arXiv 1702.074312017-02-24cs.SEcs.DC

一种用于云弹性管理中资源适应的债务感知学习方法

A Debt-Aware Learning Approach for Resource Adaptations in Cloud Elasticity Management

Carlos Mera-Gómez, Francisco Ramírez, Rami Bahsoon, Rajkumar Buyya

摘要：

弹性是一种云属性，它使应用程序及其执行系统能够按需动态获取和释放共享的计算资源。此外，它通过降低这些共享资源的平均成本，展现了云中规模经济的优势。然而，它仍然是一个开放的挑战，以实现资源需求和供应之间的完美匹配，在自主弹性管理。资源自适应决策本质上涉及经济性和性能之间的权衡，这会在理想和实际资源配置之间产生差距。如果管理不当，这一差距可能会对云客户的长期总体效用产生负面影响。为了解决这个问题，我们提出了一个技术债务感知学习方法的自主弹性管理的基础上的弹性债务资源配置的强化学习的适应追求的战略决策，权衡经济性能。我们扩展CloudSim和Burlap来评估我们的方法。评估表明，弹性技术债务的强化学习为云客户提供了更高的效用，同时符合预期的性能水平。

英文摘要：

Elasticity is a cloud property that enables applications and its execution systems to dynamically acquire and release shared computational resources on demand. Moreover, it unfolds the advantage of economies of scale in the cloud through a drop in the average costs of these shared resources. However, it is still an open challenge to achieve a perfect match between resource demand and provision in autonomous elasticity management. Resource adaptation decisions essentially involve a trade-off between economics and performance, which produces a gap between the ideal and actual resource provisioning. This gap, if not properly managed, can negatively impact the aggregate utility of a cloud customer in the long run. To address this limitation, we propose a technical debt-aware learning approach for autonomous elasticity management based on a reinforcement learning of elasticity debts in resource provisioning; the adaptation pursues strategic decisions that trades off economics against performance. We extend CloudSim and Burlap to evaluate our approach. The evaluation shows that a reinforcement learning of technical debts in elasticity obtains a higher utility for a cloud customer, while conforming expected levels of performance.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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