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Agent-based multi-tier SLA negotiation for intercloud

delete2022-06-28
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OA
AI
李林 (Lin Li)
刘骊 cover
刘骊 (Li Liu)
S
Shalin Huang
S
Shibiao Lv
林开标 cover
林开标 (Kai-Biao Lin)
朱顺痣 cover
朱顺痣 (Shunzhi Zhu) *
DOI:10.1186/s13677-022-00286-6delete
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Abstract

Abstract

En 中文
The evolving intercloud enables idle resources to be traded among cloud providers to facilitate utilization optimization and to improve the cost-effectiveness of the service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, in which cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, in this paper a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process is proposed. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent's behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent's behavior. Moreover, this information can guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can improve the negotiation performance and the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and demonstrates the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.
Keywords:
Multi-agent negotiation
SLA negotiation
Multi-tier negotiation
Cloud computing
Intercloud

Journal

J
Journal of Cloud Computing-Advances Systems and Applications
IF:
4.3
Papers:
737
Citations:
2.2K

Organization

X
Xiamen University of Technology
Scholars:
3.8K
Papers: 2.5K
Citations: 5.1K