Caching Middleware for FaaS in Edge-Cloud Continuum based on Remote Direct Memory Access

Abstract

Cloud computing, and serverless computing in particular, are transforming traditional software architecture by making components and even entire systems available on scalable resources as needed. Function-as-a-Service (FaaS) is a specific paradigm in which users make function code available in the cloud and operate them there in an event-driven manner. One of the fundamental performance hurdles of FaaS is the overhead introduced by state-passing. Serverless functions are typically short-lived and stateless. If one intends to pass state along function chaining, inefficient messaging protocols are necessary to achieve statefulness. In that sense, caching appears as a feasible solution to persist state between function executions. As caching introduces latency when interacting with the cache, we focus on integrating high-performance computing protocols in a common cloud stack, to efficiently gap this pitfall. Remote Direct Memory Access (RDMA) allows to bypass the CPU and to directly insert data into the memory of a peer computing instance, saving CPU cycles and thus reducing latency experienced in communication. Within this thesis, we present the design and implementation of a dedicated caching middleware layer for communication of functions in a FaaS towards a distributed caching system using RDMA. This middleware abstraction layer enables us to connect functions on a FaaS to the cache without introducing additional overhead for the functions. The middleware layer takes values to be written and stores or reads them from the cache using RDMA communication. We design the system with the aim of being functional for various hardware requirements and integrating seamlessly with established cloud technologies. In this context, we identify a software abstraction for RDMA called SoftiWARP that allows us to integrate RDMA without relying on specialized hardware. We evaluate the system we have designed with the comparable state-of-the-art caching system Redis. While we experience significant overhead for small data values of 40 bytes, we observe up to 91% improved latency values for large data values greater than 1 MB.Our results indicate that the integration of high-performance protocols, even if abstracted by software, represents an advantageous further development for the design of serverless cloud concepts.