Fifer: Tackling Resource Underutilization in the Serverless Era

Datacenters are witnessing a rapid surge in the adoption of serverless functions for microservices-based applications. A vast majority of these microservices typically span less than a second, have strict SLO requirements, and are chained together as per the requirements of an application. The aforementioned characteristics introduce a new set of challenges, especially in terms of container provisioning and management, as the state-of-the-art resource management frameworks, employed in serverless platforms, tend to look at microservice-based applications similar to conventional monolithic applications. Hence, these frameworks suffer from microservice agnostic scheduling and colossal container over-provisioning, especially during workload fluctuations, thereby resulting in poor resource utilization. In this work, we quantify the above shortcomings using a variety of workloads on a multi-node cluster managed by the Kubernetes and Brigade serverless framework. To address them, we propose Fifer --- an adaptive resource management framework to efficiently manage function-chains on serverless platforms. The key idea is to make Fifer (i) utilization conscious by efficiently bin packing jobs to fewer containers using function-aware container scaling and intelligent request batching, and (ii) at the same time, SLO-compliant by proactively spawning containers to avoid cold-starts, thus minimizing the overall response latency. Combining these benefits, Fifer improves container utilization and cluster-wide energy consumption by 4× and 31%, respectively, without compromising on SLO's, when compared to the state-of-the-art schedulers employed by serverless platforms.

数据中心在基于微服务的应用程序中的无服务器功能中迅速激增特征介绍了一系列新的挑战，尤其是在容器的配置和管理方面，随着最先进的资源管理框架在无服务器平台中带来的，倾向于研究类似于传统的单片应用程序的基于微服务的应用程序，这些框架遭受了微服务不可知论的调度和巨大的容器，尤其是在工作负载波动期间，因此在这项工作中导致了资源不佳。 kubernetes和Brigade无服务器框架。使用功能感知的容器缩放和智能请求批处理，以及（ii）同时，通过主动产卵容器来避免使用冷启动，从而使整体响应延迟最小化。 - 与无服务器平台工作的最新调度程序相比，与SLO的范围内分别耗能4倍和31％，而没有损害SLO的能源。