Migration-Driven Energy Consumption Optimisation Atop Cloud Continuum

OPTIMIZED TASK SCHEDULING IN FOG-CLOUD ENVIRONMENTS USING A COST-AWARE GENETIC ALGORITHM

Energy-Aware Digital Twin Allocation with LLMs for Adaptive Resource Management in Cloud-Native Infrastructures

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.

Optimized Energy Efficient Resource Management in Cloud Datacenters using GreenCloud Sim

The current invention outlines a Java-driven framework for efficient resource management in cloud data centres using the CloudSim simulation environment. The framework presents a predictive auto-scaling mechanism that examines historical traffic patterns to forecast future workload requirements, allowing for predictive Virtual Machine (VM) al- location rather than traditional fixed threshold-based techniques. Prior to VM migration, the system assesses a Service Level Agreement (SLA) risk factor to avoid performance degradation and potential SLA violations through intelligent power management. A specific Green Scheduler Algorithm dynamically consolidates Virtual Machines by allocating workloads to optimally loaded physical machines based on fore- casted workload conditions. Machines with low utilization are automatically migrated across different power-saving states, such as idle, sleep, and deep sleep modes. This comprehensive framework strikes a balance between energy savings and the preservation of service reliability and Quality of Service (QoS). Simulation results demonstrate the effectiveness of energy savings, improved resource utilization, and SLA compliance, making it suitable for scalable and ecofriendly cloud resource management.

A Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing in Cloud Systems

Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.

Evaluating Dynamic and Energy-Efficient Task Offloading Mechanisms in Heterogeneous Fog Computing Systems

The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded. This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay. Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.

Emulating the SDN-Enabled Computing Continuum through Lightweight Virtualization

This work introduces an emulation framework that allows developers and operators to decide how to deploy networks, computing devices, and applications in a Computing Continuum environment, ensuring compliance with established Quality of Service standards.