Cluster Computing

, Volume 21, Issue 2, pp 1349–1363 | Cite as

Agent-based fuzzy constraint-directed negotiation for service level agreements in cloud computing

  • Lin Li
  • Chee Shin Yeo
  • Chia-Yu Hsu
  • Liang-Chih Yu
  • K. Robert LaiEmail author


Establishing a service level agreement (SLA) between a cloud provider and a cloud consumer is becoming increasingly critical: consumers expect a specified quality of service (QoS) for their cloud applications, and providers must be able to guarantee that the agreed-upon QoS will be maintained. These concepts require the SLA negotiation to be performed in a manner whereby a provider and a consumer can effectively bargain on various QoS preferences, such as price, response time and service level. This paper presents a novel agent-based fuzzy constraint-directed negotiation (AFCN) model for SLA negotiation. It provides a framework for integrating time, resource (market) and behavioral factors into the decision making process for service level agreements and cloud load balancing. The proposed AFCN model supports an iterative many-to-many bargaining negotiation infrastructure that is a fully distributed and autonomous approach and that does not require a broker to coordinate the negotiation process. The novelty of the proposed model is that it uses the concept of a fuzzy membership function to represent imprecise QoS preferences. This added information sharing is critical for the effectiveness of distributed coordination. It can not only speed up the convergence but also enforce global consistency through iterative exchanges of offers and counter-offers with limited information sharing and without privacy breaches. To consider the behavior of different agents, the AFCN model can also flexibly adopt different negotiation strategies such as the competitive, win-win, and collaborative strategies in different cloud computing environments. The experimental results demonstrate that the proposed model consistently outperforms other agent-based SLA negotiation models in terms of the degree of satisfaction, the ratio of successful negotiation, the buying price of the consumer agent (CA), the revenue of the provider agent (PA), and the convergence speed. Consequently, the proposed AFCN is both flexible and useful for fully distributed SLA negotiations.


Agent-based negotiation Service level agreement Fuzzy constraint Load balancing 



This study was partially supported by the Taiwan Ministry of Science and Technology Grants MOST 104-2221-E-155-013, MOST 104-3115-E-155-002, MOST 105-2118-E-155-010,and MOST 106-2118-E-155-007, and by Grant 2016Y0079 from the Natural Science Foundation of Fujian Province, China.


  1. 1.
    Petcu, D., Macariu, G., Panica, S., Craciun, C.: Portable cloud applications—from theory to practice. Future Gener. Comput. Syst. 29(6), 1417–1430 (2013)CrossRefGoogle Scholar
  2. 2.
    Toosi, A.N., Calheiros, R.N., Buyya, R.: Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput. Surv. 47(1), 7:1–7:47 (2014)CrossRefGoogle Scholar
  3. 3.
    Kritikos, K., Pernici, B., Plebani, P., Cappiello, C., Comuzzi, M., Benbernou, S., Brandic, I., Kertész, A., Parkin, M., Carro, M.: A survey on service quality description. ACM Comput. Surv. 46(1), 1:1–1:58 (2013)CrossRefGoogle Scholar
  4. 4.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  5. 5.
    Maurer, M., Emeakaroha, V.C., Brandic, I., Altmann, J.: Cost-benefit analysis of an SLA mapping approach for defining standardized cloud computing goods. Future Gener. Comput. Syst. 28(1), 39–47 (2012)CrossRefGoogle Scholar
  6. 6.
    Hammadi, A., Hussain, O.K., Dillon, T., Hussain, F.K.: A framework for SLA management in cloud computing for informed decision making. Clust. Comput. 16(4), 961–977 (2013)CrossRefGoogle Scholar
  7. 7.
    Garg, S.K., Vecchiola, C., Buyya, R.: Mandi: a market exchange for trading utility and cloud computing services. J. Supercomput. 64(3), 1153–1174 (2013)CrossRefGoogle Scholar
  8. 8.
    Wu, L., Garg, S.K., Buyya, R., Chen, C., Versteeg, S.: Automated SLA negotiation framework for cloud computing. In: CCGrid 2013, 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, Delft, Netherlands, May 13–16, pp. 235–244. IEEE Computer Society (2013)Google Scholar
  9. 9.
    Hu, J., Gu, J., Sun, G., Zhao, T.: A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In: 3rd International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2010, Dalian, China, December 18–20, pp. 89–96. IEEE (2010)Google Scholar
  10. 10.
    Gutierrez-Garcia, J.O., Ramirez-Nafarrate, A.: Agent-based load balancing in cloud data centers. Clust. Comput. 18(3), 1041–1062 (2015)CrossRefGoogle Scholar
  11. 11.
    Ferrer, A.J., Hernández, F., Tordsson, J., Elmroth, E., Ali-Eldin, A., Zsigri, C., Sirvent, R., Guitart, J., Badia, R.M., Djemame, K., Ziegler, W., Dimitrakos, T., Nair, S.K., Kousiouris, G., Konstanteli, K., Varvarigou, T.A., Hudzia, B., Kipp, A., Wesner, S., Corrales, M., Forgó, N., Sharif, T., Sheridan, C.: OPTIMIS: a holistic approach to cloud service provisioning. Future Gener. Comput. Syst. 28(1), 66–77 (2012)CrossRefGoogle Scholar
  12. 12.
    Hung, P.C.K., Li, H., Jeng, J.-J.: WS-negotiation: an overview of research issues. In: HICSS 2004, 37th Hawaii International Conference on System Sciences, Big Island, HI, USA, January 5–8, 2004. IEEE Computer Society (2004)Google Scholar
  13. 13.
    Zulkernine, F.H., Martin, P.: An adaptive and intelligent SLA negotiation system for web services. IEEE Trans. Serv. Comput. 4(1), 31–43 (2011)CrossRefGoogle Scholar
  14. 14.
    Sim, K.M.: Agent-based cloud computing. IEEE Trans. Serv. Comput. 5(4), 564–577 (2012)CrossRefGoogle Scholar
  15. 15.
    Venticinque, S., Aversa, R., Di Martino, B., Rak, M., Petcu, D.: A cloud agency for SLA negotiation and management. In: Euro-Par 2010, Parallel Processing Workshops, pp. 587–594. Springer (2010)Google Scholar
  16. 16.
    Zheng, X., Martin, P., Brohman, K.: Cloud service negotiation: concession vs. tradeoff approaches. In: CCGrid 2012, 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Ottawa, Canada, May 13–16, 2012. pp. 515–522. IEEE (2012)Google Scholar
  17. 17.
    Sim, K.M.: Agent-based interactions and economic encounters in an intelligent InterCloud. IEEE Trans. Cloud Comput. 3(3), 358–371 (2015)CrossRefGoogle Scholar
  18. 18.
    Gutierrez-Garcia, J.O., Sim, K.M.: Agent-based cloud bag-of-tasks execution. J. Syst. Softw. 104, 17–31 (2015)CrossRefGoogle Scholar
  19. 19.
    Baranwal, G., Vidyarthi, D.P.: A fair multi-attribute combinatorial double auction model for resource allocation in cloud computing. J. Syst. Softw. 108, 60–76 (2015)CrossRefGoogle Scholar
  20. 20.
    Samimi, P., Teimouri, Y., Mukhtar, M.: A combinatorial double auction resource allocation model in cloud computing. Inf. Sci. doi: 10.1016/j.ins.2014.02.008 (2014)
  21. 21.
    Zhang, H., Jiang, H., Li, B., Liu, F., Vasilakos, A.V., Liu, J.: A framework for truthful online auctions in cloud computing with heterogeneous user demands. IEEE Trans. Comput. 65(3), 805–818 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Jung, J.-J., Jo, G.-S.: Brokerage between buyer and seller agents using constraint satisfaction problem models. Decis. Support Syst. 28(4), 293–304 (2000)CrossRefGoogle Scholar
  23. 23.
    Hsu, C.-Y., Kao, B.-R., Ho, V.L., Lai, K.R.: Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling. Eng. Appl. Artif. Intell. 53, 140–154 (2016)CrossRefGoogle Scholar
  24. 24.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998)CrossRefGoogle Scholar
  25. 25.
    Dastjerdi, A.V., Buyya, R.: An autonomous time-dependent SLA negotiation strategy for cloud computing. Comput. J. 58(11), 3202–3216 (2015)CrossRefGoogle Scholar
  26. 26.
    Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make issue trade-offs in automated negotiations. Artif. Intell. 142(2), 205–237 (2002)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Luo, X., Jennings, N.R., Shadbolt, N., Leung, H.-F., Lee, J.H.-M.: A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments. Artif. Intell. 148(1–2), 53–102 (2003)CrossRefzbMATHGoogle Scholar
  28. 28.
    Hani, A.F.M., Paputungan, I.V., Hassan, M.F.: Renegotiation in service level agreement management for a cloud-based system. ACM Comput. Surv. 47(3), 51 (2015)CrossRefGoogle Scholar
  29. 29.
    Halboob, W., Abbas, H., Khan, M.K., Khan, F.A., Pasha, M.: A framework to address inconstant user requirements in cloud SLAs management. Clust. Comput. 18(1), 123–133 (2015)CrossRefGoogle Scholar
  30. 30.
    Chun, S.-H., Choi, B.-S.: Service models and pricing schemes for cloud computing. Clust. Comput. 17(2), 529–535 (2014)CrossRefGoogle Scholar
  31. 31.
    Macías, M., Guitart, J.: SLA negotiation and enforcement policies for revenue maximization and client classification in cloud providers. Future Gener. Comput. Syst. 41, 19–31 (2014)CrossRefGoogle Scholar
  32. 32.
    Lai, K.R.: Fuzzy Constraint Processing. North Carolina State University at Raleigh, Raleigh (1992)Google Scholar
  33. 33.
    Liu, M., Wang, M., Shen, W., Luo, N., Yan, J.: A quality of service (QoS)-aware execution plan selection approach for a service composition process. Future Gener. Comput. Syst. 28(7), 1080–1089 (2012)CrossRefGoogle Scholar
  34. 34.
    Dattorro, J.: Convex Optimization & Euclidean Distance Geometry. Meboo, Palo Alto (2010)zbMATHGoogle Scholar
  35. 35.
    Lai, K.R., Lin, M.-W.: Modeling agent negotiation via fuzzy constraints in e-business. Comput. Intell. 20(4), 624–642 (2004)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Lai, K.R., Lin, M.-W., Yu, T.-J.: Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation. Appl. Intell. 33(2), 232–246 (2010)CrossRefGoogle Scholar
  37. 37.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Pract. Exp. 41(1), 23–50 (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Lin Li
    • 1
    • 4
  • Chee Shin Yeo
    • 5
  • Chia-Yu Hsu
    • 2
    • 3
  • Liang-Chih Yu
    • 2
    • 3
  • K. Robert Lai
    • 1
    • 3
    Email author
  1. 1.Department of Computer Science and EngineeringYuan Ze UniversityChungliTaiwan
  2. 2.Department of Information ManagementYuan Ze UniversityChungliTaiwan
  3. 3.Innovation Center for Big Data and Digital ConvergenceYuan Ze UniversityChungliTaiwan
  4. 4.School of Computer and Information EngineeringXiamen University of TechnologyXiamenChina
  5. 5.School of Science and TechnologySingapore University of Social SciencesSingaporeSingapore

Personalised recommendations