A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm

  • Afshin Naseri
  • Nima Jafari NavimipourEmail author
Original Research


Cloud computing as a new computing paradigm has a great capacity for storing and accessing the remote data and services. Presently, many organizations decide to reduce the burden of local resources and support them by outsourcing the resources to the cloud. Typically, scalable resources are provided as services over the Internet. The way of choosing appropriate services in the cloud computing is done by determining the different Quality of Service (QoS) parameters to perform optimized resource allocation. Therefore, service composition as a developing approach combines the existing services to increase the number of cloud applications. Independent services can be integrated into complex composited services through service composition. In this paper, a new hybrid method is proposed for efficient service composition in the cloud computing. The agent-based method is also used to compose services by identifying the QoS parameters and the particle swarm optimization (PSO) algorithm is employed for selecting the best services based on fitness function. The simulation results have shown the performance of the method in terms of reducing the combined resources and waiting time.


Cloud computing Service composition Cloud mobile agent Particle swarm optimization 


  1. Almorsy M et al (2014) Adaptable, model-driven security engineering for SaaS cloud-based applications. Autom Softw Eng 21(2):187–224CrossRefGoogle Scholar
  2. AlRashidi M, El-Hawary M (2007) Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects. Power Systems. IEEE Transac 22(4):2030–2038Google Scholar
  3. Arvanitis S et al (2017) Why do firms adopt cloud computing? A comparative analysis based on South and North Europe firm data. Telemat Inform 34(7):1322–1332CrossRefGoogle Scholar
  4. Ashouraie M, Jafari Navimipour N (2015) Priority-based task scheduling on heterogeneous resources in the Expert Cloud. Kybernetes 44(10):1455–1471CrossRefGoogle Scholar
  5. Azad P, Navimipour JN (2017). An energy-aware task scheduling in cloud computing using a hybrid cultural and ant colony optimization algorithm. Int J Cloud Appl Comput 7(4)Google Scholar
  6. Aznoli F, Navimipour NJ (2017) Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. J Netw Comput Appl 77:73–86CrossRefGoogle Scholar
  7. Behzadi S, Alesheikh AA (2013) Introducing a novel model of belief–desire–intention agent for urban land use planning. Eng Appl Artif Intell 26(9):2028–2044CrossRefGoogle Scholar
  8. Benmerzoug D et al. (2013). Agent interaction protocols in support of cloud services composition. In: International Conference on Industrial Applications of Holonic and Multi-Agent Systems, SpringerGoogle Scholar
  9. Buyya R, Ranjan R (2010) Special section: Federated resource management in grid and cloud computing systems. Future Gener Comput Syst 26(8):1189–1191CrossRefGoogle Scholar
  10. Canfora G et al. (2005). An approach for QoS-aware service composition based on genetic algorithms. Proceedings of the 7th annual conference on Genetic and evolutionary computation, ACMGoogle Scholar
  11. Cao B et al. (2016). Querying similar process models based on the Hungarian Algorithm. IEEE Transactions on Services ComputingGoogle Scholar
  12. Chiregi M, Navimipour NJ (2016) A new method for trust and reputation evaluation in the cloud environments using the recommendations of opinion leaders’ entities and removing the effect of troll entities. Comput Hum Behav 60:280–292CrossRefGoogle Scholar
  13. Del Valle Y et al (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. Evolutionary Computation. IEEE Transac 12(2):171–195Google Scholar
  14. Dinesha H, Agrawal VK (2012). Multi-level authentication technique for accessing cloud services. Computing, Communication and Applications (ICCCA), 2012 International Conference on, IEEEGoogle Scholar
  15. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, MHS'95. IEEEGoogle Scholar
  16. Elbeltagi E et al (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inf 19(1):43–53CrossRefGoogle Scholar
  17. Ferber J (1999). Multi-agent systems: an introduction to distributed artificial intelligence, Addison-Wesley ReadingGoogle Scholar
  18. Fethallah H et al. (2012). QoS-aware service selection based on swarm particle optimization. Information Technology and e-Services (ICITeS), 2012 International Conference on, IEEEGoogle Scholar
  19. Guha T, Ludwig SA (2008). Comparison of service selection algorithms for grid services: Multiple objective particle swarm optimization and constraint satisfaction based service selection. Tools with Artificial Intelligence, 2008. ICTAI’08. 20th IEEE International Conference on, IEEEGoogle Scholar
  20. Gupta B et al. (2016). Handbook of research on modern cryptographic solutions for computer and cyber security, IGI GlobalGoogle Scholar
  21. Gutierrez-Garcia JO, Sim KM (2013) Agent-based Cloud service composition. Appl Intell 38(3):436–464CrossRefGoogle Scholar
  22. Iosup A et al. (2014). Iaas cloud benchmarking: approaches, challenges, and experience. In: Cloud Computing for Data-Intensive Applied, Springer, Berlin 83–104Google Scholar
  23. Ivanović D, Carro M (2014). Transforming Service Compositions into Cloud-Friendly Actor Networks. In: International Conference on Service-Oriented Computing, Springer, BerlinGoogle Scholar
  24. Jafari Navimipour N et al (2015) Expert Cloud: A Cloud-based framework to share the knowledge and skills of human resources. Comput Hum Behav 46(C):57–74CrossRefGoogle Scholar
  25. Jeong H-Y et al (2016) A service composition model based on user experience in Ubi-cloud comp. Telecommunication Syst 61(4):897–907CrossRefGoogle Scholar
  26. Jiuxin C et al. (2010). Efficient multi-objective services selection algorithm based on particle swarm optimization. Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific, IEEEGoogle Scholar
  27. Jula A et al (2014) Cloud computing service composition: A systematic literature review. Expert Syst Appl 41(8):3809–3824CrossRefGoogle Scholar
  28. Kang J, Sim KM (2012) A multiagent brokering protocol for supporting Grid resource discovery. Appl Intell 37(4):527–542CrossRefGoogle Scholar
  29. Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 760–766Google Scholar
  30. Kiraz MS (2016) A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing. J Ambient Intell Humaniz Comput 7(5):731–760CrossRefGoogle Scholar
  31. Kofler K et al. (2009). A parallel branch and bound algorithm for workflow QoS optimization. Parallel Processing, 2009. ICPP’09. International Conference on, IEEEGoogle Scholar
  32. Kurdi H et al (2015) A combinatorial optimization algorithm for multiple cloud service composition. Comput Electr Eng 42:107–113CrossRefGoogle Scholar
  33. Lai KR et al (2010) Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation. Appl Intell 33(2):232–246CrossRefGoogle Scholar
  34. Li J et al (2015) A hybrid cloud approach for secure authorized deduplication. IEEE Trans Parallel Distrib Syst 26(5):1206–1216CrossRefGoogle Scholar
  35. Liao J et al (2012) Service composition based on niching particle swarm optimization in service overlay networks. KSII Transac Internet Inf Syst 6(4):1106–1127Google Scholar
  36. Lin M et al (2013) Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans Netw 21(5):1378–1391CrossRefGoogle Scholar
  37. Ludwig SA, Schoene T (2011). Web service selection using particle swarm optimization and genetic algorithms. Nature Biol Inspired Computing (NaBIC), 2011 Third World Congress on, IEEEGoogle Scholar
  38. Mell P, Grance T (2009) Draft NIST working definition of cloud computing. Referenced June 3rd 15:32Google Scholar
  39. Mezgár I, Rauschecker U (2014) The challenge of networked enterprises for cloud computing interoperability. Comput Ind 65(4):657–674CrossRefGoogle Scholar
  40. Milani BA, Navimipour NJ (2016) A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions. J Netw Comput Appl 64:229–238CrossRefGoogle Scholar
  41. Murillo J et al (2011) Schedule coordination through egalitarian recurrent multi-unit combinatorial auctions. Appl Intell 34(1):47–63CrossRefGoogle Scholar
  42. Nathani A et al (2012) Policy based resource allocation in IaaS cloud. Future Gener Comput Syst 28(1):94–103CrossRefGoogle Scholar
  43. Navimipour NJ, Milani FS (2015) Task scheduling in the cloud computing based on the cuckoo search algorithm. Int J Model Opt 5(1):44Google Scholar
  44. Navimipour NJ et al (2015) Expert Cloud: A Cloud-based framework to share the knowledge and skills of human resources. Comput Hum Behav 46:57–74CrossRefGoogle Scholar
  45. Navimipour NJ et al. (2017). Resources discovery in the cloud environments using collaborative filtering and ontology relations. Electron Commer Res Appl 26(Supplement C): 89–100CrossRefGoogle Scholar
  46. Öztürk P et al (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143CrossRefGoogle Scholar
  47. Pooranian Z et al (2015) An efficient meta-heuristic algorithm for grid computing. J Combinatorial Opt 30(3):413–434MathSciNetCrossRefzbMATHGoogle Scholar
  48. Proaño J et al (2017) Empirical modeling and simulation of an heterogeneous Cloud computing environment. Parallel Comput. Google Scholar
  49. Rao J, Su X (2004). A survey of automated web service composition methods. In: Semantic Web Services Web Process Composition Springer, Berlin: 43–54Google Scholar
  50. Sellami M et al. (2013). PaaS-independent Provisioning and Management of Applications in the Cloud. In: 2013 IEEE Sixth International Conference on Cloud Computing, IEEEGoogle Scholar
  51. Sheikholeslami F, Navimipour JN (2017). Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance. Swarm and Evolutionary ComputationGoogle Scholar
  52. Shi Y, Eberhart R (1998). A modified particle swarm optimizer. Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, IEEEGoogle Scholar
  53. Singh A et al. (2015). A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing. Journal of King Saud University-Computer and Information SciencesGoogle Scholar
  54. Stergiou C et al (2018) Secure integration of IoT and cloud computing. Future Gener Comput Syst 78:964–975CrossRefGoogle Scholar
  55. Tao F et al. (2008). Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. Ind Inf IEEE Transac 4(4): 315–327CrossRefGoogle Scholar
  56. Tout H et al (2015) AOMD approach for context-adaptable and conflict-free web services composition. Comput Electr Eng 44:200–217CrossRefGoogle Scholar
  57. Verhaegen M et al. (2007). Filtering and system identification: an introduction to using Matlab software. Delft Univ Technol 68Google Scholar
  58. Wakunuma K, Masika R (2017) Cloud computing, capabilities and intercultural ethics: Implications for Africa. Telecommun Policy 41(7):695–707CrossRefGoogle Scholar
  59. Wang W et al. (2013). Revenue maximization with dynamic auctions in IaaS cloud markets. Quality of Service (IWQoS), 2013 IEEE/ACM 21st International Symposium on, IEEEGoogle Scholar
  60. Wang D et al (2015) A genetic-based approach to web service composition in geo-distributed cloud environment. Comput Electr Eng 43:129–141CrossRefGoogle Scholar
  61. Wang H et al (2016) A multi-agent reinforcement learning approach to dynamic service composition. Inf Sci 363:96–119CrossRefGoogle Scholar
  62. Wooldridge M (2009). An introduction to multiagent systems, Wiley, New JerseyGoogle Scholar
  63. Xia H et al. (2009). Web service selection algorithm based on particle swarm optimization. Dependable, Autonomic and Secure Computing, 2009. DASC’09. Eighth IEEE International Conference on, IEEEGoogle Scholar
  64. Xie R et al. (2014). Diagnosing vulnerability patterns in cloud audit logs. In: High performance cloud auditing applications, Springer, Berlin: 119–146Google Scholar
  65. Ye Z et al. (2011). Genetic algorithm based QoS-aware service compositions in cloud computing. In: International Conference on Database Systems for Advanced Applications, SpringerGoogle Scholar
  66. Yu Q et al (2015) Ant colony optimization applied to web service compositions in cloud computing. Comput Electr Eng 41:18–27CrossRefGoogle Scholar
  67. Zeginis D et al (2013) A user-centric multi-PaaS application management solution for hybrid multi-Cloud scenarios. Scalable Comput 14(1):17–32Google Scholar
  68. Zeng Z, Veeravalli B (2014) Optimal metadata replications and request balancing strategy on cloud data centers. J Parallel Distrib Comput 74(10):2934–2940CrossRefGoogle Scholar
  69. Zhao C-Y et al (2014) A hybrid algorithm combining ant colony algorithm and genetic algorithm for dynamic web service composition. Open Cybern Syst J 8:146–154CrossRefGoogle Scholar
  70. Zou G et al. (2010). AI planning and combinatorial optimization for web service composition in cloud computing. In: Proc international conference on cloud computing and virtualizationGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringTabriz Branch, Islamic Azad UniversityTabrizIran

Personalised recommendations