Machine learning Scheme for Managing Virtual Computing Resources in Cloud Market

Bashar Igried, A. Alsarhan, Mohammad Alauthman, Ammar Almomani
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引用次数: 0

Abstract

Cloud provider can maximize his profit while guaranteeing quality of service (QoS) required by clients. In this work, we propose new scheme for maximizing cloud provider profit while guaranteeing QoS for clients. Cloud provider (CP) manages all available resources on cloud market. The key objective of our scheme is extracting the optimal charging strategy for serving clients requests to maximize CP's profit by choosing proper set of requests to be served, subject to uncertain demand of service and time-varying service cost. As variety of clients classes submit their requests and offer different prices for service, this problem deserves special treatment. To tackle the uncertain service prices, Q-Iearning is proposed to extract the optimal charging policy. Numerical results show that the proposed scheme can improve the reward of CP significantly.
云市场中虚拟计算资源管理的机器学习方案
云提供商可以在保证客户所需的服务质量(QoS)的同时实现利润最大化。在这项工作中,我们提出了一种新的方案,以最大化云提供商的利润,同时保证客户的QoS。云提供商(CP)管理云市场上所有可用的资源。该方案的主要目标是在服务需求不确定和服务成本随时间变化的情况下,通过选择合适的服务请求集,提取服务客户请求的最优收费策略,使CP的利润最大化。由于不同的客户阶层提出不同的要求,提供不同的服务价格,这个问题需要特别处理。针对服务价格的不确定性,提出了q -学习方法提取最优收费策略。数值结果表明,该方案能显著提高CP的报酬。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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