Policy-Based Management of Cloud Systems

基于策略的云系统管理

• 批准号: RGPIN-2014-05331
• 负责人: Lutfiyya, Hanan
• 金额: $ 1.46万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634426
• 关键词: Policy; Based; Management; Cloud; Systems

The resources provided by a cloud infrastructure can be shared by multiple applications from different organizations. Allocating computing resources based on peak demand often results in underutilized computing resources. One approach to dealing with this is to allow for over-subscription. The assumption is that the peak demand of all applications may be more than available computing resources provided by the cloud infrastructure but the peak demand typically does not occur simultaneously. However, it is entirely possible that the peak demand of applications on a single server or a rack or a cluster may exceed the capacity. This requires that resources be allocated dynamically and in a timely fashion in response to overload on any part of the cloud infrastructure. This is referred to as dynamic resource management. There are several challenges being addressed with the proposed work. Some of these include the following:(1) Dynamic resource management requires that issues that arise with conflicting goals is addressed. For example, it is desirable to minimize power consumption. However, a primary focus on power consumption may lead to performance problems. A focus on performance management may lead to increased power consumption. (2) The detection of performance problems is not the same as the identification of the cause. It is important to address this in order to make the correct decisions with regard to re-allocation of resources.(3) Increasingly clients have applications with multiple software components. In re-allocating resources we must consider the impact on migrating a software component if other software components depend on it. (4) There are multiple actions that can be taken to address overload. One challenge is learning which action is best for a particular scenario.(5) The development of software to implement resource management algorithms needs to be made easier.(6) Different cloud providers will make different decisions for similar overload situations. The decisions would be based on the cloud provider’s business goals, which vary from provider to provider.Addressing these problems is important for the next-generation of cloud infrastructures that need to maintain expected performance for client applications and yet be energy efficient.

云基础设施提供的资源可以由来自不同组织的多个应用程序共享。根据峰值需求分配计算资源通常会导致计算资源利用不足。假设是允许超额订阅。所有应用程序的峰值需求可能超过云基础设施提供的可用计算资源，但峰值需求通常不会同时发生，但是，单个服务器或机架或集群上的应用程序的峰值需求完全有可能发生。这需要动态分配资源。并及时响应云基础设施任何部分的过载，这被称为动态资源管理。其中一些挑战包括：(1) 动态资源管理。要求解决因目标冲突而产生的问题，例如，希望尽量减少功耗，但是，主要关注功耗可能会导致性能问题。性能问题的检测不同于性能问题的识别为了在资源重新分配方面做出正确的决策，解决这个问题非常重要。(3) 越来越多的客户拥有具有多个软件组件的应用程序，在重新分配资源时，我们必须考虑对迁移的影响。 (4) 可以采取多种措施来解决过载问题。其中一个挑战是了解哪种措施最适合特定场景。(5) 需要开发实现资源管理算法的软件。 (6) 不同的云提供商会针对类似的过载情况做出不同的决策。这些决策将基于云提供商的业务目标，而业务目标因提供商而异。解决这些问题对于下一代云基础设施非常重要。需要保持客户端应用程序的预期性能，同时保持节能。