Journal of Operating Systems Development & Trends
Volume 13, Issue 2 (2026)
Table of contents
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Comparative Analysis of Classical, Metaheuristic, and Reinforcement Learning Approaches to Load Balancing in Cloud Computing
Abstract: Efficient load balancing remains one of the most critical challenges in cloud computing, particularly as modern cloud environments rely on heterogeneous virtual machines (VMs) with varying computational capabilities. The effective distribution of workloads across these resources directly influences system performance, energy efficiency, and service reliability. This paper presents a comparative evaluation of three major categories of load-balancing approaches: traditional scheduling heuristics, nature-inspired metaheuristic algorithms, and reinforcement learning (RL)-based techniques. The …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 1–6 Read article
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Next-Generation OS Architectures for Low-Power Smart Hardware: A Comprehensive Review
Abstract: The ubiquity of smart devices has dramatically transformed how humans engage with technology. Devices with high characteristics do use standard operating systems like Linux, but those with low characteristics cannot do so because of extreme re-source constraints. The survey also offers a range of other results and analysis applicable to existing low-end operating systems as well (along with architectural highlights and constraints as applicable). A field survey of technical institutes …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 15–27 Read article
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AI-PASS: An AI-Based Predictive, Adaptive and Energy Aware Task Scheduling Framework for Heterogeneous Parallel Computing
Abstract: Heterogeneous parallel computing systems are made up of a combination of multicore CPUs, GPUs, and specialized devices to boost computational power and utilize resources. But changes in workload intensity and processor capability, energy consumption and system conditions are dynamic, making efficient task scheduling difficult. Traditional scheduling approaches are based primarily on task properties or the state of the resources employed and can be sub-optimal when workloads are evolving. This paper …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 28–34 Read article
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Mirror-Symmetric NoC Topologies for 64-Core Arrays: Investigating Latency and Energy Trade-offs versus Mesh and Torus Interconnects for Block-Level Matrix Multiplication Workloads
Abstract: Modern chip multiprocessors increasingly rely on many-core arrays to accelerate dense linear algebra kernels such as block-level general matrix multiplication (GEMM), where inter-tile communication often rivals computation cost. Network-on-Chip (NoC) topology therefore strongly influences overall latency and energy efficiency. This work proposes a mirror-symmetric NoC topology for 64-core arrays, in which long-range bypass links are added about a central reflective axis to shorten paths between symmetric core partitions that frequently …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 35–44 Read article
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AI-Driven Malware Detection in Linux: A Survey of Techniques, Challenges, and Future Directions
Abstract: With the rapid growth of Linux-based systems across cloud computing, Internet of Things (IoT) devices, servers, and embedded platforms, Linux environments have increasingly become attractive targets for sophisticated malware attacks. This survey provides a comprehensive review of Artificial Intelligence (AI)-based approaches for detecting Linux malware, focusing on research published between 2022 and 2026. More than 40 different detection methodologies are examined, covering both traditional Machine Learning (ML) techniques, such as …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 7–14 Read article