EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

TL;DR

EnergAt introduces a thread-level, NUMA-aware method for precise energy attribution in multi-tenant environments, addressing the lack of hardware support and enabling sustainable cloud computing.

Contribution

The paper presents a novel software-centric energy attribution approach that works with limited hardware support for multi-tenant systems.

Findings

EnergAt accurately attributes energy consumption at thread level.

The method remains effective despite noisy-neighbor effects.

Prototype validation confirms theoretical model's robustness.

Abstract

In the post-Moore's Law era, relying solely on hardware advancements for automatic performance gains is no longer feasible without increased energy consumption, due to the end of Dennard scaling. Consequently, computing accounts for an increasing amount of global energy usage, contradicting the objective of sustainable computing. The lack of hardware support and the absence of a standardized, software-centric method for the precise tracing of energy provenance exacerbates the issue. Aiming to overcome this challenge, we argue that fine-grained software energy attribution is attainable, even with limited hardware support. To support our position, we present a thread-level, NUMA-aware energy attribution method for CPU and DRAM in multi-tenant environments. The evaluation of our prototype implementation, EnergAt, demonstrates the validity, effectiveness, and robustness of our theoretical model, even in the presence of the noisy-neighbor effect. We envisage a sustainable cloud environment and emphasize the importance of collective efforts to improve software energy efficiency.