Jul 2026· Asian Journal of Research in Computer Science· 0 citations
TL;DR
The paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems, and 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.
Abstract
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 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.
Anshul Atre, K. Singh, B. Chaurasia et al.· Journal of Circuits, Systems...· 0 citations
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.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
The Internet of Things (IoT) has grown rapidly in recent years, enabling the interconnection of a large number of heterogeneous and distributed devices. This number is expected to exceed 70 billion according to Statista. With this massive scale, fulfilling complex IoT applications that require combinations of multiple objects remains a real challenge. Moreover, several Quality of Service (QoS) requirements must be satisfied, making the problem of selecting appropriate IoT services NP-hard. In such environments, task offloading is a key mechanism to efficiently distribute computational workloads across edge, fog, and cloud resources. However, selecting the optimal offloading decision remains a difficult NP-hard problem due to system heterogeneity and conflicting objectives. In this paper, we propose a GNN-DQN-based approach for task offloading in edge–fog–cloud environments. Unlike prior GNN-DQN approaches limited to single- or dual-tier architectures, our framework explicitly models heterogeneous node types and inter-tier communication links, enabling more balanced and scalable resource allocation. Experimental results show that GNN-DQN achieves a mean latency of 2.64 s, representing improvements of 70.2% over Random, 7.8% over DQN-only, and 3.5% over Greedy. A GNN-A2C baseline is also included to broaden the comparison with a modern DRL method. Despite sharing the same GNN encoder, it underperforms GNN-DQN across all metrics, confirming the superiority of the DQN learning backbone. These results highlight the effectiveness of integrating graph-based representation with reinforcement learning, while also revealing a trade-off between latency optimization and energy efficiency.
Sirine Hakim, Sonia Yassa· International Conference on...· 0 citations
The advent of 6G networks and the rapid growth of Internet of Things (IoT) are revolutionizing the telecommunication sector, integrating edge-cloud systems with vast amounts of data from IoT and AI techniques. These advancements make these systems essential in managing and delivering a multitude of services, offered by smart cities and industrial domains, being just a few among the many possible application scenarios. Specifically, this shift introduces complexities in orchestrating services and managing available resources while addressing challenges such as reducing latency, growing bandwidth, ensuring trust, and integrating different technologies. In this sense, this review explores the recent approaches of the state of the art focused on service orchestration in IoT edge-cloud environments, concentrating on architectures and methodologies that enable resource allocation and service management. Furthermore, it examines platforms for orchestration development, highlighting their characteristics and contributions. Finally, emerging concerns such as network topology and trust, which previous surveys have often overlooked, are discussed together with the most relevant research directions.
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions.
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations
The key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum.
K. Aghababaiyan, B. Coll-Perales, Javier Gozálvez· 0 citations