Abstract: In distributed service resource environments, a large number of service resources are underutilized or even idle for a long time due to the narrow percentage of peak load. Adaptive elastic scaling of resources can significantly improve system energy efficiency and reduce operation overhead: on the one hand, service resources can be expanded to cope with high demand during high load; and on the other hand, they can be scaled down to reduce the overhead during low load. However, the actual load is usually highly volatile, and the service resources required to satisfy the quality of service are constantly varying, which poses a great challenge to the adaptive elastic scaling of service resources. Although existing commercial distributed platforms generally have some resource elastic scaling capability, they suffer from limited adaptive ability and poor precision, and there exists a considerable improvement space. In order to promote the research and application development in this field, this paper analyzes the studies on adaptive elastic scaling of service resources in this environment. Firstly, the corresponding research background and main challenges in demand evaluation and resource adjustment are analyzed. Secondly, a categorized survey of domestic and international studies in this field is conducted, by dividing these studies into three categories based on the resource objects they adjust. The similarities and differences of these studies are compared, and their respective characteristics and usefulness are summarized. Finally, these study efforts are summarily analyzed, then a comprehensive realization is outlined based on the analysis, and besides, the current application status, research challenges and future research trends are discussed.

Key words: parallel and distributed computing, distributed service resources, resource management and allocation, adaptive elastic scaling

摘要： 分布式服务资源环境下，由于峰值负载的整体占比很小，大量服务资源长期处于低利用率甚至闲置状态。通过实现资源自适应弹性伸缩，在高负载时扩充服务资源以应对高需求，在低负载时将其缩减以降低开销，可显著提升系统能效并降低运作开销。但实际负载通常具有很强的波动性，满足服务质量所需的服务资源持续变化，这给服务资源自适应弹性伸缩带来了巨大挑战。尽管现有的商用分布式平台已普遍具有一定的资源弹性伸缩能力，但它们的自适应能力有限、精准性不佳，存在很大提升空间。为促进该领域的研究与应用发展，就该环境下服务资源自适应弹性伸缩研究进行分类分析与探讨。分析并介绍了相应的研究背景及主要存在于需求评估与资源调整上的挑战；就该领域的国内外相关研究，依据其调整的资源对象分为三类，以此进行分类论述并比较了各研究工作的异同，且就各自的特点与效用进行了分析与总结；总述分析了这些研究工作并概括出一个全面而整体的实现，探讨了业界的应用现状、研究面临的挑战以及未来趋势。

关键词: 并行与分布式计算, 分布式服务资源, 资源管理与分配, 自适应弹性伸缩