SLEEPY-rApp: Delay-aware Sleep Scheduling for Energy Efficiency in MEC-enabled O-RAN

This paper presents a novel resource allocation and MEC server deactivation strategy for MEC-enabled O-RAN networks, with the objective of optimizing energy consumption while satisfying strict delay requirements. Our approach leverages an intelligent orchestration application, SLEEPY-rApp, deployed in the SMO layer to dynamically control MEC server activation and request routing. We formulate a joint optimization problem that simultaneously considers computing capacity, end-to-end delay, and energy consumption. To address the NP-hard nature of this problem in real time, we propose a low-complexity heuristic that adapts to varying network conditions and workload patterns. Simulation results indicate that our method significantly reduces energy usage---particularly during peak operating periods---while maintaining the required quality of service. These findings underscore the potential of intelligent orchestration in enhancing the energy efficiency of future MEC-enabled O-RAN systems.

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Publication Details

Published
2025-03-30
DOI
https://doi.org/10.1145/3721889.3721927
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
1.49
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article

SLEEPY-rApp: Delay-aware Sleep Scheduling for Energy Efficiency in MEC-enabled O-RAN

Anastasios Giannopoulos, Sotirios Spantideas, Panagiotis Trakadas, George Levis
1 citations
IoT and Edge/Fog Computing
1.49
article

SLEEPY-rApp: Delay-aware Sleep Scheduling for Energy Efficiency in MEC-enabled O-RAN

Anastasios Giannopoulos, Sotirios Spantideas, Panagiotis Trakadas, George Levis
article en
1 citations

Abstract

This paper presents a novel resource allocation and MEC server deactivation strategy for MEC-enabled O-RAN networks, with the objective of optimizing energy consumption while satisfying strict delay requirements. Our approach leverages an intelligent orchestration application, SLEEPY-rApp, deployed in the SMO layer to dynamically control MEC server activation and request routing. We formulate a joint optimization problem that simultaneously considers computing capacity, end-to-end delay, and energy consumption. To address the NP-hard nature of this problem in real time, we propose a low-complexity heuristic that adapts to varying network conditions and workload patterns. Simulation results indicate that our method significantly reduces energy usage---particularly during peak operating periods---while maintaining the required quality of service. These findings underscore the potential of intelligent orchestration in enhancing the energy efficiency of future MEC-enabled O-RAN systems.

National and Kapodistrian University of Athens (GR)
Affordable and clean energy
Openalex Percentile: Top 18%
IoT and Edge/Fog Computing
1.49
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SLEEPY-rApp: Delay-aware Sleep Scheduling for Energy Efficiency in MEC-enabled O-RAN — Anastasios Giannopoulos, Sotirios Spantideas, et al. · (2025) | TGRS Research Map | TGRS