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PNap: Lifecycle-aware Edge Multi-state sleep for Energy Efficient MEC

Federico Giarrè, Holger Karl

Abstract

Multi-access Edge Computings (MECs) enables low-latency services by executing applications at the network edge. To fulfill low-latency requirements of mobile users, providers have to keep multiple edge servers running at multiple locations, even when, in low-load phases, their capacity is not needed. This significantly increases energy consumption. Multi-state sleep mechanisms mitigate this issue by allowing servers to enter progressively deeper sleep states, trading energy savings for longer wake-up delays. At the same time, service execution depends on non-instantaneous lifecycle operations that cannot be performed while servers are asleep, tightly coupling energy management with service continuity. This paper introduces PowerNap (PNap), a lifecycle-aware orchestration framework that jointly manages server sleep states and service lifecycle states. By leveraging traffic forecasting, PNap jointly minimizes the number of active edge servers and service disruptions. We compare PNap against baselines approaches and a state-of-the-art approach. Results validate PNap, showing how it can reduce energy consumption by up to 14.9% with respect to a state-of-the-art solution while matching its service availability results.

PNap: Lifecycle-aware Edge Multi-state sleep for Energy Efficient MEC

Abstract

Multi-access Edge Computings (MECs) enables low-latency services by executing applications at the network edge. To fulfill low-latency requirements of mobile users, providers have to keep multiple edge servers running at multiple locations, even when, in low-load phases, their capacity is not needed. This significantly increases energy consumption. Multi-state sleep mechanisms mitigate this issue by allowing servers to enter progressively deeper sleep states, trading energy savings for longer wake-up delays. At the same time, service execution depends on non-instantaneous lifecycle operations that cannot be performed while servers are asleep, tightly coupling energy management with service continuity. This paper introduces PowerNap (PNap), a lifecycle-aware orchestration framework that jointly manages server sleep states and service lifecycle states. By leveraging traffic forecasting, PNap jointly minimizes the number of active edge servers and service disruptions. We compare PNap against baselines approaches and a state-of-the-art approach. Results validate PNap, showing how it can reduce energy consumption by up to 14.9% with respect to a state-of-the-art solution while matching its service availability results.
Paper Structure (22 sections, 14 equations, 8 figures, 3 tables, 2 algorithms)

This paper contains 22 sections, 14 equations, 8 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. Service Lifeycle
  5. End-to-end latency computation
  6. Wireless delay
  7. Network forwarding
  8. Service time at
  9. Power consumption
  10. Processing Load
  11. State and Transitions
  12. Problem Formulation
  13. Proposed Approach
  14. Numerical Evaluation
  15. Scenario
  16. ...and 7 more sections

Figures (8)

  • Figure 1: Example of wake-up delay and service lifecycle at user connection.
  • Figure 2: Lifecycle of a servicegiarre_surfing_2025
  • Figure 3: Example of PNap time-evolving coverage approach.
  • Figure 4: Average percentage of energy consumed over the peak possible consumption with respect to an increasing latency allowed. Standard deviation shown as error bars.
  • Figure 5: Average user-perceived service availability with respect to an increasing latency allowed. Standard deviation shown as error bars.
  • ...and 3 more figures