Caching Aided Multi-Tenant Serverless Computing

TL;DR

FaasCamp is a caching-enhanced multi-tenant serverless framework that improves cold-start performance by multi-tier warm pools, secure sharing, and machine learning-based cache policies, outperforming existing solutions.

Contribution

It introduces a multi-tier warm pool with secure sharing and machine learning-driven cache replacement for improved serverless performance.

Findings

FaasCamp outperforms existing platforms in warm start rate.

Multi-tier warm pools reduce cold-start latency.

ML-based cache policies enhance warm-up efficiency.

Abstract

One key to enabling high-performance serverless computing is to mitigate cold-starts. Current solutions utilize a warm pool to keep function alive: a warm-start can be analogous to a CPU cache-hit. However, modern cache has multiple hierarchies and the last-level cache is shared among cores, whereas the warm pool is limited to a single tenant for security concerns. Also, the warm pool keep-alive policy can be further optimized using cache replacement algorithms. In this paper, we borrow practical optimizations from caching, and design FaasCamp, a caching-aided multi-tenant serverless computing framework. FaasCamp extends the single-tier warm pool into multi-tiers, with a reclaim pool introduced enabling secure function instance sharing among tenants. Also, FaasCamp leverages machine learning to approximate the optimal cache replacement policy to improve the warm rate. We have implemented a prototype and conducted extensive experiments under multiple scenarios. The results show that FaasCamp can outperform existing platforms with minimal overhead.