论文标题
JCSP：边缘计算系统的联合缓存和服务位置
JCSP: Joint Caching and Service Placement for Edge Computing Systems
论文作者
论文摘要
借助受限的资源，在边缘的什么，何处和如何缓存是边缘计算系统的关键挑战之一。缓存的项目不仅包括应用程序数据内容，还包括处理传入请求的边缘服务的本地缓存。但是，当前系统在不考虑缓存和排队的延迟相互作用的情况下将内容和服务分开。因此，在本文中，我们提出了一类新型的随机模型，可以共同优化内容缓存和服务放置决策。我们首先解释了如何将分层排队网络（LQN）模型应用于边缘服务放置，并表明将其与遗传算法相结合的资源分配的准确性比已建立的基线提供了更高的精度。接下来，我们扩展了使用缓存组件的LQN，以建立用于内容缓存和服务放置（JCSP）的联合建模方法，并目前的分析方法来分析所得模型。最后，我们模拟了现实世界中的Azure痕迹来评估JCSP方法，并发现JCSP在响应时间的提高了35％，而在存储器使用情况下的记忆使用量则比基线启发式方法降低了500MB。
With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency interplay of caching and queueing. Therefore, in this paper, we propose a novel class of stochastic models that enable the optimization of content caching and service placement decisions jointly. We first explain how to apply layered queueing networks (LQNs) models for edge service placement and show that combining this with genetic algorithms provides higher accuracy in resource allocation than an established baseline. Next, we extend LQNs with caching components to establish a joint modeling method for content caching and service placement (JCSP) and present analytical methods to analyze the resulting model. Finally, we simulate real-world Azure traces to evaluate the JCSP method and find that JCSP achieves up to 35% improvement in response time and 500MB reduction in memory usage than baseline heuristics for edge caching resource allocation.
