOI: https://doi.org/10.1109/ICNSS.2011.6059967

E. Summary. (2014). WHITE PAPER 2 Cybersecurity Problems Today 2 What Is an NGFW? 3 Best Practices for Selecting an NGFW. Next-Generation Firewalls: The New Norm in Defense.

Retrieved from:https://webobjects.cdw.com/webobjects/media/pdf/Solutions/Security/148649-Next-Generation-Firewalls-The-New-Norm-In-Defense.pdf

Naidu, V. R., Bhat, A. Z., & Singh, B. (2019). Cloud Concept for Implementing Multimedia Based Learning in Higher Education. In Smart Technologies and Innovation for a Sustainable Future: Proceedings of the 1st American University in the Emirates International Research Conference—Dubai, UAE 2017 (pp. 81-84). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-01659-3_11