E2DLB: An Energy Efficient Deadline Aware Load Balancing Algorithm for Fog and Edge Computing

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.

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

A two-layer optimized architecture for energy- and QoS-aware fog computing in distributed IoT ecosystems

Findings confirm that system stability and service quality are bounded by fog density, QoS-aware routing, and real-time load regulation, rather than by mere resource scaling.

Lightweight IoT Node Offloading Framework for Real-Time Edge Analytics

The rapid growth of Internet of Things (IoT) deployments has intensified the need for efficient, decentralized computation management at the network edge. This paper presents a lightweight, neighbor-aware one-hop task offloading framework designed for resource-constrained IoT networks. The proposed adaptive scheme combines Exponential Weighted Moving Average (EWMA) load estimation with a queue-depth gate to prevent unnecessary offloading under transient load spikes, and an assignment-pressure mechanism to distribute tasks more evenly across neighboring nodes. We evaluate the framework using a custom-developed discrete-event simulator on a 90-node ringplus-chord topology with heterogeneous hotspot and light nodes, comparing against three baselines: local-only execution, random offloading, and least-loaded neighbor selection. Results show that a load-aware but pressure-unaware least-loaded strategy surprisingly produces the highest load variance (377.25), worse than random offloading (114.84), due to severe task funneling toward persistently fast nodes. The proposed scheme eliminates task drops entirely, achieves an average latency of 148.7 ms, and reduces task-count variance to 44.33 - an $8.5 \times$ improvement over the least-loaded baseline and 4.6× over local-only execution - while requiring only 28.21% of tasks to be offloaded. These results demonstrate that assignment-pressure tracking is essential for fair load distribution in energy-limited IoT deployments.

Energy and Fairness-Aware Task Offloading Using Genetic Algorithm in Multi-Server Mobile Edge Computing

An energy-and fairness-aware task offloading (EFATO) scheme through a GA to achieve the optimal task scheduling and loading within multiple edge servers and a novel fitness function is proposed that combines energy cost, computational delay and fairness index.

Internet of Things-Centric Optimized Service Provisioning in Multi-Cloud Environment

A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.