Agent-based Self-Adaptive System for Cloud

G. Shyam, F. Azam
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Abstract

There is a need to analyze the appropriate assets/resource requirements and identify the reason for Quality-of-Service (QoS) degradation (if any), and ensure better assets/resource allocation decisions in Cloud computing scenario. In this work, agents are used to achieve the same through the condition of assets/resource at run-time and about expectations by clients. We have developed an agent application learning system for decision making including readily available globalized and localized information, and client assets/resource requirements etc. Results demonstrate that the proposed system can enhance assets/resource utility, while fulfilling the QoS requested by clients.
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基于agent的云自适应系统
有必要分析适当的资产/资源需求,确定服务质量(QoS)下降的原因(如果有的话),并确保在云计算场景中做出更好的资产/资源分配决策。在这项工作中,代理通过运行时的资产/资源条件和客户端的期望来实现相同的目标。我们开发了一个代理应用程序学习系统,用于决策制定,包括随时可用的全球化和本地化信息,以及客户资产/资源需求等。结果表明,该系统在满足客户端QoS要求的同时,提高了资产/资源的利用率。
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