Elasticity Control for Latency-Intolerant Mobile Edge Applications
Elasticity Control for Latency-Intolerant Mobile Edge Applications
复制标题
不容忍延迟的移动边缘应用程序的弹性控制
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
E. Elmroth
中科院分区：
文献类型：
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作者：
Chanh Nguyen;C. Klein;E. Elmroth
弹性是移动的边缘云（MEC）成为托管软件应用程序的成熟计算平台所需的基本属性。然而，MEC必须科普传统云平台环境中不会出现的几个挑战。这些问题包括边缘数据中心（EDC）潜在的高度分布的地理部署，异构性和有限的资源容量，以及最终用户的移动性。在本文中，我们提出了一个弹性控制器，以帮助MEC通过自动主动资源扩展来克服这些挑战。控制器利用关于EDC的物理位置的信息以及物理上相邻EDC中的工作负载变化的相关性来预测EDC处的请求到达速率。这些预测被用来作为一个预测理论驱动的性能模型，估计资源的数量，应提供给EDC，以满足预定义的服务水平目标（SLO），同时最大限度地提高资源利用率的输入。该控制器还集成了一个组级负载均衡器，负责在运行时重定向EDC之间的请求，以最大限度地减少请求拒绝率。我们评估我们的方法进行模拟与模拟MEC部署在一个大都市地区和模拟应用程序的工作负载，使用真实世界的用户移动性跟踪。结果表明，我们提出的主动控制器表现出更好的缩放行为比一个国家的最先进的反应控制器，并提高了资源配置的效率，从而帮助MEC维持资源利用率和拒绝率，满足预定义的SLO，同时保持系统的稳定性。
Elasticity is a fundamental property required for Mobile Edge Clouds (MECs) to become mature computing platforms hosting software applications. However, MECs must cope with several challenges that do not arise in the context of conventional cloud platforms. These include the potentially highly distributed geographical deployment, heterogeneity, and limited resource capacity of Edge Data Centers (EDCs), and end-user mobility.In this paper, we present an elasticity controller to help MECs overcome these challenges by automatic proactive resource scaling. The controller utilizes information on the physical locations of EDCs and the correlation of workload changes in physically neighboring EDCs to predict request arrival rates at EDCs. These predictions are used as inputs for a queueing theory-driven performance model that estimates the number of resources that should be provisioned to EDCs in order to meet predefined Service Level Objectives (SLOs) while maximizing resource utilization. The controller also incorporates a grouplevel load balancer that is responsible for redirecting requests among EDCs during runtime so as to minimize the request rejection rate. We evaluate our approach by performing simulations with an emulated MEC deployed over a metropolitan area and a simulated application workload using a real-world user mobility trace. The results show that our proposed pro-active controller exhibits better scaling behavior than a state-of-the-art re-active controller and increases the efficiency of resource provisioning, thereby helping MECs to sustain resource utilization and rejection rates that satisfy predefined SLOs while maintaining system stability.