energy-aware scheduling
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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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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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Cloud-based Application Development and Optimization
Abstract: As cloud computing powers today’s applications, optimizing cloud-based development is crucial to achieve performance, cost effectiveness, and scalability. This research focuses on enhancing the design, deployment, and maintenance of cloud applications, tackling challenges in resource management, scalability, and resilience. We specifically explore dynamic resource allocation algorithms that use predictive analytics for auto-scaling based on workload variations, aiming to cut costs while preserving high performance. The study also investigates cross-cloud optimization …
Published in Journal of Open Source Developments · Vol. 12, Issue 1, 2025 · pp. 37–42 Read article