Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 18 references
Abstract
Content Delivery Networks (CDNs) and edge platforms are increasingly expected to reduce energy consumption while preserving strict Quality of Service (QoS) and Service Level Agreement (SLA) targets under highly variable demand. In practice, operators often keep excess capacity online to ab-sorb sudden spikes and to hedge against warm-up delays, which leads to persistent energy waste during off-peak periods. This paper presents a forecast driven orchestration framework that couples short-horizon workload prediction with a practical server life-cycle controller managing active, warm-standby, and off pools. The controller converts uncertainty-aware forecasts into risk-calibrated capacity decisions, using hysteresis and warm-up queue dynamics to avoid oscil-lations and to prevent transient under-provisioning. We evaluate the approach in closed-loop simulation using real Points of Presence (PoP) level traces and statis-tically constrained synthetic scenarios generated with a Large Language Model (LLM) to stress-test bursty and heavy-tailed regimes beyond the observed data. Results show that probabilistic, tail-aware provisioning improves reliability under volatile demand while still enabling meaningful energy reductions compared to static provisioning.
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
TADOF is a dynamic optimization framework that jointly performs energy-aware elastic scaling and task migration under drift-aware periodic modeling and burst detection and observed about 18–34% lower energy and 15–28% lower migration cost than threshold and A2C/TD3 baselines while preserving QoS.
Juan Guo, Yanqun Zuo, Zhixian Chang· Cluster Computing· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
The proliferation of distributed energy resources at the edge of distribution networks provides substantial flexibility for virtual power plant (VPP) operation. However, existing methods often rely on aggregate load information and homogeneous scheduling policies. They, therefore, overlook device-specific response characteristics, heterogeneous response times, and operational safety constraints. This paper presents EDGE-VPP, an end-to-end scheduling framework that connects fine-grained load perception with safety-aware decision-making across multiple temporal scales. At the perception layer, a Load Decomposition Transformer (LDT) uses learnable multi-frequency positional encodings and device-specific attention heads. It jointly detects appliance states and disaggregates device power from aggregate measurements. At the coordination layer, a three-tier cloud–edge–device architecture assigns sub-second emergency response to devices, minute-level economic dispatch to edge controllers, and hour-ahead planning to the cloud. Bidirectional information exchange mitigates conflicts among these control layers. At the optimization layer, multi-constraint proximal policy optimization factorizes continuous and discrete actions. Adaptive Lagrange multipliers enforce voltage and current limits, while two value estimators stabilize policy learning. Experiments on REDD, UK-DALE, and a self-constructed VPP dataset show that LDT reduces mean absolute error by up to 6.86% and improves the F1-score by 3.51% over the Transformer baseline. The complete EDGE-VPP framework also achieves the lowest operating cost and the fewest constraint violations among the evaluated scheduling methods.
Managing containerized workloads in cloud-native infrastructures poses complex challenges due to the need to simultaneously balance performance, efficiency, and sustainability. This work proposes an adaptive resource allocation framework that leverages Digital Twins for real-time system monitoring and integrates Large Language Models to support context-aware decision-making under multi-objective constraints. The proposed approach dynamically optimizes latency, bandwidth utilization, and energy consumption, enabling intelligent workload orchestration across heterogeneous data center environments. A flexible utility function is introduced to allow system operators to adjust trade-offs between responsiveness and environmental impact. Experimental results demonstrate that the framework consistently outperforms traditional heuristic and learning-based baselines, achieving higher allocation accuracy, improved network utilization, and faster workload completion, while reducing overall energy consumption by more than 20% in sustainability-oriented scenarios. These findings highlight the potential of combining digital twins-driven observability with large language model-based reasoning to enable interpretable, adaptive, and energy-efficient resource management in next-generation cloud computing environments.
Pedro Henrique Sachete Garcia, A. Lorenzon, M. Luizelli et al.· SN Computer Science· 0 citations