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Resource Management Framework for Multi-tier Service Using Case-Based Reasoning and Optimization Algorithm

机译：基于案例推理和优化算法的多层服务资源管理框架

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摘要

The emergence of cloud computing has made elasticity of virtual resources one of the most critical features of cloud service. Such elasticity reflects the fluctuation of resource provisioning due to the variety of service demands. Most high-demanding services adopt a multi-tier architecture. However, offering quality-of-service () guarantee for these services with least resource usage costs under dynamic and unpredictable workloads and different resource demands is a significantly complex problem. Therefore, cloud providers () need to adopt a dynamic resource optimization and provisioning framework. Numerous rule-based and model-based approaches have been designed for dynamic resource provisioning in virtualized data centers. However, these approaches mainly focus on providing service-level guarantees for running services and most of them do not address mainly the problem of minimizing the number of running virtual machines in order to increase profit. This research proposes a new resource optimization and provisioning () framework to detect, solve the bottlenecks, and satisfy the service-level requirements of running services and to increase the profits. To demonstrate the effectiveness of the proposed against other approaches, a prototype running on a cloud platform is developed, and a workload generator and multi-tier service model are adopted. Results show that the framework outperforms other existing approaches by 75% in terms of on-demand service configurations while providing service-level guarantee for running services.
机译：云计算的出现使虚拟资源的弹性成为云服务最关键的功能之一。这种弹性反映了由于各种服务需求而导致的资源供应波动。大多数高要求的服务都采用多层体系结构。但是，在动态和不可预测的工作负载以及不同的资源需求下，以最少的资源使用成本为这些服务提供服务质量保证是一个非常复杂的问题。因此，云提供商（）需要采用动态资源优化和配置框架。已经设计了许多基于规则和基于模型的方法来在虚拟化数据中心中进行动态资源供应。但是，这些方法主要集中于为运行的服务提供服务级别的保证，而大多数方法并没有主要解决最小化运行虚拟机以增加利润的问题。本研究提出了一种新的资源优化和配置框架，以检测，解决瓶颈并满足运行服务的服务级别要求并增加利润。为了证明该建议相对于其他方法的有效性，开发了在云平台上运行的原型，并采用了工作负载生成器和多层服务模型。结果表明，该框架在按需服务配置方面比其他现有方法高出75％，同时为运行中的服务提供了服务级别的保证。

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