SkewCache: Skewed Layer-wise Caching for Function Chains in Serverless Edge Computing

In serverless edge computing (SEC), traditional monolithic applications are encapsulated in multiple dependent functions, in which event-driven service provisioning introduces the cold start problem. Existing works adopt uniform container caching for each function to mitigate the cold start problem, which remains with relatively low efficiency due to ignoring the skewed invocation frequency and the heterogeneity of cold-start behaviors of functions. In this paper, we propose SkewCache, an efficient layer-wise container caching framework for frequency-skewed function chains in SEC. Based on the container’s layer structure, SkewCache enables fine-grained and balanced container caching on multiple edge servers, which takes into account both the invocation frequency skewness and the cold start latency of each function. The problem is modeled as an integer nonlinear programming (INLP) to minimize the application completion time (ACT). To solve the INLP, we first convert it to an equivalent integer linear programming (ILP) form. Then, we propose an approximation algorithm to solve the ILP with a guaranteed approximation ratio. To evaluate the performance of the proposed algorithm, we conduct intensive simulations and the results show that our algorithms outperform baselines, achieving up to 1.94× speedup in terms of ACT reduction.