LaSS: Running Latency Sensitive Serverless Computations at the Edge
LaSS: Running Latency Sensitive Serverless Computations at the Edge
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DOI：
10.1145/3431379.3460646
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发表时间：
2020-06
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影响因子：
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通讯作者：
Bin Wang;Ahmed Ali-Eldin;Prashant J. Shenoy
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文献类型：
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作者：
Bin Wang;Ahmed Ali-Eldin;Prashant J. Shenoy
无服务器计算已经成为在云中运行短期计算的新范例。由于其处理物联网工作负载的能力，人们对在边缘运行无服务器功能非常感兴趣。然而，边缘的约束性质和工作负载的延迟敏感性质导致无服务器平台面临许多挑战。在本文中，我们介绍了LaSS，一个使用模型驱动方法在边缘资源上运行延迟敏感的无服务器计算的平台。LaSS使用基于原则的方法来确定每个托管功能的适当分配，并根据工作负载动态自动缩放分配的资源。LaSS使用公平共享分配方法来保证在存在过载的情况下向每个功能分配最小的资源。此外，它利用基于容器收缩和终止的资源回收方法，将资源从过度配置的功能重新分配到配置不足的功能。我们在OpenWhisk无服务器边缘集群上实现了我们方法的原型，并进行了详细的实验评估。我们的研究结果表明，LaSS可以准确地预测高度动态工作负载下无服务器功能所需的资源，并在数百毫秒内重新配置容器容量，同时保持公平的份额分配保证。
Serverless computing has emerged as a new paradigm for running short-lived computations in the cloud. Due to its ability to handle IoT workloads, there has been considerable interest in running serverless functions at the edge. However, the constrained nature of the edge and the latency sensitive nature of workloads result in many challenges for serverless platforms. In this paper, we present LaSS, a platform that uses model-driven approaches for running latency-sensitive serverless computations on edge resources. LaSS uses principled queuing-based methods to determine an appropriate allocation for each hosted function and auto-scales the allocated resources in response to workload dynamics. LaSS uses a fair-share allocation approach to guarantee a minimum of allocated resources to each function in the presence of overload. In addition, it utilizes resource reclamation methods based on container deflation and termination to reassign resources from over-provisioned functions to under-provisioned ones. We implement a prototype of our approach on an OpenWhisk serverless edge cluster and conduct a detailed experimental evaluation. Our results show that LaSS can accurately predict the resources needed for serverless functions in the presence of highly dynamic workloads, and reprovision container capacity within hundreds of milliseconds while maintaining fair share allocation guarantees.