Prediction-driven resource provisioning for serverless container runtimes

TL;DR

This paper introduces SLOPE, a neural network-based prediction framework for serverless FaaS platforms that optimizes container provisioning, reduces costs, and ensures service-level objectives amidst dynamic workloads.

Contribution

SLOPE is a novel prediction framework that leverages past data and application similarity to improve resource provisioning in serverless environments.

Findings

Reduces operating costs by 66.25% on average.

Effectively predicts resource needs using neural networks and graph similarity.

Enhances SLOP's ability to meet SLOs under dynamic workloads.

Abstract

In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, as (i) real-world FaaS workloads may exhibit highly dynamic request patterns, (ii) applications have service-level objectives (SLOs) that must be met, and (iii) container provisioning can be a costly process. In this paper, we present SLOPE, a prediction framework for serverless FaaS platforms to address the aforementioned challenges. Specifically, it trains a neural network model that utilizes knowledge from past runs in order to estimate the number of instances required to satisfy the invocation rate requirements of the serverless applications. In cases that a priori knowledge is not available, SLOPE makes predictions using a graph edit distance approach to capture the similarities among serverless applications. Our experimental results illustrate the efficiency and benefits of our approach, which can reduce the operating costs by 66.25% on average.