A Microservice-Based Platform for Sustainable and Intelligent SLO Fulfilment and Service Management

TL;DR

This paper introduces CASCA, an open-source microservice platform enabling cloud providers to reconfigure services dynamically for sustainable and intelligent SLO fulfillment while preserving developer privacy.

Contribution

The paper presents CASCA, a novel microservice-based platform that facilitates privacy-preserving, intelligent reconfiguration of services to meet SLOs in the Computing Continuum.

Findings

CASCA successfully reconfigured services in a real testbed.

Decision systems in Bash, Rust, and Python were effective.

The platform maintained privacy while optimizing SLOs.

Abstract

The Microservices Architecture (MSA) design pattern has become a staple for modern applications, allowing functionalities to be divided across fine-grained microservices, fostering reusability, distribution, and interoperability. As MSA-based applications are deployed to the Computing Continuum (CC), meeting their Service Level Objectives (SLOs) becomes a challenge. Trading off performance and sustainability SLOs is especially challenging. This challenge can be addressed with intelligent decision systems, able to reconfigure the services during runtime to meet the SLOs. However, developing these agents while adhering to the MSA pattern is complex, especially because CC providers, who have key know-how and information to fulfill these SLOs, must comply with the privacy requirements of application developers. This work presents the Carbon-Aware SLO and Control plAtform (CASCA), an open-source MSA-based platform that allows CC providers to reconfigure services and fulfill their SLOs while maintaining the privacy of developers. CASCA is architected to be highly reusable, distributable, and easy to use, extend, and modify. CASCA has been evaluated in a real CC testbed for a media streaming service, where decision systems implemented in Bash, Rust, and Python successfully reconfigured the service, unaffected by upholding privacy.