A Review of Reliable and Efficient Task Offloading Strategies on Fog Computing Model for IOT Based Applications

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.

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

Due to the rapid growth of IoT and smart city applications the need for low-latency efficient service provisioning in distributed systems has grown substantially. Conventional cloud-centric architectures which rely on centralized processing tend to introduce significant latency that makes them ill-suited for real-time IoT workloads. This work addresses the challenge of service placement and resource allocation for IoT applications operating across multi cloud and fog computing infrastructures. Achieving satisfactory Quality of Service (QoS) requires simultaneous consideration of latency, bandwidth and resource utilization. Current single cloud and statically configured deployment strategies struggle with scalability and responsiveness in dynamic IoT scenarios. There is a clear need for adaptive intelligent frameworks capable of handling fluctuating workloads and heterogeneous resource availability. To address task placement, this work introduces a lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time. Fog-layer task scheduling is handled through an enhanced weighted fair queuing (EWFQ) mechanism that incorporates user-defined priorities and live feedback signals. A weighted Q-learning algorithm (WQLA) is further introduced to refine placement decisions by learning from interactions with the deployment environment across multi-cloud and fog nodes. Simulation results confirm that the proposed approach yields reduced latency and more consistent wait times relative to heuristic and genetic baselines. Energy efficiency and service availability are also sustained under varying load conditions. The combined, adaptive framework delivers a practical and scalable method for IoT service provisioning in multi-cloud environment, advancing the groundwork for future work in context-sensitive, secure, and scalable resource management.

Hardware-based Efficient Task Offloading in IoT-Fog-Cloud

The rapid expansion of the Internet of Things (IoT) has led to an exponential increase in data volume, creating challenges for efficient data processing and latency control. Traditional cloud-based systems often experience excessive latency, making them less suitable for real-time applications. This research paper proposes a hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture. An ESP8266-based IoT device senses real-time temperature data and transmits it via the MQTT protocol. The fog layer is implemented using Node-RED, which performs real-time data processing and decision-making, and generates alerts based on predefined temperature thresholds. This minimizes the dependency on the cloud for immediate responses. The processed data is then offloaded to the cloud layer using InfluxDB for data storage and Grafana for visualization and analysis. To measure task offloading performance, the proposed system includes a latency comparison between fog-layer processing time and cloud-layer response time. Experimental results demonstrate a significant reduction in response latency at the fog layer (avg. 103 ms) compared to cloud-layer response time (avg. 271 ms), representing approximately 62% lower latency.

Task Scheduling in the Fog to Cloud Continuum for IoT Services: A Taxonomy and Structured Synthesis of Distributed Resource Management

Task scheduling has often been treated as a secondary concern in fog computing, something to address only after the architecture is defined. This review argues that it is instead the central runtime decision in the fog to cloud continuum, because it determines whether the promised gains in latency, energy efficiency, and reliability can actually be achieved. The study synthesizes 102 foundational, methodological, and technical sources on task scheduling in fog enabled IoT environments. The aim was not simply to catalogue algorithms, but to examine how the field has framed the scheduling problem and how that framing has changed over time. The evidence reveals a clear progression. Early studies commonly assumed stable resources, predictable workloads, and simplified network conditions, which made scheduling easier to model but less representative of real deployments. More recent work has relaxed these assumptions and introduced dynamic, multi objective, application aware, learning based, and deployment oriented approaches. Six research streams emerge from this evolution. The main finding is that algorithmic sophistication has advanced faster than evaluation practice. Reported improvements in latency, energy consumption, and other QoS metrics are often difficult to compare because studies use different workloads, simulators, baselines, and experimental assumptions. Scheduling and orchestration overhead is rarely measured, while physical testbed validation remains limited. These gaps directly affect confidence in whether a proposed scheduler would behave as expected in operational fog systems. The review therefore identifies several priorities for future work: standardized benchmark workloads, cloud native scheduling that accounts for container lifecycle and microservice dependencies, resilience aware scheduling that treats failures and migration as first class concerns, and carbon aware orchestration that extends beyond energy minimization. Beyond the taxonomy, the paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems.

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

A Comprehensive Review of Offloading Algorithms in Replicated Fog Computing Environments

This paper provides an in-depth review of computation offloading algorithms within replicated fog computing environments, a critical area for modern distributed systems. It delineates the foundational concepts of fog computing and data replication, exploring their architectures, characteristics, and inherent challenges. The paper then systematically examines various offloading mechanisms and objectives, categorizing state-of-the-art algorithms, including optimization-based, machine learning-driven, and dynamic resource management approaches. A significant focus is placed on the complex interplay between offloading and replication, particularly concerning data consistency, fault tolerance, resource heterogeneity, and security. Through an analysis of real-world applications and identified research gaps, this review aims to serve as a foundational resource for PhD researchers, guiding future investigations into more robust, efficient, and intelligent offloading strategies in highly dynamic and resource-constrained fog environments.