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Adaptive resource configuration for Cloud infrastructure management

机译：用于云基础架构管理的自适应资源配置

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摘要

To guarantee the vision of Cloud Computing QoS goals between the Cloud provider and the customer have to be dynamically met. This so-called Service Level Agreement (SLA) enactment should involve little human-based interaction in order to guarantee the scalability and efficient resource utilization of the system. To achieve this we start from Autonomic Computing, examine the autonomic control loop and adapt it to govern Cloud Computing infrastructures. We first hierarchically structure all possible adaptation actions into so-called escalation levels. We then focus on one of these levels by analyzing monitored data from virtual machines and making decisions on their resource configuration with the help of knowledge management (KM). The monitored data stems both from synthetically generated workload categorized in different workload volatility classes and from a real-world scenario: scientific workflow applications in bioinformatics. As KM techniques, we investigate two methods, Case-Based Reasoning and a rule-based approach. We design and implement both of them and evaluate them with the help of a simulation engine. Simulation reveals the feasibility of the CBR approach and major improvements by the rule-based approach considering SLA violations, resource utilization, the number of necessary reconfigurations and time performance for both, synthetically generated and real-world data.
机译：为了保证实现云计算的愿景，必须动态地满足云提供商和客户之间的QoS目标。这种所谓的“服务水平协议”（SLA）法规应包含很少的基于人的交互，以保证系统的可伸缩性和有效的资源利用。为了实现这一目标，我们从自主计算开始，检查自主控制环，并使其适应于云计算基础架构。我们首先将所有可能的适应动作按层次结构化为所谓的升级级别。然后，我们通过分析来自虚拟机的监视数据并借助知识管理（KM）对其资源配置做出决策，从而专注于这些级别之一。监视的数据既来自分类为不同工作负载波动性类别的综合生成的工作负载，又来自现实世界的场景：生物信息学中的科学工作流程应用程序。作为KM技术，我们研究了两种方法，基于案例的推理和基于规则的方法。我们设计和实现它们，并在仿真引擎的帮助下对其进行评估。仿真显示了CBR方法的可行性和基于规则的方法的重大改进，其中考虑了SLA违规，资源利用，必要重新配置的次数以及综合性能和实际数据的时间性能。

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