Quantifying Computational Reliability of Task Assignment in Extreme Edge Computing

iFogSim-Placement: A Simulation Framework for Edge Service Placement

Service placement in edge computing is a complex multi-objective optimisation problem defined by conflicting requirements and dynamic environments. While self-adaptive approaches are essential for handling this dynamism, the community needs specific frameworks to evaluate them. We introduce iFogSim-placement, a framework designed for experimenting with self-adaptive service placement. Unlike existing simulators, iFogSim-placement decouples decision-making from execution for modelling information staleness, mobility, and stochastic demand. The artefact enables implementing self-adaptive approaches as Java plug-ins and evaluating them under heterogeneous configurations. Researchers can define experiments to evaluate self-adaptive approaches under variable configurations, including network topology, application complexity, request patterns, and mobility scenarios. We present the architecture, usage instructions, and validation of the artefact, demonstrating its ability to capture performance metrics, including application latency, resource usage, and energy consumption. The artefact is open-source (https://github.com/DawnSpider96/iFogSim-Placement), aiming to lower the barrier to developing and fairly benchmarking self-adaptive edge systems.

Stable matching based efficient task offloading in heterogeneous edge environment

A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.

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.

Optimal and Energy Efficient Imprecise Computation Task Deployment on Heterogeneous Multicore Platforms Combining DVFS and DPM

A novel QoS-aware task deployment methodology to enhance the Quality of Service (QoS) under resource limitations is introduced and results demonstrate that the proposed method achieves superior system performance compared to existing approaches.

A survey on dependent task offloading in vehicular edge computing: models, algorithms, and challenges

An Adaptive Task Offloading Strategy for Edge Computing based on Dynamic Network State

Dynamic edge networks suffer from fluctuating bandwidth, latency, and edge-node load, which weaken conventional task offloading strategies under deadline and energy constraints. This study proposes an adaptive task offloading strategy based on dynamic network states. An online broad learning system predicts short-term bandwidth, transmission delay, and load from sliding-window observations. The predictions are embedded into a model predictive control framework for rolling optimization of the local-edge task allocation ratio, while confidence-interval-guided compensation adjusts decisions under prediction uncertainty. ADMM is used for distributed solution. NS-3/Python simulations show that the proposed method achieves a $93\%$ task success rate, 1.22 W average terminal energy consumption, 3.2 switches/s, and 45 ms average completion time, outperforming local execution, reactive offloading, and DQN-based offloading. The proposed strategy improves real-time reliability, energy efficiency, and robustness in highly dynamic edge computing environments.