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Journal of Network and Systems Management
Published in: Journal of Network and Systems Management 2/2021

01-04-2021

Meta-heuristic Approaches for Effective Scheduling in Infrastructure as a Service Cloud: A Systematic Review

Authors: J. Kok Konjaang, Lina Xu

Published in: Journal of Network and Systems Management | Issue 2/2021

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Abstract

Cloud computing involves a large number of shared virtual servers that are accessible from both public and private networks. It has provided scalable and multitenant computing approaches for Infrastructure as a Service, Software as a Service, and Platform as a Service to cloud users on pay-per-use bases. Over the past decades, researchers from different domains such as astronomy, physics, earth science, and bioinformatics have used scientific workflow applications to model many real-world problems in both paralleled and distributed computing environments. However, achieving efficient workflow scheduling is challenging. This is due to the large size of the task set that each workflow application generates. The complex dependencies between these workflows make it difficult to find an optimal solution to workflow scheduling problems within polynomial time. This paper analyzed workflows scheduling problems in cloud and grid computing environment through providing a comprehensive survey based on the state-of-the-art meta-heuristic algorithms. We analyzed the literature from four perspectives, including (i) existing meta-heuristics, (ii) scheduling efficiency, system performance, and execution budget, (iii) scheduling environment and (iv) quality of service performance metrics. Also, we have presented the research gaps and provided future directions for future investigation.
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Literature
1.
Saeedi, S., Khorsand, R., Bidgoli, S.G, Ramezanpour, M.: Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Comput. Ind. Eng. 106649 (2020)
2.
Shields, M.: Control-versus data-driven workflows. In: Workflows for e-Science, pp. 167–173. Springer, Berlin (2007)
3.
Meena, J., Kumar, M., Vardhan, M.: Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access 4, 5065–5082 (2016)CrossRef
4.
Rodriguez, M.A., Buyya, R.: A taxonomy and survey on scheduling algorithms for scientific workflows in IAAS cloud computing environments. Concurr. Comput. 29(8), e4041 (2017)CrossRef
5.
Ludäscher, B., Bowers, S., McPhillips, T.: Scientific workflows. Encyclopedia of Database Systems, pp. 2507–2511 (2009)
6.
Chakravarthi, K.K., Shyamala, L., Vaidehi, V.: Topsis inspired cost-efficient concurrent workflow scheduling algorithm in cloud. J. King Saud Univ.-Comput. Inf. Sci. (2020)
7.
Kok Konjaang, J., Maipan-uku, J., Kennedy Kubuga, K.: An efficient max-min resource allocator and task scheduling algorithm in cloud computing environment. arXiv (2016)
8.
Alkhanak, E.N., Lee, S.P., Rezaei, R., Parizi, R.M.: Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J. Syst. Softw. 113, 1–26 (2016)CrossRef
9.
Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 24th IEEE international conference on advanced information networking and applications. IEEE, 2010, pp. 400–407 (2010)
10.
Manasrah, A.M., Ba Ali, H.: Workflow scheduling using hybrid ga-pso algorithm in cloud computing. Wirel. Commun. Mobile Comput. (2018)
11.
Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)CrossRef
12.
Singh, L., Singh, S.: A genetic algorithm for scheduling workflow applications in unreliable cloud environment. In: International conference on security in computer networks and distributed systems, pp. 139–150. Springer, Berlin (2014)
13.
Dai, Y., Lou, Y., Lu, X.: A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-qos constraints in cloud computing. In: 2015 7th international conference on intelligent human-machine systems and cybernetics, vol. 2. IEEE, pp. 428–431 (2015)
14.
Jena, R.: Task scheduling in cloud environment: a multi-objective ABC framework. J. Inf. Optim. Sci. 38(1), 1–19 (2017)MathSciNet
15.
Chen, Z.-G., Zhan, Z.-H., Li, H.-H., Du, K.-J., Zhong, J.-H., Foo, Y.W., Li, Y., Zhang, J.: Deadline constrained cloud computing resources scheduling through an ant colony system approach. In: 2015 international conference on cloud computing research and innovation (ICCCRI). IEEE, pp. 112–119 (2015)
16.
Rajakumar, R., Dhavachelvan, P., Vengattaraman, T.: A survey on nature inspired meta-heuristic algorithms with its domain specifications. In: 2016 international conference on communication and electronics systems (ICCES). IEEE, pp. 1–6 (2016)
17.
Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015)
18.
Tsai, C.-W., Rodrigues, J.J.: Metaheuristic scheduling for cloud: a survey. IEEE Syst. J. 8(1), 279–291 (2013)CrossRef
19.
Singh, P., Dutta, M., Aggarwal, N.: A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl. Inf. Syst. 52(1), 1–51 (2017)CrossRef
20.
Kaur, M.D., et al.: Review on different metaheuristic techniques for parallel computing. J. Adv. Res. Cloud Comput. Virtualiz. Web Appl. 1(2), 28–32 (2018)
21.
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A.: Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement. Syst. Rev. 4(1), 1 (2015)CrossRef
22.
Afzal, W., Torkar, R., Feldt, R.: A systematic review of search-based testing for non-functional system properties. Inf. Softw. Technol. 51(6), 957–976 (2009)CrossRef
23.
Donyagard Vahed, N., Ghobaei-Arani, M., Souri, A.: Multiobjective virtual machine placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments: a comprehensive review. Int. J. Commun. Syst. 32(14), e4068 (2019)CrossRef
24.
Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering-a systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009)CrossRef
25.
Milani, A.S., Navimipour, N.J.: Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J. Netw. Comput. Appl. 71, 86–98 (2016)CrossRef
26.
Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.: Parallelization of scientific workflows in the cloud (2014)
27.
Da Silva, R.F., Chen, W., Juve, G., Vahi, K., Deelman, E.: Community resources for enabling research in distributed scientific workflows. In: 2014 IEEE 10th International Conference on e-Science, vol. 1. IEEE, 2014, pp. 177–184 (2014)
28.
Cotes-Ruiz, I.T., Prado, R.P., García-Galán, S., Muñoz-Expósito, J.E., Ruiz-Reyes, N.: Dynamic voltage frequency scaling simulator for real workflows energy-aware management in green cloud computing. PLoS ONE 12(1), e0169803 (2017)CrossRef
29.
Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Fut. Gener. Comput. Syst. 29(3), 682–692 (2013)CrossRef
30.
Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: “Characterization of scientific workflows. In: 2008 third workshop on workflows in support of large-scale science. IEEE, 2008, pp. 1–10 (2008)
31.
32.
Konjaang, J.K., Ayob, F.H., Muhammed, A.: Cost effective expa-max-min scientific workflow allocation and load balancing strategy in cloud computing. JCS 14(5), 623–638 (2018)
33.
Abbott, B., Abbott, R., Adhikari, R., Ajith, P., Allen, B., Allen, G., Amin, R., Anderson, S., Anderson, W., Arain, M., et al.: Ligo: the laser interferometer gravitational-wave observatory. Rep. Prog. Phys. 72(7), 076901 (2009)CrossRef
34.
Berriman, G.B., Deelman, E., Good, J.C., Jacob, J.C., Katz, D.S., Kesselman, C., Laity, A.C., Prince, T.A., Singh, G., Su, M.-H.: Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand. In: Optimizing scientific return for astronomy through information technologies, vol. 5493. International Society for Optics and Photonics, 2004, pp. 221–233 (2004)
35.
Elsherbiny, S., Eldaydamony, E., Alrahmawy, M., Reyad, A.E.: An extended intelligent water drops algorithm for workflow scheduling in cloud computing environment. Egypt. Inf. J. 19(1), 33–55 (2018)
36.
Bhoi, U., Ramanuj, P.N., et al.: Enhanced max-min task scheduling algorithm in cloud computing. IJAIEM 2(4), 259–264 (2013)
37.
38.
Nasr, A.A., El-Bahnasawy, N.A., Attiya, G., El-Sayed, A.: Cost-effective algorithm for workflow scheduling in cloud computing under deadline constraint. Arab. J. Sci. Eng. 44(4), 3765–3780 (2019)CrossRef
39.
Singh, R., Choudhury, S., Gehlot, A.: Intelligent communication, control and devices: proceedings of ICICCD 2017, vol. 624. Springer, Berlin (2018)
40.
Chen, W., Deelman, E.: Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-Science. IEEE, pp. 1–8 (2012)
41.
Juarez, F., Ejarque, J., Badia, R.M.: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Fut. Gener. Comput. Syst. 78, 257–271 (2018)CrossRef
42.
Kister, T.C., Hawkins, B.: Maintenance Planning and Scheduling: Streamline Your Organization for a Lean Environment. Elsevier, Devon (2006)
43.
Ali, S.A., Alam, M.: A relative study of task scheduling algorithms in cloud computing environment. In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, pp. 105–111 (2016)
44.
Mathew, T., Sekaran, K.C., Jose, J.: Study and analysis of various task scheduling algorithms in the cloud computing environment. In: 2014 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp. 658–664 (2014)
45.
Zhou, X., Zhang, G., Sun, J., Zhou, J., Wei, T., Hu, S.: Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft. Fut. Gener. Comput. Syst. 93, 278–289 (2019)CrossRef
46.
Madni, S.H.H., Latiff, M.S.A., Abdullahi, M., Usman, M.J., et al.: Performance comparison of heuristic algorithms for task scheduling in IAAS cloud computing environment. PLoS ONE 12(5), e0176321 (2017)CrossRef
47.
Verma, A., Kaushal, S.: Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In: 2014 recent advances in engineering and computational sciences (RAECS). IEEE, pp. 1–6 (2014)
48.
Wu, Z., Ni, Z., Gu, L., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: 2010 international conference on computational intelligence and security. IEEE, pp. 184–188 (2010)
49.
Rimal, B.P., Maier, M.: Workflow scheduling in multi-tenant cloud computing environments. IEEE Trans. Parallel Distrib. Syst. 28(1), 290–304 (2016)CrossRef
50.
Haidri, R.A., Katti, C.P., Saxena, P.C.: Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing. J. King Saud Univ. Comput. Inf. Sci. (2017)
51.
Xu, X., Dou, W., Zhang, X., Chen, J.: Enreal: an energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans. Cloud Comput. 4(2), 166–179 (2015)CrossRef
52.
Chen, H., Zhu, X., Qiu, D., Guo, H., Yang, L.T., Lu, P.: Eons: minimizing energy consumption for executing real-time workflows in virtualized cloud data centers. In: 2016 45th international conference on parallel processing workshops (ICPPW). IEEE, pp. 385–392 (2016)
53.
Cao, F., Zhu, M.M., Wu, C.Q.: Energy-efficient resource management for scientific workflows in clouds. In: 2014 IEEE world congress on services. IEEE, pp. 402–409 (2014)
54.
Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Fut. Gener. Comput. Syst. 26(4), 608–621 (2010)CrossRef
55.
Sossa, M.A.R.: Resource provisioning and scheduling algorithms for scientific workflows in cloud computing environments. Ph.D. dissertation, University of Melbourne, Department of Computing and Information Systems (2016)
56.
Tawfeek, M.A., El-Sisi, A., Keshk, A.E., Torkey, F.A.: Cloud task scheduling based on ant colony optimization. In: 2013 8th international conference on computer engineering & systems (ICCES). IEEE, pp. 64–69 (2013)
57.
Gupta, R., Gajera, V., Jana, P.K. et al.: An effective multi-objective workflow scheduling in cloud computing: a PSO based approach. In: 2016 ninth international conference on contemporary computing (IC3). IEEE, pp. 1–6 (2016)
58.
Kumar, B., Kalra, M., Singh, P.: Discrete binary cat swarm optimization for scheduling workflow applications in cloud systems. In: 2017 3rd international conference on computational intelligence & communication technology (CICT). IEEE, pp. 1–6 (2017)
59.
Ngatman, M.F., Sharif, J.M., Ngadi, M.A.: A study on modified PSO algorithm in cloud computing. In: 2017 6th ICT International Student Project Conference (ICT-ISPC). IEEE, pp. 1–4 (2017)
60.
Arabnejad, H., Barbosa, J.G.: Maximizing the completion rate of concurrent scientific applications under time and budget constraints. J. Comput. Sci. 23, 120–129 (2017)MathSciNetCrossRef
61.
Madni, S.H.H., Abd Latiff, M.S., Coulibaly, Y., et al.: Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Clust. Comput. 20(3), 2489–2533 (2017)CrossRef
62.
Rodriguez Sossa, M.A.: Resource provisioning and scheduling algorithms for scientific workflows in cloud computing environments. PhD dissertation (2016)
63.
Durillo, J.J., Nae, V., Prodan, R.: Multi-objective workflow scheduling: an analysis of the energy efficiency and makespan tradeoff. In: 2013 13th IEEE/ACM international symposium on cluster, cloud, and grid computing. IEEE, pp. 203–210 (2013)
64.
Li, Z., Ge, J., Hu, H., Song, W., Hu, H., Luo, B.: Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Trans. Serv. Comput. 11(4), 713–726 (2018)CrossRef
65.
Shuja, J., Madani, S.A., Bilal, K., Hayat, K., Khan, S.U., Sarwar, S.: Energy-efficient data centers. Computing 94(12), 973–994 (2012)MATHCrossRef
66.
Zhu, X., Yang, L.T., Chen, H., Wang, J., Yin, S., Liu, X.: Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans. Cloud Comput. 2(2), 168–180 (2014)CrossRef
67.
Xu, X., Dou, W., Zhang, X., Chen, J.: Enreal: an energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans. Cloud Comput. 4(2), 166–179 (2016)CrossRef
68.
Buyya, R., Beloglazov, A., Abawajy, J.: Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv:​1006.​0308 (2010)
69.
Sivagami, V., Easwarakumar, K.: An improved particle swarm optimization algorithm for load balanced fault tolerant virtual machine scheduling in computational cloud
70.
Baxodirjonovich, K.N., Choe, T.-Y.: Dynamic task scheduling algorithm based on ant colony scheme. Int. J. Eng. Technol. 1163–1172 (2015)
71.
Li, J.-Q., Pan, Q.-K., Gao, K.-Z.: Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Intl. J. Adv. Manuf. Technol. 55(9–12), 1159–1169 (2011)CrossRef
72.
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer. Technical Report (2005)
73.
Seeley, T.: The Wisdom of the Hive Cambridge. Belknap Press of Harvard University Press [Google Scholar], Harvard (1995)CrossRef
74.
Chen, W.-n., Shi, Y., Zhang, J.: An ant colony optimization algorithm for the time-varying workflow scheduling problem in grids. In: 2009 IEEE congress on evolutionary computation. IEEE, pp. 875–880 (2009)
75.
Xiang, B., Zhang, B., Zhang, L.: Greedy-ant: ant colony system-inspired workflow scheduling for heterogeneous computing. IEEE Access 5, 11 404–11 412 (2017)CrossRef
76.
Beheshti, Z., Shamsuddin, S.M.H.: A review of population-based meta-heuristic algorithms. Int. J. Adv. Soft Comput. Appl 5(1), 1–35 (2013)
77.
Lazar, A.: Heuristic knowledge discovery for archaeological data using genetic algorithms and rough sets. In: Heuristic and optimization for knowledge discovery. IGI Global, pp. 263–278 (2002)
78.
79.
Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J.: A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8(2), 239–287 (2009)MathSciNetMATHCrossRef
80.
Alkayal, E.: Optimizing resource allocation using multi-objective particle swarm optimization in cloud computing systems. PhD dissertation, University of Southampton (2018)
81.
Dhiman, G., Kumar, V.: Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70 (2017)CrossRef
82.
Kumar, A., Bawa, S.: A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services. Soft Comput. 1–14 (2019)
83.
Cui, Y., Geng, Z., Zhu, Q., Han, Y.: Multi-objective optimization methods and application in energy saving. Energy 125, 681–704 (2017)CrossRef
84.
Yang, X.-S., Deb, S.: Cuckoo search via lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp. 210–214 (2009)
85.
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)
86.
Karimkashi, S., Kishk, A.A.: Invasive weed optimization and its features in electromagnetics. IEEE Trans. Antennas Propag. 58(4), 1269–1278 (2010)CrossRef
87.
Yang, X.-S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optimiz. 46(9), 1222–1237 (2014)MathSciNetCrossRef
88.
Dhiman, G., Kaur, A.: Optimizing the design of airfoil and optical buffer problems using spotted hyena optimizer. Designs 2(3), 28 (2018)CrossRef
89.
90.
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRef
91.
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Gsa: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)MATHCrossRef
92.
Kennedy, J., Eberhart, R.: Particle swarm optimization (PSO). In: Proc. IEEE international conference on neural networks, Perth, pp. 1942–1948 (1995)
93.
Holland, J.H., et al.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)CrossRef
94.
Dorigo, M., Maniezzo, V., Colorni, A., et al.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B Cybern. 26(1), 29–41 (1996)CrossRef
95.
Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)CrossRef
96.
Wang, Z., Shuang, K., Yang, L., Yang, F.: Energy-aware and revenue-enhancing combinatorial scheduling in virtualized of cloud datacenter. J. Converg. Inf. Technol. 7(1), 62–70 (2012)
97.
Guo, P., Xue, Z.: An adaptive PSO-based real-time workflow scheduling algorithm in cloud systems. In: 2017 IEEE 17th international conference on communication technology (ICCT). IEEE, pp. 1932–1936 (2017)
98.
Yang, X.-S.: Optimization and metaheuristic algorithms in engineering. In: Metaheuristics in water, geotechnical and transport engineering, pp. 1–23 (2013)
99.
Montana, D., Brinn, M., Moore, S., Bidwell, G.: Genetic algorithms for complex, real-time scheduling. In: SMC’98 conference proceedings. In: 1998 IEEE international conference on systems, man, and cybernetics (Cat. No. 98CH36218), vol. 3. IEEE, pp. 2213–2218 (1998)
100.
101.
Snaselova, P., Zboril, F.: Genetic algorithm using theory of chaos. Proc. Comput. Sci. 51, 316–325 (2015)CrossRef
102.
Abrishami, S., Naghibzadeh, M., Epema, D.H.: Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans. Parallel Distrib. Syst. 23(8), 1400–1414 (2011)CrossRef
103.
Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program. 14(3–4), 217–230 (2006)
104.
Haidri, R.A., Katti, C.P., Saxena, P.C.: Cost-effective deadline-aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing. Concurr. Comput. 31(7), e5006 (2019)CrossRef
105.
Shishido, H.Y., Estrella, J.C., Toledo, C.F.M., Arantes, M.S.: Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds. Comput. Electr. Eng. 69, 378–394 (2018)CrossRef
106.
Kaur, G., Kalra, M.: Deadline constrained scheduling of scientific workflows on cloud using hybrid genetic algorithm. In: 2017 7th international conference on cloud computing, data science & engineering-confluence. IEEE, pp. 276–280 (2017)
107.
Page, A.J., Naughton, T.J.: Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing. In: 19th IEEE international parallel and distributed processing symposium. IEEE (2005)
108.
Shojafar, M., Javanmardi, S., Abolfazli, S., Cordeschi, N.: Fuge: a joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method. Clust. Comput. 18(2), 829–844 (2015)CrossRef
109.
Amalarethinam, D.D.G., Beena, T.L.A.: Workflow scheduling for public cloud using genetic algorithm (WSGA). IOSRJCE, e-ISSN, pp. 2278–0661 (2016)
110.
Nagar, R., Gupta, D.K., Singh, R.M.: Time effective workflow scheduling using genetic algorithm in cloud computing (2018)
111.
Deng, F., Lai, M., Geng, J.: Multi-workflow scheduling based on genetic algorithm. In: 2019 IEEE 4th international conference on cloud computing and big data analysis (ICCCBDA). IEEE, pp. 300–305 (2019)
112.
Kołodziej, J., Khan, S.U., Wang, L., Zomaya, A.Y.: Energy efficient genetic-based schedulers in computational grids. Concurr. Comput. 27(4), 809–829 (2015)CrossRef
113.
Gabaldon, E., Lerida, J.L., Guirado, F., Planes, J.: Blacklist multi-objective genetic algorithm for energy saving in heterogeneous environments. J. Supercomput. 73(1), 354–369 (2017)CrossRef
114.
Verma, A., Kaushal, S.: Budget constrained priority based genetic algorithm for workflow scheduling in cloud (2013)
115.
Gharooni-fard, G., Moein-darbari, F., Deldari, H., Morvaridi, A.: Scheduling of scientific workflows using a chaos-genetic algorithm. Proc. Comput. Sci. 1(1), 1445–1454 (2010)CrossRef
116.
Sellami, K., Ahmed-Nacer, M., Tiako, P., Chelouah, R.: Immune genetic algorithm for scheduling service workflows with GOS constraints in cloud computing. S. Afr. J. Ind. Eng. 24(3), 68–82 (2013)
117.
Wang, W.-J., Chang, Y.-S., Lo, W.-T., Lee, Y.-K.: Adaptive scheduling for parallel tasks with QOS satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 783–811 (2013)CrossRef
118.
Kumar, P., Verma, A.: Scheduling using improved genetic algorithm in cloud computing for independent tasks. In: Proceedings of the international conference on advances in computing, communications and informatics. ACM, pp. 137–142 (2012)
119.
Hamad, S.A., Omara, F.A.: Genetic-based task scheduling algorithm in cloud computing environment. Intl. J. Adv. Comput. Sci. Appl. 7(4), 550–556 (2016)
120.
Liu, J., Luo, X.-G., Zhang, X.-M., Zhang, F., Li, B.-N.: Job scheduling model for cloud computing based on multi-objective genetic algorithm. Intl. J. Comput. Sci.Issues (IJCSI) 10(1), 134 (2013)
121.
Wu, Q., Yun, D., Lin, X., Gu, Y., Lin, W., Liu, Y.: On workflow scheduling for end-to-end performance optimization in distributed network environments. In: Workshop on job scheduling strategies for parallel processing, pp. 76–95. Springer, Berlin (2012)
122.
Zeng, L., Veeravalli, B., Li, X.: Scalestar: budget conscious scheduling precedence-constrained many-task workflow applications in cloud. In: 2012 IEEE 26th international conference on advanced information networking and applications. IEEE, pp. 534–541 (2012)
123.
Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)CrossRef
124.
Cheng, C.-T., Wang, W.-C., Xu, D.-M., Chau, K.: Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour. Manage 22(7), 895–909 (2008)CrossRef
125.
Choudhary, V., Kacker, S., Choudhury, T., Vashisht, V.: An approach to improve task scheduling in a decentralized cloud computing environment. Intl. J. Comput. Technol. Appl. 3(1), 312–316 (2012)
126.
Bittencourt, L.F., Madeira, E.R., Da Fonseca, N.L.: Scheduling in hybrid clouds. IEEE Commun. Mag. 50(9), 42–47 (2012)CrossRef
127.
Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: Sixth annual China grid conference. IEEE, pp. 3–9 (2011)
128.
Mollajafari, M., Shahhoseini, H.S.: An efficient ACO-based algorithm for scheduling tasks onto dynamically reconfigurable hardware using TSP-likened construction graph. Appl. Intell. 45(3), 695–712 (2016)CrossRef
129.
Wen, W.-T., Wang, C.-D., Wu, D.-S., Xie, Y.-Y.: An ACO-based scheduling strategy on load balancing in cloud computing environment. In: 2015 ninth international conference on frontier of computer science and technology. IEEE, pp. 364–369 (2015)
130.
Madivi, R., Kamath, S.S.: An hybrid bio-inspired task scheduling algorithm in cloud environment. In: Fifth international conference on computing, communications and networking technologies (ICCCNT). IEEE, pp. 1–7 (2014)
131.
Xu, P., He, G., Li, Z., Zhang, Z.: An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization. Int. J. Distrib. Sens. Netw. 14(12), 1550147718793799 (2018)CrossRef
132.
Gupta, A., Garg, R.: Load balancing based task scheduling with aco in cloud computing. In: 2017 international conference on computer and applications (ICCA). IEEE, pp. 174–179 (2017)
133.
Xianfeng, Y., HongTao, L.: Load balancing of virtual machines in cloud computing environment using improved ant colony algorithm. Intl. J. Grid Distrib. Comput. 8(6), 19–30 (2015)CrossRef
134.
Singh, L., Singh, S.: Deadline and cost based ant colony optimization algorithm for scheduling workflow applications in hybrid cloud. Intl. J. Sci. Eng. Res. 5(10), 1417–1420 (2014)
135.
Pan, Q.-K., Tasgetiren, M.F., Suganthan, P.N., Chua, T.J.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf. Sci. 181(12), 2455–2468 (2011)MathSciNetCrossRef
136.
Liang, Y.-C., Chen, A.H.-L., Nien, Y.-H.: Artificial bee colony for workflow scheduling. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp. 558–564. (2014)
137.
Kruekaew, B., Kimpan, W.: Virtual machine scheduling management on cloud computing using artificial bee colony. In: Proceedings of the international multiconference of engineers and computer scientists, pp. 12–14 (2014)
138.
Ld, D.B., Krishna, P.V.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)CrossRef
139.
Basturk, B.: An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE swarm intelligence symposium, Indianapolis (2006)
140.
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J. Global Optim. 39(3), 459–471 (2007)MathSciNetMATHCrossRef
141.
Arsuaga-Ríos, M., Vega-Rodríguez, M.A., Prieto-Castrillo, F.: Multi-objective artificial bee colony for scheduling in grid environments. In: 2011 IEEE symposium on swarm intelligence. IEEE, pp. 1–7 (2011)
142.
Kim, S.-S., Byeon, J.-H., Liu, H., Abraham, A., McLoone, S.: Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization. Soft Comput. 17(5), 867–882 (2013)CrossRef
143.
Zhang, Y., Zeng, P., Zang, C.: Optimization algorithm for home energy management system based on artificial bee colony in smart grid. In: 2015 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER). IEEE, pp. 734–740 (2015)
144.
Bhagade, A.S., Puranik, P.V.: Artificial bee colony (abc) algorithm for vehicle routing optimization problem. Intl. J. Soft Comput. Eng. 2(2), 329–333 (2012)
145.
Lučić, P., Teodorović, D.: Vehicle routing problem with uncertain demand at nodes: the bee system and fuzzy logic approach. In: Fuzzy sets based heuristics for optimization, pp. 67–82. Springer, Berlin (2003)
146.
Yao, B., Yan, Q., Zhang, M., Yang, Y.: Improved artificial bee colony algorithm for vehicle routing problem with time windows. PLoS ONE 12(9), e0181275 (2017)CrossRef
147.
Ozturk, C., Karaboga, D.: Hybrid artificial bee colony algorithm for neural network training. In: 2011 IEEE congress of evolutionary computation (CEC). IEEE, pp. 84–88 (2011)
148.
Garro, B.A., Sossa, H., Vázquez, R.A.: Artificial neural network synthesis by means of artificial bee colony (abc) algorithm. In: 2011 IEEE congress of evolutionary computation (CEC). IEEE, pp. 331–338 (2011)
149.
Wang, L., Zhou, G., Xu, Y., Wang, S., Liu, M.: An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Intl. J. Adv. Manuf. Technol. 60(1–4), 303–315 (2012)CrossRef
150.
Liu, Y.-F., Liu, S.-Y.: A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem. Appl. Soft Comput. 13(3), 1459–1463 (2013)CrossRef
151.
Hesabian, N., Haj, H., Javadi, S.: Optimal scheduling in cloud computing environment using the bee algorithm. Int. J. Comput. Netw. Commun. Secur. 3, 253–258 (2015)
152.
Vivekanandan, K., Ramyachitra, D., Anbu, B.: Artificial bee colony algorithm for grid scheduling. J. Converg. Inf. Technol. 6(7), 328–339 (2011)
153.
Mousavinasab, Z., Entezari-Maleki, R., Movaghar, A.: A bee colony task scheduling algorithm in computational grids. In: International conference on digital information processing and communications, pp. 200–210. Springer, Berlin (2011)
154.
Kansal, N.J., Chana, I.: Artificial bee colony based energy-aware resource utilization technique for cloud computing. Concurr. Comput. 27(5), 1207–1225 (2015)CrossRef
155.
Kaur, G., Agnihotri, M.: Enhanced artificial bee colony based workflow scheduling for cloud computing environment
156.
Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36–38), 3902–3933 (2005)MATHCrossRef
157.
158.
Lee, K.S., Geem, Z.W.: A new structural optimization method based on the harmony search algorithm. Comput. Struct. 82(9–10), 781–798 (2004)CrossRef
159.
Yang, X.-S.: Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm, pp. 1–14. Springer, Berlin (2009)
160.
Melnik, M., Trofimenko, T.: Polyrhythmic harmony search for workflow scheduling. Proc. Comput. Sci. 66, 468–476 (2015)CrossRef
161.
Chaudhary, N., Kalra, M.: An improved harmony search algorithm with group technology model for scheduling workflows in cloud environment. In: 2017 4th IEEE Uttar Pradesh section international conference on electrical, computer and electronics (UPCON). IEEE, pp. 73–77 (2017)
162.
Fathi, M.H., Khanli, L.M.: Consolidating VMS in green cloud computing using harmony search algorithm. In: Proceedings of the 2018 international conference on internet and e-business. ACM, pp. 146–151 (2018)
163.
Yuan, Y., Xu, H., Yang, J.: A hybrid harmony search algorithm for the flexible job shop scheduling problem. Appl. Soft Comput. 13(7), 3259–3272 (2013)CrossRef
164.
Agrawal, M., Bansal, R., Choudhary, A., Agrawal, A.: Hetrogenous computing task scheduling using improved harmony search optimization. In: 2018 international conference on advances in computing, communication control and networking (ICACCCN). IEEE, pp. 11–15 (2018)
165.
Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R.: The social engineering optimizer (SEO). Eng. Appl. Artif. Intell. 72, 267–293 (2018)CrossRef
166.
Glover, F.: Heuristics for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977)CrossRef
167.
168.
Glover, F., McMillan, C.: The general employee scheduling problem. An integration of MS and AI. Comput. Operat. Res. 13(5), 563–573 (1986)CrossRef
169.
Moscato, P., et al.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report 826, 1989 (1989)
170.
Battiti, R., Brunato, M.: Reactive search optimization: learning while optimizing. In: Handbook of Metaheuristics, pp. 543–571. Springer, Berlin (2010)
171.
Voudouris, C., Tsang, E.: Partial constraint satisfaction problems and guided local search. In: Proc., practical application of constraint technology (PACT’96), London, pp. 337–356 (1996)
172.
Storn, R., Price, K.: Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetMATHCrossRef
173.
174.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
175.
Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in internet hosting centers. Adapt. Behav. 12(3–4), 223–240 (2004)CrossRef
176.
Krishnanand, K., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS, pp. 84–91 (2005)
177.
Haddad, O.B., Afshar, A., Mariño, M.A.: Honey-bees mating optimization (hbmo) algorithm: a new heuristic approach for water resources optimization. Water Resour. Manag. 20(5), 661–680 (2006)CrossRef
178.
Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, pp. 4661–4667 (2007)
179.
Hosseini, H.S.: Problem solving by intelligent water drops. In: 2007 IEEE congress on evolutionary computation. IEEE, pp. 3226–3231 (2007)
180.
Yang, X.-S.: Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, pp. 169–178. Springer, Berlin (2009)
181.
Kashan, A.H.: League championship algorithm: a new algorithm for numerical function optimization. In: 2009 international conference of soft computing and pattern recognition. IEEE, pp. 43–48 (2009)
182.
Tamura, K., Yasuda, K.: Spiral dynamics inspired optimization. J. Adv. Comput. Intell. Intell. Inf. 15(8), 1116–1122 (2011)CrossRef
183.
Shah-Hosseini, H.: Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int. J. Comput. Sci. Eng. 6(1–2), 132–140 (2011)
184.
Civicioglu, P.: Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput. Geosci. 46, 229–247 (2012)CrossRef
185.
Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)MathSciNetMATHCrossRef
186.
Neshat, M., Sepidnam, G., Sargolzaei, M.: Swallow swarm optimization algorithm: a new method to optimization. Neural Comput. Appl. 23(2), 429–454 (2013)CrossRef
187.
Hajiaghaei-Keshteli, M., Aminnayeri, M.: Keshtel algorithm (ka); a new optimization algorithm inspired by keshtels feeding. In: Proceeding in IEEE conference on industrial engineering and management systems, pp. 2249–2253 (2013)
188.
Meng, X., Liu, Y., Gao, X., Zhang, H.: A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence, pp. 86–94. Springer, Berlin (2014)
189.
Gandomi, A.H.: Interior search algorithm (isa): a novel approach for global optimization. ISA Trans. 53(4), 1168–1183 (2014)CrossRef
190.
Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)
191.
Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)CrossRef
192.
Hudaib, A.A., Fakhouri, H.N.: Supernova optimizer: a novel natural inspired meta-heuristic. Mod. Appl. Sci. 12(1), 32–50 (2018)CrossRef
193.
Pijarski, P., Kacejko, P.: A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Eng. Optim. 1–20 (2019)
194.
Gogulan, R., Kavitha, A., Kumar, U.K.: An multiple pheromone algorithm for cloud scheduling with various GOS requirements. Intl. J. Comput. Sci. Issues (IJCSI) 9(3), 232 (2012)
195.
Fidanova, S., Durchova, M.: Ant algorithm for grid scheduling problem. In: International conference on large-scale scientific computing, pp. 405–412. Springer, Berlin (2005)
196.
Idris, H., Ezugwu, A.E., Junaidu, S.B., Adewumi, A.O.: An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems. PLoS ONE 12(5), e0177567 (2017)CrossRef
197.
Ku-Mahamud, K.R., Nasir, H.J.A.: Ant colony algorithm for job scheduling in grid computing. In: 2010 fourth Asia international conference on mathematical/analytical modelling and computer simulation. IEEE, pp. 40–45 (2010)
198.
Maipan-uku, J., Konjaang, J.K., Baba, A.I.: New batch mode scheduling strategy for grid computing system. Int. J. Eng. Technol. 8, 1314–1323 (2016)
199.
Cao, J., Spooner, D.P., Jarvis, S.A., Nudd, G.R.: Grid load balancing using intelligent agents. Fut. Gener. Comput. Syst. 21(1), 135–149 (2005)CrossRef
200.
Milan, S.T., Rajabion, L., Ranjbar, H., Navimipoir, N.J.: Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments. Comput. Operat. Res. (2019)
201.
Mostafaie, T., Khiyabani, F.M., Navimipour, N.J.: A systematic study on meta-heuristic approaches for solving the graph coloring problem. Comput. Operat. Res. 104850 (2019)
202.
Hussain, K., Salleh, M.N.M., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 52(4), 2191–2233 (2019)CrossRef
203.
Soltani, N., Soleimani, B., Barekatain, B.: Heuristic algorithms for task scheduling in cloud computing: a survey. Intl. J. Comput. Netw. Inf. Secur. 9(8), 16 (2017)
204.
Ramakrishnan, L., Plale, B.: A multi-dimensional classification model for scientific workflow characteristics. In: Proceedings of the 1st international workshop on workflow approaches to new data-centric science, pp. 1–12 (2010)
205.
Buyya, R., Calheiros, R.N., Dastjerdi, A.V.: Big Data: Principles and Paradigms. Morgan Kaufmann, San Fransico (2016)
206.
Mohapatra, S., Panigrahi, C.R., Pati, B., Mishra, M.: A comparative study of task scheduling algorithm in cloud computing. In: Advanced computing and intelligent engineering, pp. 325–338. Springer, Berlin (2020)
207.
Goren, H.G., Tunali, S., Jans, R.: A review of applications of genetic algorithms in lot sizing. J. Intell. Manuf. 21(4), 575–590 (2010)CrossRef
Metadata
Title
Meta-heuristic Approaches for Effective Scheduling in Infrastructure as a Service Cloud: A Systematic Review
Authors
J. Kok Konjaang
Lina Xu
Publication date
01-04-2021
Publisher
Springer US
Published in
Journal of Network and Systems Management / Issue 2/2021
Print ISSN: 1064-7570
Electronic ISSN: 1573-7705
DOI
https://doi.org/10.1007/s10922-020-09577-2

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