Skip to content
Open access

Spanergy: Energy-Aware Distributed Tracing for Microservices

May 2026 · IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing · pp. 276-286 · 1 citation · 37 references
Computer Science

TL;DR

Results show that energy-aware tracing is feasible at modest overhead and provides actionable insights for energy-efficient microservices, and Spanergy, an energy-aware distributed tracing approach that correlates permicroservice power measurements with traces and that attributes measured energy consumption to request segments, i.e. trace spans.

Abstract

Cloud computing is gaining popularity by giving access to seemingly unlimited virtual resources. However, Cloud data centres are built with physical resources and their electricity consumption has been continuously growing over the past decades. Microservices are an important building block of Cloud applications, calling for new solutions to observe their energy consumption. Distributed tracing is widely deployed to diagnose latency and failures in microservice-based applications, yet it does not expose the energy cost of individual end-user requests. Such a gap limits energy-aware debugging, accountability, and control. This paper presents Spanergy, an energy-aware distributed tracing approach that correlates permicroservice power measurements with traces and that attributes measured energy consumption to request segments, i.e. trace spans. We showcase Spanergy with synchronous request chains and asynchronous interactions across microservices. We present a rigorous experimental protocol and statistical analysis plan to quantify overhead and to validate conservation and coverage properties on realistic configurations. Enabling OpenTelemetry tracing increased total experiment energy by 59.1% relative to the uninstrumented baseline, and Spanergy post-processing added 15.2% of the baseline energy. Hence, Spanergy's incremental energy cost is smaller than the energy overhead of enabling tracing itself, making the approach lightweight in practice. Spanergy also reveals that a non-negligible fraction of request energy comes from spans outside the latency-critical path. These results show that energy-aware tracing is feasible at modest overhead and provides actionable insights for energy-efficient microservices.

Read PDF

Similar papers

Book Open access Sep 2026

Energy Efficiency in Actor Systems: A Comparative Study of Akka and Elixir

The energy consumption of digital infrastructures and applications constitutes a critical global concern. To build a sustainable digital future, software engineering must prioritize energy efficiency alongside traditional performance metrics. Actors provide an attractive model for distributed large-scale applications;...

I. Samus, Mario Südholt, C. De Roover et al. · 0 citations
Book Open access Aug 2026

CubeTrace: Microscopic Network Tracing for Heterogeneous Cloud Gateways

CubeTrace is presented, a unified, function-level flow tracing system that enables microscopic tracing inside heterogeneous cloud gateways and introduces minimal overhead, consuming less than 1% of memory resources and adding less than 1% to forwarding latency.

Yunming Xiao, Yinchao Yang, Jiaqi Zheng et al. · 1 citation
Review Open access Aug 2026

Analysis of Microservice Approaches to V2X Systems with Edge Computing

The aim of the study is to estimate the resource cost of using KubeEdge on top of K3s under an identical microservice workload that emulates V2X message processing and to establish a baseline for subsequent evaluation of service placement algorithms.

G. Tambovtsev, A. Vladyko, P. Plotnikov · 0 citations
Preprint Aug 2026

How Accurately Can the Energy Use of Spark Applications Be Estimated Based on Resource Utilisation?

This work uses Apache Spark running on Kubernetes as a case-study dataflow runtime and cluster resource manager to compare model-based energy estimates to Intel RAPL package and DRAM energy on an AWS bare-metal cloud and an on-premises cluster, comparing different CPU usage signals and memory coefficients.

Youssef Moawad, Kathleen West, Vasilis Bountris et al. · 0 citations
Book Open access Sep 2026

QMScaler: A QoS-Constrained Resource-Efficient Microservice Autoscaling Framework for Edge Environments

QMScaler is proposed, a QoS-driven microservice horizontal scaling framework based on Monte Carlo Tree Search (MCTS) that aims to minimize the number of container instances while meeting the QoS requirements of multiple application functions.

Tian-Yang Zheng, Peng-Fei Yang, Zhe Xu et al. · 0 citations
Review Aug 2026

Slasher: Power Flexibility for Cloud Datacenters

The main power modulation scenarios are reviewed, the power reduction levers using data from production cloud datacenters are characterized, Slasher's system architecture is described, and the cloud datacenter power modulation control problem is formulated.

Liuzixuan Lin, Fiodar Kazhamiaka, A. Kumbhare et al. · 1 citation

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.