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

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.

[1]  K. Robert Lai,et al.  Modeling Agent Negotiation Via Fuzzy Constraints in E‐Business , 2004, Comput. Intell..

[2]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[3]  Rajkumar Buyya,et al.  Interconnected Cloud Computing Environments , 2014, ACM Comput. Surv..

[4]  Dana Petcu,et al.  Portable Cloud applications - From theory to practice , 2013, Future Gener. Comput. Syst..

[5]  Nicholas R. Jennings,et al.  Using similarity criteria to make issue trade-offs in automated negotiations , 2002, Artif. Intell..

[6]  Mario Macías,et al.  SLA negotiation and enforcement policies for revenue maximization and client classification in cloud providers , 2014, Future Gener. Comput. Syst..

[7]  Patrick Martin,et al.  An Adaptive and Intelligent SLA Negotiation System for Web Services , 2011, IEEE Transactions on Services Computing.

[8]  Jianhua Gu,et al.  A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment , 2010, 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming.

[9]  Muriati Mukhtar,et al.  A combinatorial double auction resource allocation model in cloud computing , 2016, Inf. Sci..

[10]  Deo Prakash Vidyarthi,et al.  A fair multi-attribute combinatorial double auction model for resource allocation in cloud computing , 2015, J. Syst. Softw..

[11]  H T Stalker,et al.  Plant breeding in the 1990s: proceedings of the Symposium, North Carolina State University, Raleigh, North Carolina, USA, Mar 1991 , 1992 .

[12]  Patrick Martin,et al.  Cloud Service Negotiation: Concession vs. Tradeoff Approaches , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[13]  Adrian Ramirez-Nafarrate,et al.  Agent-based load balancing in Cloud data centers , 2015 .

[14]  Kwang Mong Sim,et al.  Agent-based Cloud bag-of-tasks execution , 2015, J. Syst. Softw..

[15]  Rajkumar Buyya,et al.  Mandi: a market exchange for trading utility and cloud computing services , 2011, The Journal of Supercomputing.

[16]  Jörn Altmann,et al.  Cost-benefit analysis of an SLA mapping approach for defining standardized Cloud computing goods , 2012, Future Gener. Comput. Syst..

[17]  Tharam S. Dillon,et al.  A framework for SLA management in cloud computing for informed decision making , 2012, Cluster Computing.

[18]  GeunSik Jo,et al.  Brokerage between buyer and seller agents using Constraint Satisfaction Problem models , 2000, Decis. Support Syst..

[19]  K. Robert Lai,et al.  Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling , 2016, Eng. Appl. Artif. Intell..

[20]  Salima Benbernou,et al.  A survey on service quality description , 2013, CSUR.

[21]  Farrukh Aslam Khan,et al.  A framework to address inconstant user requirements in cloud SLAs management , 2014, Cluster Computing.

[22]  Kwang Mong Sim,et al.  Agent-Based Interactions and Economic Encounters in an Intelligent InterCloud , 2015, IEEE Transactions on Cloud Computing.

[23]  Salvatore Venticinque,et al.  A Cloud Agency for SLA Negotiation and Management , 2010, Euro-Par Workshops.

[24]  K. Robert Lai,et al.  Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation , 2010, Applied Intelligence.

[25]  Nicholas R. Jennings,et al.  Negotiation decision functions for autonomous agents , 1998, Robotics Auton. Syst..

[26]  Benoit Hudzia,et al.  Future Generation Computer Systems Optimis: a Holistic Approach to Cloud Service Provisioning , 2022 .

[27]  Rajkumar Buyya,et al.  An Autonomous Time-Dependent SLA Negotiation Strategy for Cloud Computing , 2015, Comput. J..

[28]  Jon C. Dattorro,et al.  Convex Optimization & Euclidean Distance Geometry , 2004 .

[29]  Mohd Fadzil Hassan,et al.  Renegotiation in Service Level Agreement Management for a Cloud-Based System , 2015, ACM Comput. Surv..

[30]  Athanasios V. Vasilakos,et al.  A Framework for Truthful Online Auctions in Cloud Computing with Heterogeneous User Demands , 2016, IEEE Trans. Computers.

[31]  Kwang Mong Sim,et al.  Agent-Based Cloud Computing , 2012, IEEE Transactions on Services Computing.

[32]  K. Lai.  Fuzzy constraint processing , 1992 .

[33]  Se-Hak Chun,et al.  Service models and pricing schemes for cloud computing , 2013, Cluster Computing.

[34]  Nicholas R. Jennings,et al.  A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments , 2003, Artif. Intell..

[35]  Weiming Shen,et al.  A quality of service (QoS)-aware execution plan selection approach for a service composition process , 2012, Future Gener. Comput. Syst..

[36]  Rajkumar Buyya,et al.  Automated SLA Negotiation Framework for Cloud Computing , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[37]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[38]  Patrick C. K. Hung,et al.  WS-Negotiation: an overview of research issues , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.