Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1029-1034· 0 citations· 19 references
Abstract
The rapid proliferation of Internet of Things (IoT) devices has intensified demands for low-latency, resource-efficient task scheduling at the network edge. Conventional policies such as Round-Robin and First-Come-First-Serve (FCFS) fail to satisfy the Quality-of-Service (QoS) requirements of Industrial-IoT and autonomous-vehicle workloads. This paper presents Multi-Queue Priority-Based Scheduling (MQPBS), a lightweight algorithm that classifies tasks into three dynamic priority queues (High, Medium, Low) using deadline-aware heuristics, applies Shortest-Job-First (SJF) intra-queue ordering, and employs an aging mechanism to prevent starvation. Extensive simulation over task sets of 200–1000 tasks demonstrates that MQPBS reduces average waiting time by up to 17.6%, improves throughput by up to 10.8%, lowers energy consumption by 20%, and cuts the Deadline Miss Ratio (DMR) compared with the Priority-Aware Task-Scheduling (PaTS) baseline. Ablation experiments confirm the independent contribution of each algorithmic component. Scalability and sensitivity analyses further validate the robustness of MQPBS under heterogeneous arrival patterns and varying load intensities. The results establish MQPBS as a scalable, reliable scheduler for next-generation edge infrastructures.
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, res...
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations
In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addres...
M. Yacoub, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
: Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud...
S. Vijaykumar, Shanker Chandre· Journal of Computer Science· 0 citations
The article presents the Average‐Based Load Balancing and Resource Allocation Mechanism (ALBRAM), which identifies suitable nodes and dynamically allocates resources to maintain the load balance among all the servers to provide fairness, optimal resource utilisation, and balanced workloads.
Ajay Nain, Rohit Malik, Sophiya Sheikh et al.· Concurrency and Computation· 0 citations
A dynamic resource allocation and task scheduling approach based on end-edge-cloud cooperation is established in order to enhance task completion, resource utilization, satisfaction of service level agreements (SLA) and reduce delay and energy consumption.
Shao-Meng Ren, Zheng-Jie He, Xiang-Yun Yi et al.· 電腦學刊· 0 citations
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