Top

Knowledge and Information Systems

Published in:

12-04-2017 | Survey Paper

A review of task scheduling based on meta-heuristics approach in cloud computing

Authors: Poonam Singh, Maitreyee Dutta, Naveen Aggarwal

Published in: Knowledge and Information Systems | Issue 1/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application. Future research issues have also been suggested in this research work.

next article A user parameter-free approach for mining robust sequential classification rules

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Ranjan R, Buyya R (2009) Decentralized overlay for federation of enterprise clouds. Handb Res Scalable Comput Technol. doi:10.4018/978-1-60566-661-7.ch009

Stevens T, De Leenheer M, Develder C et al (2009) Multi-cost job routing and scheduling in grid networks. Future Gener Comput Syst 25:912–925. doi:10.1016/j.future.2008.08.004 CrossRef

Yu J, Buyya R, Ramamohanarao K (2008) Workflow scheduling algorithms for grid computing. In: Studied computer intelligence, pp 173–214

Shirazi B, Hurson A, Kavi K (1995) Introduction to scheduling and load balancing. IEEE Computer Society

Juve G, Deelman E (2011) Scientific workflows in the cloud. In: Cafaro M, Aloisio G (eds) Grids, clouds and virtualization. Springer, London, pp 71–91CrossRef

Li X, Song J, Huang B (2015) A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics. Int J Adv Manuf Technol 84:119–131. doi:10.1007/s00170-015-7804-9 CrossRef

Szabo C, Sheng QZ, Kroeger T et al (2014) Science in the cloud: allocation and execution of data-intensive scientific workflows. J Grid Comput 12:245–264. doi:10.1007/s10723-013-9282-3 CrossRef

Pathirage M, Perera S, Kumara I, Weerawarana S (2011) A multi-tenant architecture for business process executions. In: IEEE 9th international conference on web services, pp 121–128

Kwok Y-K, Ahmad I (1999) Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv 31:406–471. doi:10.1145/344588.344618 CrossRef

10.

Y J, Buyya R (2005) A taxonomy of workflow management systems for grid computing. J Grid Comput 3:171–200CrossRef

11.

Wieczorek M, Hoheisel A, Prodan R (2009) Towards a general model of the multi-criteria workflow scheduling on the grid. Future Gener Comput Syst 25:237–256. doi:10.1016/j.future.2008.09.002 CrossRef

12.

Garey MR, Johnson DS (1990) Computers and intractability: a guide to the theory of NP-completeness. W.H. Freeman & Co., New YorkMATH

13.

MadadyarAdeh M, Bagherzadeh J (2011) An improved ant algorithm for grid scheduling problem using biased initial ants. In: 3rd international conference on computer research and development, pp 373–378

14.

Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley, LondonMATHCrossRef

15.

Hollingsworth D (1993) Workflow management coalition: the workflow reference model. Work Manag Coalit 59:904–913. doi:10.1007/s00101-010-1752-4

16.

Ranaldo N, Zimeo E (2009) Time and cost-driven scheduling of data parallel tasks in grid workflows. IEEE Syst J 3:104–120. doi:10.1109/JSYST.2008.2011299 CrossRef

17.

Wu Q, Yun D, Lin X, et al (2013) On Workflow scheduling for end-to-end performance optimization in distributed network environments. In: Lecture notes in computer science (Job Sched. Strateg. Parallel Process) pp 76–95

18.

Abrishami S, Naghibzadeh M, Epema DHJ (2012) Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans Parallel Distrib Syst 23:1400–1414. doi:10.1109/TPDS.2011.303 CrossRef

19.

Sellami K, Ahmed Nacer M, Tiako PF, Chelouah R (2013) Immune genetic algorithm for scheduling service workflows with QoS constraints in cloud computing. S Afr J Ind Eng 24:68–82

20.

Huang J (2014) The workflow task scheduling algorithm based on the GA model in the cloud computing environment. J Softw 9:873–880. doi:10.4304/jsw.9.4.873-880

21.

Zhao C (2009) Independent tasks scheduling based on genetic algorithm in cloud computing. In: 5th international conference on wireless communications network of mobile computers, pp 1–4

22.

Yassa S, Chelouah R, Kadima H, Granado B (2013) Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci World J 2013:1–13. doi:10.1155/2013/350934 MATHCrossRef

23.

Delavar AG, Aryan Y (2014) HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Clust Comput J Netw Softw Tools Appl 17:129–137. doi:10.1007/s10586-013-0275-6

24.

Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program J 14:217–230

25.

Poola D, Garg SK, Buyya R, et al (2014) Robust scheduling of scientific workflows with deadline and budget constraints in clouds. In: International conference on advanced information networking applications robust. IEEE, pp 858–865

26.

Wang Y, Shi W (2013) On scheduling algorithms for mapreduce jobs in heterogeneous clouds with budget constraints. In: Baldoni R, Nisse N, van Steen M (eds) Princeton distribution system. Springer, Berlin, pp 251–265

27.

Wang Y, Shi W (2015) Budget-driven scheduling algorithms for batches of mapreduce jobs in heterogeneous clouds. IEEE Trans Cloud Comput 2:306–319CrossRef

28.

Abrishami S, Naghibzadeh M (2012) Deadline-constrained workflow scheduling in software as a service cloud. Sci Iran 19:680–689. doi:10.1016/j.scient.2011.11.047 CrossRef

29.

Rodriguez MA, Buyya R (2014) Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2:222–235. doi:10.1109/TCC.2014.2314655 CrossRef

30.

Marcon DS, Bittencourt LF, Dantas R, et al (2013) Workflow specification and scheduling with security constraints in hybrid clouds. In: 2nd IEEE Latin America Conference Cloud Computing and Communications, pp 29–34

31.

Jianfang C, Junjie C, Qingshan Z (2014) An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm. Cybern Inf Technol 14:25–39. doi:10.2478/cait-2014-0003 MathSciNet

32.

Gonzalez N, Miers C, Redígolo F et al (2012) A quantitative analysis of current security concerns and solutions for cloud computing. J Cloud Comput Adv Syst Appl 1:11. doi:10.1186/2192-113X-1-11 CrossRef

33.

Chunlin L, Layuan L (2006) QoS based resource scheduling by computational economy in computational grid. Inf Process Lett 98:119–126. doi:10.1016/j.ipl.2006.01.002 MathSciNetMATHCrossRef

34.

Prodan R, Wieczorek M (2010) Bi-criteria scheduling of scientific grid workflows. IEEE Trans Autom Sci Eng 7:364–376CrossRef

35.

Wang X, Shin C, Buyya R, Su J (2011) Optimizing makespan and reliability for workflow applications with reputation and look-ahead genetic algorithm. Future Gener Comput Syst 27:1124–1134CrossRef

36.

Hwang E, Kim KH (2012) Minimizing cost of virtual machines for deadline-constrained mapreduce applications in the cloud. In: 13th ACM/IEEE international conference on grid computing minimizing, pp 130–138

37.

Li K, Xu G, Zhao G, et al (2011) Cloud task scheduling based on load balancing ant colony optimization. In: Sixth annual Chinagrid conference, pp 3–9

38.

Ma J (2010) A novel heuristic genetic load balancing algorithm in grid computing. In: 2nd international conference on intelligent human-machine systems and cybernetics, pp 166–169

39.

Hu Y, Xing L, Zhang W, et al (2010) A knowledge-based ant colony optimization for a grid workflow scheduling problem. In: Advanced swarm intelligence notes computer science, pp 241–248

40.

Fan Z, Shen H, Wu Y, et al (2013) Simulated-annealing load balancing for resource allocation in cloud environments. In: International conference on parallel and distributed computing applications and technologies simulated-annealing, pp 1–6

41.

Singhal U, Jain S (2014) A new fuzzy logic and GSO based load balancing mechanism for public cloud. Int J Grid Distrib Comput 7:97–110CrossRef

42.

Xue S, Li M, Xu X, Chen J (2014) An ACO-LB algorithm for task scheduling in the cloud environment. J Softw 9:466–473. doi:10.4304/jsw.9.2.466-473

43.

Alejandra M, Sossa R (2011) Cost minimization heuristics for scheduling workflows on heterogeneous distributed environments. The University of Melbourne

44.

Rajni Chana I (2013) Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. Future Gener Comput Syst 29:751–762. doi:10.1016/j.future.2012.09.005 CrossRef

45.

Lin J, Zhong Y, Lin X, et al (2014) Hybrid ant colony algorithm clonal selection in the application of the cloud ’s resource scheduling

46.

Sakellariou R, Zhao H (2004) A low-cost rescheduling policy for efficient mapping of workflows on grid systems. Sci Program 12:253–262

47.

Liu K (2009) Scheduling algorithms for instance-intensive cloud workflows. Swinburne University of Technology

48.

Wang X, Wang Y, Zhu H (2012) Energy-efficient multi-job scheduling model for cloud computing and its genetic algorithm. Math Probl Eng 2012:1–16. doi:10.1155/2012/589243 MathSciNetMATH

49.

Negru C, Pop F, Cristea V, et al (2013) Energy efficient cloud storage service: key issues and challenges. In: 2013 4th international conference emerging intelligence data web technologied, pp 763–766

50.

Shu W, Wang W, Wang Y (2014) A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP J Wirel Commun Netw 2014:64. doi:10.1186/1687-1499-2014-64 CrossRef

51.

Tsai C, Rodrigues JJPC (2014) Metaheuristic scheduling for cloud: a survey. IEEE Syst J 8:279–291CrossRef

52.

Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egypt Inf J 16:275–295. doi:10.1016/j.eij.2015.07.001 CrossRef

53.

Poonam, Dutta M, Aggarwal N (2016) Meta-Heuristics Based Approach for Work flow Scheduling in Cloud Computing: a Survey. In: Advanced Intelligent System of Computing, pp 1331–1345

54.

Wu F, Wu Q, Tan Y (2015) Workflow scheduling in cloud: a survey. J Supercomput 71:3373–3418. doi:10.1007/s11227-015-1438-4 CrossRef

55.

Alkhanak EN, Lee SP, Khan SUR (2015) Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities. Future Gener Comput Syst. doi:10.1016/j.future.2015.01.007

56.

Branch U (2016) Towards workflow scheduling in cloud computing? a comprehensive analysis. J Netw Comput Appl 66:64–82. doi:10.1016/j.jnca.2016.01.018 CrossRef

57.

Holland JH (1975) Adaptation in natural and artificial systems

58.

Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing Co

59.

Pop F, Dobre C, Cristea V (2009) Genetic algorithm for DAG scheduling in grid environments. In: IEEE 5th international conference on intelligence computer communication Process, pp 299–305

60.

Dasgupta K, Mandal B, Dutta P, Kumar J (2013) A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol 10:340–347. doi:10.1016/j.protcy.2013.12.369 CrossRef

61.

Ge Y, Wei G (2010) GA-based task scheduler for the cloud computing systems. In: International conference web information system Mining, WISM 2010. pp 181–186

62.

Zheng Z, Wang R, Zhong H, Zhang X (2011) An approach for cloud resource scheduling based on Parallel Genetic Algorithm. In: 3rd international conference on computer research devices, pp 444–447

63.

Wang T, Liu Z, Chen Y, et al (2014) Load balancing task scheduling based on genetic algorithm in cloud computing. In: IEEE 12th international conference on dependable, autonomic security computing, pp 146–152

64.

Jang SH, Kim TY, Kim JK, Lee JS (2012) The study of genetic algorithm-based task scheduling for cloud computing. Int J Control Autom 5:157–162

65.

Liu J, Luo X, Zhang X et al (2013) Job scheduling model for cloud computing based on multi-objective genetic algorithm. Int J Comput Sci Issues 10:134–139

66.

Kaur K, Chharbra A, Gurvinder Singh (2010) Heuristics based genetic algorithm for scheduling static tasks in homogeneous parallel system. J Comput Sci Secur 4:183–198

67.

Fanian A, Gulliver TA, Canada BC (2013) Fast workflow scheduling for grid computing based on a multi-objective genetic algorithm. In: IEEE Pacific Rim conference on communication computer signal process, pp 96–101

68.

Gu J (2012) A new resource scheduling strategy based on genetic algorithm in cloud computing environment. J Comput 7:42–52. doi:10.4304/jcp.7.1.42-52

69.

Nasonov D, Butakov N, Balakhontseva M et al (2014) Hybrid evolutionary workflow scheduling algorithm for dynamic heterogeneous distributed computational environment. Adv Intell Syst Comput 299:83–92. doi:10.1007/978-3-319-07995-0_9

70.

Shen G, Zhang Y (2011) A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers. Adv Swarm Intell 6728:522–529CrossRef

71.

Kolodziej J, Khan SU, Xhafa F (2011) Genetic algorithms for energy-aware scheduling in computational grids. In: International conference on P2P, parallel, grid, cloud internet computing (3PGCIC), pp 17–24

72.

Zhu K, Song H, Liu L, et al (2011) Hybrid genetic algorithm for cloud computing applications. In: IEEE Asia-Pacific services computing conference, pp 182–187

73.

Sawant S (2011) A genetic algorithm scheduling approach for virtual machine resources in a cloud computing environment. San Jose State University

74.

Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurr Comput Program, C3P Rep 826:1989

75.

Merz P, Freisleben B (1997) A genetic local search approach to the quadratic assignment problem. In: 7th international conference on genetic algorithms, p 1

76.

Jouglet A, Oğuz C, Sevaux M (2009) Hybrid flow-shop: a memetic algorithm using constraint-based scheduling for efficient search. J Math Model Algorithms 8:271–292. doi:10.1007/s10852-008-9101-1 MATHCrossRef

77.

Moscato P, Norman MG (1992) A “Memetic” approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. In: International conference on parallel computing transputer applications. IOS Press, pp 177–186

78.

Kashani MH, Jahanshahi M. A new method based on memetic algorithm for task scheduling in distributed systems. Int J Simul Syst Sci Technol. 10

79.

Padmavathi S, Shalinie SM, Abhilaash R (2010) A memetic algorithm based task scheduling considering communication cost on cluster of workstations. Int J Adv Soft Comput Appl 2:174–190

80.

Sutar P, Sawant J, Jadhav J (2006) Task scheduling for multiprocessor systems using memetic algorithms. In: International conference on performance modeling evaluation heterenogeneous networks, pp 1–9

81.

Zhao F, Tang J (2012) A memetic algorithm combined particle swarm optimization with simulated annealing and its application on multiprocessor scheduling problem. Prz Elektrotechniczny 88:292–296

82.

Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation 2007. CEC 2007. IEEE Congress, pp 4661–4667

83.

Behnamian J, Zandieh M (2011) A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Syst Appl 38:14490–14498. doi:10.1016/j.eswa.2011.04.241 CrossRef

84.

Attar SF (2011) A novel imperialist competitive algorithm to solve flexible flow shop scheduling problem in order to minimize maximum completion time. Int J Comput Appl 28:27–32

85.

Madani-isfahani M, Ghobadian E, Tekmehdash HI et al (2009) An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration. Int J Ind Eng Comput 4:191–202. doi:10.5267/j.ijiec.2013.02.002

86.

Yakhchi S, Ghafari SM, Yakhchi M et al (2015) ICA-MMT: a load balancing method in cloud computing environment. In: 2nd World symposium web application networks IEEE, pp 1–7

87.

Yousefyan S, Dastjerdi A V, Salehnamadi MR (2013) Cost effective cloud resource provisioning with imperialist competitive algorithm optimization. In: 5th Conference on information knowledge technology, pp 55–60

88.

Pooraniana Z, Shojafar M, Javadi B, Abraham A (2014) Using imperialist competition algorithm for independent task scheduling in grid computing. J Intell Fuzzy Syst 27:1–16. doi:10.3233/IFS-130988

89.

Ahmadi M (2015) Cloud data centers using the imperialist competitive algorithm. In: IEEE fifth international conference on big data cloud computing, IEEE, pp 62–67

90.

Piroozfard H, Wong KY (2014) An imperialist competitive algorithm for the job shop scheduling problems. In: IEEE international conference on industrial engineering management, pp 69–73

91.

Jula A, Othman Z, Sundararajan E (2013) A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition. In: IEEE working of memetic computing, pp 37–43

92.

Jula A, Othman Z, Sundararajan E (2015) Expert systems with applications imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition. Expert Syst Appl 42:135–145. doi:10.1016/j.eswa.2014.07.043 CrossRef

93.

Fatemipour F, Fatemipour F (2012) Scheduling scientific workflows using imperialist competitive algorithm. In: International conference on industrial intelligent information (ICIII 2012), pp 218–225

94.

Faragardi HR, Rajabi A, Shojaee R, Nolte T (2013) Towards energy-aware resource scheduling to maximize reliability in cloud computing systems. In: IEEE international conference on high performance computing communication international conference on embeded ubiquitous computing, pp 1469–1479

95.

Rajakumar BR (2012) The lion’s algorithm: a new nature-inspired search algorithm. Procedia Technol 6:126–135. doi:10.1016/j.protcy.2012.10.016 CrossRef

96.

Yazdani M, Jolai F (2015) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng. doi:10.1016/j.jcde.2015.06.003

97.

Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 19:264–279. doi:10.1016/j.asoc.2014.01.036 CrossRef

98.

Aryan Y, Delavar AG (2014) A bi-objective workflow application scheduling in cloud computing systems. Int J Integr Technol Educ 3:51–62CrossRef

99.

Vidyarthi DP, Tripathi AK (2001) Maximizing reliability of distributed computing system with task allocation using simple genetic algorithm. J Syst Archit 47:549–554. doi:10.1016/S1383-7621(01)00013-3 CrossRef

100.

Verma A, Kaushal S (2013) Budget constrained priority based genetic algorithm for workflow scheduling in cloud. In: Fifth international conference on advanced recent technology communication computing IET, pp 216–222

101.

Barrett E, Duggan J (2011) A learning architecture for scheduling workflow applications in the cloud. In: Ninth IEEE European conference on web service, pp 83–90

102.

Javanmardi S, Shojafar M, Amendola D, et al (2014) Hybrid job scheduling algorithm for cloud computing environment. In: Fifth international conference innovationa bio-inspired computer applications IBICA 2014, pp 43–52

103.

Kaur S, Verma A (2012) An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int J Inf Technol Comput Sci 4:74–79. doi:10.5815/ijitcs.2012.10.09

104.

Abarghoei A, Mahdipour E, Askarzadeh M (2015) Cloud computing resource planning based on imperialist competitive algorithm. Cumhur Sci J 36:1312–1324

105.

Arshad R, Rafeh R (2015) Deadline-constrained workflow scheduling using imperialist competitive algorithm on infrastructure as a service clouds. In: International conference on knowledge-based engineering innovation, pp 835–842

106.

Fayazi M (2016) Resource allocation in cloud computing using imperialist competitive algorithm with reliability approach. Int J Adv Comput Sci Appl 7:323–331

107.

Yang X (2014) Nature-inspired optimization algorithms. nature-inspired optim algorithms. doi:10.1016/B978-0-12-416743-8.00017-8

108.

Madureira A, Ipp I (2005) Swarm intelligence for scheduling: a review. In: International conference on business sustain, pp 1–8

109.

Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278. doi:10.1016/j.tcs.2005.05.020 MathSciNetMATHCrossRef

110.

Chiang C-W, Lee Y-C, Lee C-N, Chou T-Y (2006) Ant colony optimisation for task matching and scheduling. IEE Proc Comput Digit Tech 153:373–380CrossRef

111.

Chen W-N, Zhang J, Yu Y (2007) Workflow scheduling in grids: an ant colony optimization approach. In: Evolutionary computation 2007. CEC 2007. IEEE Congress, pp 3308–3315

112.

Chen WN, Shi Y, Zhang J (2009) An ant colony optimization algorithm for the time-varying workflow scheduling problem in grids. IEEE Congr Evol Comput CEC 2009:875–880. doi:10.1109/CEC.2009.4983037

113.

Pacini E, Mateos C, García C (2015) Advances in engineering software balancing throughput and response time in online scientific clouds via ant colony optimization. Adv Eng Softw 84:31–47. doi:10.1016/j.advengsoft.2015.01.005 CrossRef

114.

Liu X-F, Zhan Z-H, Du K-J, Chen W-N (2014) Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In: Annual conference genetic evolution computing. ACM, New York, pp 41–48

115.

Chimakurthi L, Madhu Kumar S (2011) Power efficient resource allocation for clouds using ant colony framework. Comput Res Repos abs/1102.2

116.

Mathiyalagan P, Suriya S, Sivanandam SN (2010) Modified ant colony algorithm for grid scheduling. Int J Comput Sci Eng 2:132–139.

117.

Liu A, Wang Z (2008) Grid task scheduling based on adaptive ant colony algorithm. In: International conference on management e-commerce e-government grid. pp 415–418

118.

Bagherzadeh J, MadadyarAdeh M (2009) An improved ant algorithm for grid scheduling problem. In: 14th International CSI computing conference, pp 323–328

119.

Chen W-NCW-N, Zhang JZJ (2009) An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Trans Syst Man Cybern Part C 39:29–43. doi:10.1109/TSMCC.2008.2001722 CrossRef

120.

Tawfeek MA, El-sisi A (2013) Cloud task scheduling based on ant colony optimization. In: 8th International conference on computing engineering systems, pp 64–69

121.

Gogulan R, Kavitha MA, Kumar UK (2012) An multiple pheromone algorithm for cloud scheduling with various QOS requirements. Int J Comput Sci Issues 9:232–238

122.

Khambre PD, Deshpande A, Mehta A, Sain A (2014) Modified pheromone update rule to implement ant colony optimization algorithm for workflow scheduling algorithm problem in grids. Int J Adv Res Comput Sci Technol 2:424–429

123.

Singh L, Singh S (2014) Deadline and cost based ant colony optimization algorithm for scheduling workflow applications in hybrid cloud. Int J Sci Eng Res 5:1417–1420

124.

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948

125.

Pandey S, Wu L, Guru SM, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: IEEE international conference on advanced information networking applications, pp 400–407

126.

Wu Z, Ni Z, Gu L, Liu X (2010) A revised discrete particle swarm optimization for cloud workflow scheduling. In: International conference on computer intelligence Security CIS. pp 184–188

127.

Xue S, Wu W (2012) Scheduling workflow in cloud computing based on hybrid particle swarm algorithm. Telkomnika Indones J Electr Eng 10:1560–1566

128.

Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad-Barkousaraie a (2011) A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Syst Appl 38:10812–10821. doi:10.1016/j.eswa.2011.02.050 MATHCrossRef

129.

Chen WN, Shi Y, Zhang J (2009) An ant colony optimization algorithm for the time-varying workflow scheduling problem in grids. In: IEEE congress on evolutionary computation CEC 2009, pp 875–880. doi:10.1109/CEC.2009.4983037

130.

Karimi M, Motameni H, Branch S (2013) Tasks scheduling in computational grid using a hybrid discrete particle swarm optimization. Int J Grid Distrib Comput 6:29–38CrossRef

131.

Pooranian Z, Shojafar M, Abawajy JH, Abraham A (2015) An efficient meta-heuristic algorithm for grid computing. J Comb Optim 30:413–434. doi:10.1007/s10878-013-9644-6 MathSciNetMATHCrossRef

132.

Gomathi B, Krishnasamy K (2013) Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment. J Theor Appl Inf Technol 55:33–38

133.

Sridhar M (2015) Hybrid particle swarm optimization scheduling for cloud computing. In: IEEE international advance computing conference IEEE, pp 1196–1200

134.

Al-Maamari A, Omara Fa (2015) Task scheduling using hybrid algorithm in cloud computing environments. IOSR J Comput Eng 17:2278–2661. doi:10.9790/0661-173696106

135.

Zhang L, Chen Y, Sun R (2008) A task scheduling algorithm based on PSO for grid computing. Int J Comput Intell Res 4:37–43. doi:10.1109/ISDA.2006.253921

136.

Liu H, Abraham A, Hassanien AE (2010) Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Gener Comput Syst 26:1336–1343. doi:10.1016/j.future.2009.05.022 CrossRef

137.

Aron R, Chana I, Abraham A (2015) A hyper-heuristic approach for resource provisioning-based scheduling in grid environment. J Supercomput 71:1427–1450. doi:10.1007/s11227-014-1373-9 CrossRef

138.

Sidhu MS, Thulasiraman P, Thulasiram RK (2013) A load-rebalance PSO heuristic for task matching in heterogeneous computing systems. In: Swarm intelligence (SIS), 2013 IEEE Symposium, pp 180–187

139.

Ramezani F, Lu J, Hussain FK (2014) Task-based system load balancing in cloud computing using particle swarm optimization. Int J Parallel Program 42:739–754. doi:10.1007/s10766-013-0275-4 CrossRef

140.

Milani FS (2015) Multi-objective task scheduling in the cloud computing based on the patrice swarm optimization. Int J Inf Technol Comput Sci 5:61–66. doi:10.5815/ijitcs.2015.05.09

141.

Wang Z, Shuang K, Yang L, Yang F (2012) Energy-aware and revenue-enhancing combinatorial scheduling in virtualized of cloud datacenter. J Converg Inf Technol 7:62–70. doi:10.4156/jcit.vol7.issue1.8

142.

Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech Rep TR06, Erciyes Univ

143.

Liu Y-F, Liu S-Y (2013) A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem. Appl Soft Comput 13:1459–1463. doi:10.1016/j.asoc.2011.10.024 CrossRef

144.

Huang YM, Lin JC (2011) A new bee colony optimization algorithm with idle-time-based filtering scheme for open shop-scheduling problems. Expert Syst Appl 38:5438–5447. doi:10.1016/j.eswa.2010.10.010 CrossRef

145.

Ziarati K, Akbari R, Zeighami V (2011) On the performance of bee algorithms for resource-constrained project scheduling problem. Appl Soft Comput J 11:3720–3733. doi:10.1016/j.asoc.2011.02.002 CrossRef

146.

Karaboga D, Gorkemli B (2011) A combinatorial artificial bee colony algorithm for traveling salesman problem. In: 2011 International symposium innovation intelligent system application, pp 50–53

147.

Hashemi SM, Hanani A (2013) Solving the scheduling problem in computational grid using artificial bee colony algorithm. Adv Comput Sci Int J 2:37–41

148.

Mousavinasab Z, Entezari-maleki R, Movaghar A (2011) A bee colony task scheduling algorithm in computational grids. In: International conference on digital information processing communication. Springer, Berlin, pp 200–210

149.

De Mello RF, Senger LJ, Yang LT (2006) A routing load balancing policy for grid computing environments. In: 28th International conference on advanced information networking applications IEEE Computer Society, Los Alamitos, pp 153–158

150.

DB LD, Krishna PV (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput J 13:2292–2303. doi:10.1016/j.asoc.2013.01.025 CrossRef

151.

Soni A (2015) A bee colony based multi-objective load balancing technique for cloud computing environment. Int J Comput Appl 114:19–25

152.

Pan J, Wang H, Zhao H, Tang L (2014) Interaction artificial bee colony based load balance method in cloud computing. In: Eighth international conference on genetics evolutionary computation, pp 49–57

153.

Yeboah T, Odabi OI (2015) Hybrid bee ant colony algorithm for effective load balancing and job scheduling in cloud computing. West African J Ind Acad Res 13:54–59

154.

Priyadarsini RJ, Arockiam L (2015) PBCOPSO: A parallel optimization algorithm for task scheduling in cloud environment. Indian J Sci Technol 8:6–10. doi:10.17485/ijst/2015/v8i CrossRef

155.

Kashani MH (2011) Utilizing bee colony to solve task scheduling problem in distributed systems. In: International conference on computational intelligence on communication system networks, pp 298–303

156.

Navimipour NJ (2015) Task scheduling in the cloud environments based on an artificial bee colony algorithm. In: International conference on image processing production computer science, Istanbul (Turkey), pp 38–44

157.

Hesabian N, Haj H, Javadi S (2015) Optimal scheduling in cloud computing environment using the bee algorithm. Int J Comput Netw Commun Secur 3:253–258

158.

Garg A, Krishna CR (2014) An improved honey bees life scheduling algorithm for a public cloud. In: International conference on contemporary computing and informatics, pp 1140–1147

159.

Singh R (2015) Analysis of enhanced TDB based parallel scheduling algorithm using artificial bee colony. In: International Conference on Modelling and Simulatio Analysis UKSIM-AMSS. IEEE, pp 470–475

160.

Kumar RS (2014) Improving task scheduling in large scale cloud computing environment using artificial bee colony algorithm. Int J Comput Appl 103:29–32

161.

Udomkasemsub O, Xiaorong L, Achalakul T (2012) A multiple-objective workflow scheduling framework for cloud data analytics. In: 9th International joint conference on computer science software engineering, pp 391–398

162.

Liang Y, Chen AH, Nien Y (2014) Artificial bee colony for workflow scheduling. In: IEEE congress evolutionary computation IEEE, pp 558–564

163.

Kansal NJ, Chana I (2014) Artificial bee colony based energy-aware resource utilization technique for cloud computing. Concurr Comput Pract Exp 27:1207–1225. doi:10.1002/cpe CrossRef

164.

Yang XS (2010) A new metaheuristic bat-inspired algorithm. Stud Comput Intell 284:65–74. doi:10.1007/978-3-642-12538-6_6 MATH

165.

Mishra S, Shaw K, Mishra D (2012) A new meta-heuristic bat inspired classification approach for microarray data. Procedia Technol 4:802–806. doi:10.1016/j.protcy.2012.05.131 CrossRef

166.

Jacob L (2014) Bat algorithm for resource scheduling in cloud computing. Int J Res Appl Sci Eng Technol 2:53–57

167.

Kumar V, Aramudhan M (2014) Trust based resource selection in cloud computing using hybrid algorithm. Int J Comput Intell Informatics 4:169–176

168.

Suresh Kumar VS (2014) Hybrid optimized list scheduling and trust based resource selection in cloud computing. J Theor Appl Inf Technol 69:434–442

169.

Raghavan S, Marimuthu C, Sarwesh P, Chandrasekaran K (2015) Bat algorithm for scheduling workflow applications in cloud. In: Electronic design, computer networks & automated verification (EDCAV), 2015 international conference on IEEE, Shillong, pp 139–144

170.

George S (2015) Hybrid PSO-MOBA for profit maximization in cloud computing. Int J Adv Comput Sci Appl 6:159–163

171.

Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. PRICAI 2006 trends. Artif Intell 4099:854–858. doi:10.1007/978-3-540-36668-3_94

172.

Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J Innov Comput Inf Control 3:163–173

173.

Tsai PW, Pan JS, Chen SM, et al (2008) Parallel cat swarm optimization. In: 7th international conference on machine learning and cybernetics, ICMLC, pp 3328–3333

174.

Pradhan PM, Panda G (2012) Solving multiobjective problems using cat swarm optimization. Expert Syst Appl 39:2956–2964. doi:10.1016/j.eswa.2011.08.157 CrossRef

175.

Shojaee R, Faragardi HR, Alaee S, Yazdani N (2012) A new cat swarm optimization based algorithm for reliability-oriented task allocation in distributed systems. In: Sixth international symposium telecommunication, pp 861–866

176.

Sharafi Y, Khanesar MA, Teshnehlab M (2013) Discrete binary cat swarm optimization algorithm. In: 3rd IEEE international conference on computer, control and communication, pp 1–6

177.

Bilgaiyan S, Sagnika S, Das M (2014) Workflow scheduling in cloud computing environment using cat swarm optimization. In: Souvenir 2014 IEEE international advance computing conference, IACC 2014, pp 680–685. doi:10.1109/IAdCC.2014.6779406

178.

Bilgaiyan S, Sagnika S, Das M (2015) A multi-objective cat swarm optimization algorithm for workflow scheduling in cloud computing environment. Adv Intell Syst Comput 308:73–84. doi:10.1007/978-81-322-2012-1_9

179.

Rouhi S, Nejad EB (2015) CSO-GA: a new scheduling technique for cloud computing systems based on cat swarm optimization and genetic algorithm. Cumhur Univ Fac Sci J 36:1672–1685

180.

Poonam, Dutta M, Aggarwal N (2016) Scheduling scientific workflow applications using hybrid meta- heuristic approach in cloud computing. In: International conference on recent trends engineering material science, pp 328–329

181.

Lu X, Gu Z (2011) A load-adaptive cloud resource scheduling model based on ant colony algorithm. In: IEEE international conference cloud computing intelligence system, pp 296–300

182.

Khan S, Sharama N (2014) Effective scheduling algorithm for load balancing (SALB) using Ant colony optimization in cloud computing. Int J Adv Res Comput Sci Softw Eng 4:966–973

183.

Zhang Z, Zhang X (2010) A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. In: 2nd International conference on industrial mechatronics and automation, pp 240–243

184.

Dam S, Mandal G, Dasgupta K, Dutta P (2014) An ant colony based load balancing strategy in cloud computing. Adv Comput Netw Inform 2:403–413. doi:10.1007/978-3-319-07350-7

185.

Zhou Y, Huang X (2014) Scheduling workflow in cloud computing based on ant colony optimization algorithm. In: Sixth international conference on business intelligence and financial engineering scheduling, pp 57–61

186.

Liu W, Peng S, Du W et al (2014) Security-aware intermediate data placement strategy in scientific cloud workflows. Knowl Inf Syst 41:423–447. doi:10.1007/s10115-014-0755-x CrossRef

187.

Yin P-Y, Yu S-S, Wang P-P, Wang Y-T (2007) Task allocation for maximizing reliability of a distributed system using hybrid particle swarm optimization. J Syst Softw 80:724–735. doi:10.1016/j.jss.2006.08.005 CrossRef

188.

Izakian H, Ladani BT, Zamanifar K, Abraham A (2009) A novel particle swarm optimization approach for grid job scheduling. Inf Syst Technol Manag 31:100–109. doi:10.1007/978-3-642-00405-6_14

189.

Guo L, Zhao S, Shen S, Jiang C (2012) Task scheduling optimization in cloud computing based on heuristic algorithm. J Netw 7:547–553. doi:10.4304/jnw.7.3.547-553

190.

Abdi S, Motamedi SA, Sharifian S (2014) Task scheduling using modified PSO algorithm in cloud computing environment. In: International conference on machine learning, electrical and mechanical engineering, pp 37–41

191.

Chen W, Zhang J, Author C (2012) A set-based discrete PSO for cloud workflow scheduling with user-defined QoS constraints. In: International conference on systems, man, cybernetics, pp 773–778

192.

Pacini E, Mateos C, Garc C (2014) Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electron J 14:1–14

193.

Huang J, Wu K, Leong LK et al (2013) A tunable workflow scheduling algorithm based on particle swarm optimization for cloud computing. Int J Soft Comput Softw Eng 3:351–358. doi:10.7321/jscse.v3.n3.53

194.

Verma A (2015) Cost minimized PSO based workflow scheduling plan for cloud computing. Int J Inf Technol Comput Sci 8:37–43. doi:10.5815/ijitcs.2015.08.06

195.

Verma A, Kaushal S (2014) Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In: Recent advances in engineering and computational sciences, pp 6–8

196.

Chitra S, Madhusudhanan B, Sakthidharan GR, Saravanan P (2014) Local minima jump PSO for workflow scheduling in cloud computing environments. In: Advance computing conference on science its applications, pp 1225–1234

197.

Pragaladan R, Maheswari R (2014) Improve workflow scheduling technique for novel particle swarm optimization in cloud environment. Int J Eng Res Gen Sci 2:675–680

198.

Kruekaew B, Kimpan W (2014) Virtual machine scheduling management on cloud computing using artificial bee colony. In: International multiconference engineers and computer scientists, pp 1–5

199.

Kang QM, He H, Song HM, Deng R (2010) Task allocation for maximizing reliability of distributed computing systems using honeybee mating optimization. J Syst Softw 83:2165–2174. doi:10.1016/j.jss.2010.06.024 CrossRef

200.

Mittal U, Kumar Y, Kaur A (2015) International journal of advanced research in computer science and software engineering a novel approach of load balancing in cloud computing using cat swarm optimization technique. Int J Adv Res Comput Sci Softw Eng 5:466–471

201.

Singh G, Su M-H, Vahi K, et al (2008) Workflow task clustering for best effort systems with Pegasus. In: Mardis Gras Conference, pp 1–8

202.

Chen W, Ferreira R, Deelman E, Sakellariou R (2015) Using imbalance metrics to optimize task clustering in scientific workflow executions. Future Gener Comput Syst 46:69–85. doi:10.1016/j.future.2014.09.014 CrossRef

203.

Zhang Y, Mandal A, Koelbel C et al (2009) Combined fault tolerance and scheduling techniques for workflow applications on computational grids. In: IEEE/ACM international symposium on cluster computing and the grid, CCGRID ’09. Shanghai, pp 244–251

204.

Ferreira R, Chen W, Chen W et al (2015) Dynamic and fault-tolerant clustering for scientific workflows. IEEE Trans Cloud Comput 4:49–62. doi:10.1109/TCC.2015.2427200

205.

Singh G, Vahi K, Ramakrishnan A et al (2007) Optimizing workflow data footprint. Sci Program 15:249–268

206.

Ramakrishnan A, Singh G, Zhao H, et al (2007) Scheduling data-intensive workflows onto storage-constrained distributed. In: 7th IEEE international symposium on cluster computing and the grid, pp 401–409

207.

Yuan D, Yang Y, Liu X, Chen J (2010) A cost-effective strategy for intermediate data storage in scientific cloud workflow systems. In: IEEE international symposium on parallel and distributed processing IEEE, pp 1–12

208.

Yuan D, Yang Y, Liu X et al (2012) A data dependency based strategy for intermediate data storage in scientific cloud workflow systems. Concurr Comput Pract Exp 24:956–976. doi:10.1002/cpe.1636 CrossRef

209.

Lin X, Wu CQ (2013) On scientific workflow scheduling in clouds under budget constraint. In: 42nd international conference on parallel processing, IEEE, pp 90–99

210.

Niyoyita JP, Dong S (2015) Storage-aware task scheduling with reliable resource selection. J Comput Inf Syst 11:123–131. doi:10.12733/jcis12798

211.

Wen X, Huang M, Shi J (2012) Study on resources scheduling based on ACO algorithm and PSO algorithm in cloud computing. In: International symposium on distributed computing and applications to business, engineering and science, pp 219–222

212.

Mathiyalagan P, Sivanandam SN, Saranya KS (2013) Hybridization of modified ant colony optimization and intelligent water drops algorithm for job scheduling incomputational grid. ICTACT J Soft Comput 4:651–655CrossRef

213.

Cho K, Tsai P, Tsai C, Yang C-S (2014) A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput Appl 26:1297–1309. doi:10.1007/s00521-014-1804-9 CrossRef

214.

Madivi R (2014) An hybrid bio-inspired task scheduling algorithm in cloud environment. In: International conference on computing, communication and networking technologies, IEEE, pp 1–7

215.

Moschakis IA, Karatza HD (2015) Towards scheduling for Internet-of-things applications on clouds: a simulated annealing approach. Concurr Comput Pract Exp 27:1886–1899. doi:10.1002/cpe.3105 CrossRef

216.

Khajehvand V, Pedram H, Zandieh M (2013) SCTTS: scalable cost-time trade-off scheduling for workflow application in grids. KSII Trans Internet Inf Syst 7:3096–3117CrossRef

217.

Gil Y, Deelman E, Ellisman M et al (2007) Examining the challenges of scientific workflows. Computer (Long Beach Calif) 40:24–32. doi:10.1109/MC.2007.421

218.

Deelman E (2010) Grids and clouds: making workflow applications work in heterogeneous distributed environments. Int J High Perform Comput Appl 24:284–298. doi:10.1177/1094342009356432 CrossRef

219.

Singh S, Chana I (2016) Cloud resource provisioning: survey, status and future research directions. Knowl Inf Syst 49:1005–1069. doi:10.1007/s10115-016-0922-3 CrossRef

220.

Yousafzai A, Gani A, Noor RM et al (2016) Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowl Inf Syst. doi:10.1007/s10115-016-0951-y

221.

Byun EK, Kee YS, Kim JS, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gener Comput Syst 27:1011–1026. doi:10.1016/j.future.2011.05.001 CrossRef

222.

Bala A, Chana I (2015) Autonomic fault tolerant scheduling approach for scientific workflows in cloud computing. Concurr Eng Res Appl 23:27–39. doi:10.1177/1063293X14567783 CrossRef

223.

Yu Z, Wang C, Shi W (2010) FLAW: failure-aware workflow scheduling in high performance computing systems. J Clust Comput 13:421–434CrossRef

224.

Poola D, Garg SK, Buyya R et al (2014) 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. doi:10.1109/AINA.2014.105

225.

Tang X, Li K, Liao G (2014) An effective reliability-driven technique of allocating tasks on heterogeneous cluster systems. Cluster Comput 17:1413–1425. doi:10.1007/s10586-014-0372-1 CrossRef

226.

Fard H, Prodan R, Barrionuevo JJD, Fahringer T (2012) A multi-objective approach for workflow scheduling in heterogeneous environments. 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing, pp 300–309. doi:10.1109/CCGrid.2012.114

227.

Bryk P, Malawski M, Juve G (2015) Storage-aware algorithms for scheduling of workflow ensembles in clouds. J Grid Comput. doi:10.1007/s10723-015-9355-6

228.

Delavar AG, Aryan Y (2012) A goal-oriented workflow scheduling in heterogeneous distributed systems. Int J Comput Appl 52:27–33

229.

Verma A, Kaushal S (2012) Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. In: International conference on recent advances and future trends in information technology, pp 1–4

230.

Singh R, Singh S (2013) Score based deadline constrained workflow scheduling algorithm for cloud systems. Int J Cloud Comput Serv Archit 3:31–41

Title: A review of task scheduling based on meta-heuristics approach in cloud computing
Authors: Poonam Singh
Maitreyee Dutta
Naveen Aggarwal
Publication date: 12-04-2017
Publisher: Springer London
Published in: Knowledge and Information Systems / Issue 1/2017
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-017-1044-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 1/2017

Random indexing of multidimensional data

A user parameter-free approach for mining robust sequential classification rules

Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filtering

Identification of critical situations via Event Processing and Event Trust Analysis

Shall I post this now? Optimized, delay-based privacy protection in social networks

rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking

Premium Partner