A Self-adaptive Approach for Managing Applications and Harnessing Renewable Energy for Sustainable Cloud Computing

TL;DR

This paper presents a self-adaptive resource management approach for cloud data centers that reduces carbon emissions by maximizing renewable energy use and minimizing brown energy consumption, using microservices and adaptive algorithms.

Contribution

It introduces a novel self-adaptive management framework leveraging microservices, brownout, and deferring algorithms to optimize renewable energy utilization in cloud computing.

Findings

Reduces brown energy usage by 21%.

Increases renewable energy usage by 10%.

Improves energy efficiency in cloud data centers.

Abstract

Rapid adoption of Cloud computing for hosting services and its success is primarily attributed to its attractive features such as elasticity, availability and pay-as-you-go pricing model. However, the huge amount of energy consumed by cloud data centers makes it to be one of the fastest growing sources of carbon emissions. Approaches for improving the energy efficiency include enhancing the resource utilization to reduce resource wastage and applying the renewable energy as the energy supply. This work aims to reduce the carbon footprint of the data centers by reducing the usage of brown energy and maximizing the usage of renewable energy. Taking advantage of microservices and renewable energy, we propose a self-adaptive approach for the resource management of interactive workloads and batch workloads. To ensure the quality of service of workloads, a brownout-based algorithm for interactive workloads and a deferring algorithm for batch workloads are proposed. We have implemented the proposed approach in a prototype system and evaluated it with web services under real traces. The results illustrate our approach can reduce the brown energy usage by 21% and improve the renewable energy usage by 10%.