Fusionize++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization

TL;DR

Fusionize++ is a framework that automatically optimizes serverless applications by dynamically fusing functions and managing infrastructure to reduce latency and costs, simplifying development and improving performance.

Contribution

It introduces a novel automated framework that fuses serverless functions and optimizes infrastructure at runtime, enhancing performance and reducing costs.

Findings

Reduced median request-response latency by over 35%.

Lowered application deployment costs by more than 35%.

Effectively manages function fusion and infrastructure adaptation in real-world scenarios.

Abstract

The Function-as-a-Service (FaaS) execution model increases developer productivity by removing operational concerns such as managing hardware or software runtimes. Developers, however, still need to partition their applications into FaaS functions, which is error-prone and complex: Encapsulating only the smallest logical unit of an application as a FaaS function maximizes flexibility and reusability. Yet, it also leads to invocation overheads, additional cold starts, and may increase cost due to double billing during synchronous invocations. Conversely, deploying an entire application as a single FaaS function avoids these overheads but decreases flexibility. In this paper we present Fusionize, a framework that automates optimizing for this trade-off by automatically fusing application code into an optimized multi-function composition. Developers only need to write fine-grained application code following the serverless model, while Fusionize automatically fuses different parts of the application into FaaS functions, manages their interactions, and configures the underlying infrastructure. At runtime, it monitors application performance and adapts it to minimize request-response latency and costs. Real-world use cases show that Fusionize can improve the deployment artifacts of the application, reducing both median request-response latency and cost of an example IoT application by more than 35%.