Spanergy: Energy-aware Distributed Tracing for Microservices

• URL: http://arxiv.org/abs/2607.24902v1
• Date: Mon, 27 Jul 2026 17:15:29 GMT
• Title: Spanergy: Energy-aware Distributed Tracing for Microservices
• Abstract summary: This paper presents Spanergy, an energy-aware distributed tracing approach.<n>It correlates per-microservice power measurements with attributes measured energy consumption to request segments i.e. trace spans.<n>Spanergy reveals that a non-negligible fraction of request energy comes from spans outside the latency-critical path.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 per-microservice 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.

Related papers

• Enabling users to work sustainably on shared institute computing resources [0.5277024349608834]
  VISPA project is a self-managed, mid-scale computing cluster that supports physics data analysis in research and teaching.<n>Monitoring system now records per-job energy consumption.<n>Real-time data on the renewable share of the German power grid enable green-window' scheduling.
  arXiv  Detail & Related papers  (2026-04-13T18:17:39Z)
• Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning [0.0]
  We train a Gradient Boosting Regressor to predict energy consumption measured via RA interfaces on the host.<n>We demonstrate for the first time, guest-only resource-based energy estimation without privileged host access.
  arXiv  Detail & Related papers  (2025-09-12T06:22:01Z)
• Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design [43.89869891417806]
  Real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning.<n>We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks.
  arXiv  Detail & Related papers  (2025-08-03T13:05:11Z)
• ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy [9.516475567386768]
  Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things (IoT) applications, promising very low energy consumption. This paper introduces a Long Short-Term Memory Projection (LSTMP)-based BLE energy consumption prediction framework. Our results show that the proposed framework predicts the energy consumption of different BLE nodes with a Mean Absolute Percentage Error (MAPE) of up to 12%.
  arXiv  Detail & Related papers  (2023-08-02T13:04:23Z)
• A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks [53.561797148529664]
  Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
  arXiv  Detail & Related papers  (2023-06-25T13:10:38Z)
• Energy Loss Prediction in IoT Energy Services [0.43012765978447565]
  We propose a novel Energy Loss Prediction framework that estimates the energy loss in sharing crowdsourced energy services. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2023-05-16T09:07:08Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)
• Memory-Aware Partitioning of Machine Learning Applications for Optimal Energy Use in Batteryless Systems [17.072240411944914]
  We present Julienning: an automated method for optimizing the total energy cost of batteryless applications. Our method can reduce the required energy storage by over 94% while only incurring a 0.12% energy overhead.
  arXiv  Detail & Related papers  (2021-08-05T09:49:42Z)
• A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning [48.63610479916003]
  This article breaks down and analyzes the main factors that influence the environmental footprint of distributed learning policies. It models both vanilla and decentralized FL policies driven by consensus. Results show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless systems characterized by low bit/Joule efficiency.
  arXiv  Detail & Related papers  (2021-03-18T16:04:42Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.