Multi-tenancy in resource-constrained environments is a key challenge in Edge computing. In this paper, we develop ‘DYVERSE: DYnamic VERtical Scaling in Edge’ environments, which is the first light-weight and dynamic vertical scaling mechanism for managing resources allocated to applications for facilitating multi-tenancy in Edge environments. To enable dynamic vertical scaling, one static and three dynamic priority management approaches that are workload-aware, community-aware and system-aware, respectively are proposed. This research advocates that dynamic vertical scaling and priority management approaches reduce Service Level Objective (SLO) violation rates. An online-game and a face detection workload in a Cloud-Edge test-bed are used to validate the research. The merit of DYVERSE is that there is only a sub-second overhead per Edge server when 32 Edge servers are deployed on a single Edge node. When compared to executing applications on the Edge servers without dynamic vertical scaling, static priorities and dynamic priorities reduce SLO violation rates of requests by up to 4% and 12% for the online game, respectively, and in both cases 6% for the face detection workload. Moreover, for both workloads, the system-aware dynamic vertical scaling method effectively reduces the latency of non-violated requests, when compared to other methods.

资源受限环境中的多租户是Edge计算中的关键挑战。在本文中，我们开发了“ DYVERSE：Edge中的动态垂直缩放”环境，这是第一种用于管理分配给应用程序的资源的轻量级动态垂直缩放机制，以促进Edge环境中的多租户。为了实现动态垂直扩展，提出了一种分别为工作负载感知，社区感知和系统感知的静态和三种动态优先级管理方法。这项研究主张动态垂直扩展和优先级管理方法可降低服务水平目标（SLO）违反率。Cloud-Edge测试平台中的在线游戏和面部检测工作量用于验证研究。DYVERSE的优点是，当在单个Edge节点上部署32台Edge服务器时，每台Edge服务器只有不到一秒的开销。与没有动态垂直扩展的Edge服务器上执行应用程序相比，静态优先级和动态优先级分别将在线游戏的请求SLO违规率分别降低了4％和12％，在这两种情况下，面部检测都分别将6％工作量。此外，对于两种工作负载，与其他方法相比，系统感知的动态垂直扩展方法可有效减少未违反请求的延迟。分别，在两种情况下，面部检测工作量均为6％。此外，对于两种工作负载，与其他方法相比，系统感知的动态垂直扩展方法可有效减少未违反请求的延迟。分别，在两种情况下，面部检测工作量均为6％。此外，对于两种工作负载，与其他方法相比，系统感知的动态垂直扩展方法可有效减少未违反请求的延迟。