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Knowledge and Information Systems

, Volume 52, Issue 1, pp 1–51 | Cite as

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

  • Poonam SinghEmail author
  • Maitreyee Dutta
  • Naveen Aggarwal
Survey Paper

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.

Keywords

Workflows scheduling Cloud computing Swarm intelligence Bio-inspired Meta-heuristics Optimization 

References

  1. 1.
    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
  2. 2.
    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 CrossRefGoogle Scholar
  3. 3.
    Yu J, Buyya R, Ramamohanarao K (2008) Workflow scheduling algorithms for grid computing. In: Studied computer intelligence, pp 173–214Google Scholar
  4. 4.
    Shirazi B, Hurson A, Kavi K (1995) Introduction to scheduling and load balancing. IEEE Computer SocietyGoogle Scholar
  5. 5.
    Juve G, Deelman E (2011) Scientific workflows in the cloud. In: Cafaro M, Aloisio G (eds) Grids, clouds and virtualization. Springer, London, pp 71–91CrossRefGoogle Scholar
  6. 6.
    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 CrossRefGoogle Scholar
  7. 7.
    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 CrossRefGoogle Scholar
  8. 8.
    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 121–128Google Scholar
  9. 9.
    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 CrossRefGoogle Scholar
  10. 10.
    Y J, Buyya R (2005) A taxonomy of workflow management systems for grid computing. J Grid Comput 3:171–200CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 12.
    Garey MR, Johnson DS (1990) Computers and intractability: a guide to the theory of NP-completeness. W.H. Freeman & Co., New YorkzbMATHGoogle Scholar
  13. 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 373–378Google Scholar
  14. 14.
    Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley, LondonzbMATHCrossRefGoogle Scholar
  15. 15.
    Hollingsworth D (1993) Workflow management coalition: the workflow reference model. Work Manag Coalit 59:904–913. doi: 10.1007/s00101-010-1752-4 Google Scholar
  16. 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 CrossRefGoogle Scholar
  17. 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 76–95Google Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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 24:68–82Google Scholar
  20. 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 Google Scholar
  21. 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 1–4Google Scholar
  22. 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 zbMATHCrossRefGoogle Scholar
  23. 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 Google Scholar
  24. 24.
    Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program J 14:217–230Google Scholar
  25. 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 858–865Google Scholar
  26. 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 251–265Google Scholar
  27. 27.
    Wang Y, Shi W (2015) Budget-driven scheduling algorithms for batches of mapreduce jobs in heterogeneous clouds. IEEE Trans Cloud Comput 2:306–319CrossRefGoogle Scholar
  28. 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 CrossRefGoogle Scholar
  29. 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 CrossRefGoogle Scholar
  30. 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 29–34Google Scholar
  31. 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 MathSciNetGoogle Scholar
  32. 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 CrossRefGoogle Scholar
  33. 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 MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Prodan R, Wieczorek M (2010) Bi-criteria scheduling of scientific grid workflows. IEEE Trans Autom Sci Eng 7:364–376CrossRefGoogle Scholar
  35. 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 27:1124–1134CrossRefGoogle Scholar
  36. 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 130–138Google Scholar
  37. 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 3–9Google Scholar
  38. 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 166–169Google Scholar
  39. 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 241–248Google Scholar
  40. 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 1–6Google Scholar
  41. 41.
    Singhal U, Jain S (2014) A new fuzzy logic and GSO based load balancing mechanism for public cloud. Int J Grid Distrib Comput 7:97–110CrossRefGoogle Scholar
  42. 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 Google Scholar
  43. 43.
    Alejandra M, Sossa R (2011) Cost minimization heuristics for scheduling workflows on heterogeneous distributed environments. The University of MelbourneGoogle Scholar
  44. 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 CrossRefGoogle Scholar
  45. 45.
    Lin J, Zhong Y, Lin X, et al (2014) Hybrid ant colony algorithm clonal selection in the application of the cloud ’s resource schedulingGoogle Scholar
  46. 46.
    Sakellariou R, Zhao H (2004) A low-cost rescheduling policy for efficient mapping of workflows on grid systems. Sci Program 12:253–262Google Scholar
  47. 47.
    Liu K (2009) Scheduling algorithms for instance-intensive cloud workflows. Swinburne University of TechnologyGoogle Scholar
  48. 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 MathSciNetzbMATHGoogle Scholar
  49. 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 763–766Google Scholar
  50. 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 CrossRefGoogle Scholar
  51. 51.
    Tsai C, Rodrigues JJPC (2014) Metaheuristic scheduling for cloud: a survey. IEEE Syst J 8:279–291CrossRefGoogle Scholar
  52. 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 CrossRefGoogle Scholar
  53. 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 1331–1345Google Scholar
  54. 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 CrossRefGoogle Scholar
  55. 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. 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 CrossRefGoogle Scholar
  57. 57.
    Holland JH (1975) Adaptation in natural and artificial systemsGoogle Scholar
  58. 58.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing CoGoogle Scholar
  59. 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 299–305Google Scholar
  60. 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 CrossRefGoogle Scholar
  61. 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 181–186Google Scholar
  62. 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 444–447Google Scholar
  63. 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 146–152Google Scholar
  64. 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 5:157–162Google Scholar
  65. 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 10:134–139Google Scholar
  66. 66.
    Kaur K, Chharbra A, Gurvinder Singh (2010) Heuristics based genetic algorithm for scheduling static tasks in homogeneous parallel system. J Comput Sci Secur 4:183–198Google Scholar
  67. 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 96–101Google Scholar
  68. 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 Google Scholar
  69. 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 Google Scholar
  70. 70.
    Shen G, Zhang Y (2011) A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers. Adv Swarm Intell 6728:522–529CrossRefGoogle Scholar
  71. 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 17–24Google Scholar
  72. 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 182–187Google Scholar
  73. 73.
    Sawant S (2011) A genetic algorithm scheduling approach for virtual machine resources in a cloud computing environment. San Jose State UniversityGoogle Scholar
  74. 74.
    Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurr Comput Program, C3P Rep 826:1989Google Scholar
  75. 75.
    Merz P, Freisleben B (1997) A genetic local search approach to the quadratic assignment problem. In: 7th international conference on genetic algorithms, p 1Google Scholar
  76. 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 zbMATHCrossRefGoogle Scholar
  77. 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 177–186Google Scholar
  78. 78.
    Kashani MH, Jahanshahi M. A new method based on memetic algorithm for task scheduling in distributed systems. Int J Simul Syst Sci Technol. 10Google Scholar
  79. 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 2:174–190Google Scholar
  80. 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 1–9Google Scholar
  81. 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 88:292–296Google Scholar
  82. 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 4661–4667Google Scholar
  83. 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 CrossRefGoogle Scholar
  84. 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 28:27–32Google Scholar
  85. 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 Google Scholar
  86. 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 1–7Google Scholar
  87. 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 55–60Google Scholar
  88. 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 Google Scholar
  89. 89.
    Ahmadi M (2015) Cloud data centers using the imperialist competitive algorithm. In: IEEE fifth international conference on big data cloud computing, IEEE, pp 62–67Google Scholar
  90. 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 69–73Google Scholar
  91. 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 37–43Google Scholar
  92. 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 CrossRefGoogle Scholar
  93. 93.
    Fatemipour F, Fatemipour F (2012) Scheduling scientific workflows using imperialist competitive algorithm. In: International conference on industrial intelligent information (ICIII 2012), pp 218–225Google Scholar
  94. 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 1469–1479Google Scholar
  95. 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 CrossRefGoogle Scholar
  96. 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. 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 CrossRefGoogle Scholar
  98. 98.
    Aryan Y, Delavar AG (2014) A bi-objective workflow application scheduling in cloud computing systems. Int J Integr Technol Educ 3:51–62CrossRefGoogle Scholar
  99. 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 CrossRefGoogle Scholar
  100. 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 216–222Google Scholar
  101. 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 83–90Google Scholar
  102. 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 43–52Google Scholar
  103. 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 Google Scholar
  104. 104.
    Abarghoei A, Mahdipour E, Askarzadeh M (2015) Cloud computing resource planning based on imperialist competitive algorithm. Cumhur Sci J 36:1312–1324Google Scholar
  105. 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 835–842Google Scholar
  106. 106.
    Fayazi M (2016) Resource allocation in cloud computing using imperialist competitive algorithm with reliability approach. Int J Adv Comput Sci Appl 7:323–331Google Scholar
  107. 107.
    Yang X (2014) Nature-inspired optimization algorithms. nature-inspired optim algorithms. doi: 10.1016/B978-0-12-416743-8.00017-8
  108. 108.
    Madureira A, Ipp I (2005) Swarm intelligence for scheduling: a review. In: International conference on business sustain, pp 1–8Google Scholar
  109. 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 MathSciNetzbMATHCrossRefGoogle Scholar
  110. 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 153:373–380CrossRefGoogle Scholar
  111. 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 3308–3315Google Scholar
  112. 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 Google Scholar
  113. 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 CrossRefGoogle Scholar
  114. 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 41–48Google Scholar
  115. 115.
    Chimakurthi L, Madhu Kumar S (2011) Power efficient resource allocation for clouds using ant colony framework. Comput Res Repos abs/1102.2Google Scholar
  116. 116.
    Mathiyalagan P, Suriya S, Sivanandam SN (2010) Modified ant colony algorithm for grid scheduling. Int J Comput Sci Eng 2:132–139.Google Scholar
  117. 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 415–418Google Scholar
  118. 118.
    Bagherzadeh J, MadadyarAdeh M (2009) An improved ant algorithm for grid scheduling problem. In: 14th International CSI computing conference, pp 323–328Google Scholar
  119. 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 CrossRefGoogle Scholar
  120. 120.
    Tawfeek MA, El-sisi A (2013) Cloud task scheduling based on ant colony optimization. In: 8th International conference on computing engineering systems, pp 64–69Google Scholar
  121. 121.
    Gogulan R, Kavitha MA, Kumar UK (2012) An multiple pheromone algorithm for cloud scheduling with various QOS requirements. Int J Comput Sci Issues 9:232–238Google Scholar
  122. 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 2:424–429Google Scholar
  123. 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 5:1417–1420Google Scholar
  124. 124.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948Google Scholar
  125. 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 400–407Google Scholar
  126. 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 184–188Google Scholar
  127. 127.
    Xue S, Wu W (2012) Scheduling workflow in cloud computing based on hybrid particle swarm algorithm. Telkomnika Indones J Electr Eng 10:1560–1566Google Scholar
  128. 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 zbMATHCrossRefGoogle Scholar
  129. 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. 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 6:29–38CrossRefGoogle Scholar
  131. 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 MathSciNetzbMATHCrossRefGoogle Scholar
  132. 132.
    Gomathi B, Krishnasamy K (2013) Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment. J Theor Appl Inf Technol 55:33–38Google Scholar
  133. 133.
    Sridhar M (2015) Hybrid particle swarm optimization scheduling for cloud computing. In: IEEE international advance computing conference IEEE, pp 1196–1200Google Scholar
  134. 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 Google Scholar
  135. 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 Google Scholar
  136. 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 CrossRefGoogle Scholar
  137. 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 CrossRefGoogle Scholar
  138. 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 180–187Google Scholar
  139. 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 CrossRefGoogle Scholar
  140. 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 Google Scholar
  141. 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 Google Scholar
  142. 142.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech Rep TR06, Erciyes UnivGoogle Scholar
  143. 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 CrossRefGoogle Scholar
  144. 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 CrossRefGoogle Scholar
  145. 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 CrossRefGoogle Scholar
  146. 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 50–53Google Scholar
  147. 147.
    Hashemi SM, Hanani A (2013) Solving the scheduling problem in computational grid using artificial bee colony algorithm. Adv Comput Sci Int J 2:37–41Google Scholar
  148. 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 200–210Google Scholar
  149. 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 153–158Google Scholar
  150. 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 CrossRefGoogle Scholar
  151. 151.
    Soni A (2015) A bee colony based multi-objective load balancing technique for cloud computing environment. Int J Comput Appl 114:19–25Google Scholar
  152. 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 49–57Google Scholar
  153. 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 13:54–59Google Scholar
  154. 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 CrossRefGoogle Scholar
  155. 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 298–303Google Scholar
  156. 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 38–44Google Scholar
  157. 157.
    Hesabian N, Haj H, Javadi S (2015) Optimal scheduling in cloud computing environment using the bee algorithm. Int J Comput Netw Commun Secur 3:253–258Google Scholar
  158. 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 1140–1147Google Scholar
  159. 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 470–475Google Scholar
  160. 160.
    Kumar RS (2014) Improving task scheduling in large scale cloud computing environment using artificial bee colony algorithm. Int J Comput Appl 103:29–32Google Scholar
  161. 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 391–398Google Scholar
  162. 162.
    Liang Y, Chen AH, Nien Y (2014) Artificial bee colony for workflow scheduling. In: IEEE congress evolutionary computation IEEE, pp 558–564Google Scholar
  163. 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 CrossRefGoogle Scholar
  164. 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 zbMATHGoogle Scholar
  165. 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 CrossRefGoogle Scholar
  166. 166.
    Jacob L (2014) Bat algorithm for resource scheduling in cloud computing. Int J Res Appl Sci Eng Technol 2:53–57Google Scholar
  167. 167.
    Kumar V, Aramudhan M (2014) Trust based resource selection in cloud computing using hybrid algorithm. Int J Comput Intell Informatics 4:169–176Google Scholar
  168. 168.
    Suresh Kumar VS (2014) Hybrid optimized list scheduling and trust based resource selection in cloud computing. J Theor Appl Inf Technol 69:434–442Google Scholar
  169. 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 139–144Google Scholar
  170. 170.
    George S (2015) Hybrid PSO-MOBA for profit maximization in cloud computing. Int J Adv Comput Sci Appl 6:159–163Google Scholar
  171. 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 Google Scholar
  172. 172.
    Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J Innov Comput Inf Control 3:163–173Google Scholar
  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 3328–3333Google Scholar
  174. 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 CrossRefGoogle Scholar
  175. 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 861–866Google Scholar
  176. 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 1–6Google Scholar
  177. 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. 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 Google Scholar
  179. 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 36:1672–1685Google Scholar
  180. 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 328–329Google Scholar
  181. 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 296–300Google Scholar
  182. 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 4:966–973Google Scholar
  183. 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 240–243Google Scholar
  184. 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 Google Scholar
  185. 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 57–61Google Scholar
  186. 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 CrossRefGoogle Scholar
  187. 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 CrossRefGoogle Scholar
  188. 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 Google Scholar
  189. 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 Google Scholar
  190. 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 37–41Google Scholar
  191. 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 773–778Google Scholar
  192. 192.
    Pacini E, Mateos C, Garc C (2014) Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electron J 14:1–14Google Scholar
  193. 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 Google Scholar
  194. 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 Google Scholar
  195. 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 6–8Google Scholar
  196. 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 1225–1234Google Scholar
  197. 197.
    Pragaladan R, Maheswari R (2014) Improve workflow scheduling technique for novel particle swarm optimization in cloud environment. Int J Eng Res Gen Sci 2:675–680Google Scholar
  198. 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 1–5Google Scholar
  199. 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 CrossRefGoogle Scholar
  200. 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 5:466–471Google Scholar
  201. 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 1–8Google Scholar
  202. 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 CrossRefGoogle Scholar
  203. 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 244–251Google Scholar
  204. 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 Google Scholar
  205. 205.
    Singh G, Vahi K, Ramakrishnan A et al (2007) Optimizing workflow data footprint. Sci Program 15:249–268Google Scholar
  206. 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 401–409Google Scholar
  207. 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 1–12Google Scholar
  208. 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 CrossRefGoogle Scholar
  209. 209.
    Lin X, Wu CQ (2013) On scientific workflow scheduling in clouds under budget constraint. In: 42nd international conference on parallel processing, IEEE, pp 90–99Google Scholar
  210. 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 Google Scholar
  211. 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 219–222Google Scholar
  212. 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 4:651–655CrossRefGoogle Scholar
  213. 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 CrossRefGoogle Scholar
  214. 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 1–7Google Scholar
  215. 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 CrossRefGoogle Scholar
  216. 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 7:3096–3117CrossRefGoogle Scholar
  217. 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 Google Scholar
  218. 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 CrossRefGoogle Scholar
  219. 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 CrossRefGoogle Scholar
  220. 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. 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 CrossRefGoogle Scholar
  222. 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 CrossRefGoogle Scholar
  223. 223.
    Yu Z, Wang C, Shi W (2010) FLAW: failure-aware workflow scheduling in high performance computing systems. J Clust Comput 13:421–434CrossRefGoogle Scholar
  224. 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. 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 CrossRefGoogle Scholar
  226. 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. 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. 228.
    Delavar AG, Aryan Y (2012) A goal-oriented workflow scheduling in heterogeneous distributed systems. Int J Comput Appl 52:27–33Google Scholar
  229. 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 1–4Google Scholar
  230. 230.
    Singh R, Singh S (2013) Score based deadline constrained workflow scheduling algorithm for cloud systems. Int J Cloud Comput Serv Archit 3:31–41Google Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentNITTTRChandigarhIndia
  2. 2.Computer Science and Engineering Department, UIETPanjab UniversityChandigarhIndia

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