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5 articles for “virtual machine allocation”
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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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An Optimized Memory Allocation and Deallocation Method while Programming in Python
Abstract: AbstractMemory management is memory allocation and deallocation for objects done automatically at run time in Python. Programmers need not allocate or deallocate memory explicitly. Python Virtual Machine (PVM) handles these activities. Every Python object is stored in its own way, that is, different objects are stored in different methods. Reference counting algorithm is used for memory management in Python.Keywords: Allocation, deallocation, PVM, reference countingCite this ArticleS.S. Sugantha Mallika, I. Mettildha …
Published in Journal of Operating Systems Development & Trends · Vol. 6, Issue 1, 2019 · pp. 1–6 Read article
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A Comprehensive Methodology for Data Security and Privacy in Cloud Computing Systems
Abstract: Cloud computing encompasses computing with a pool of virtualized computer resources. A cloud provides for self-recovering highly-scalable programming models, workloads to recover from hardware/software failures and rebalance allocations, can host different workloads and allows workloads to be deployed/scaled-out on-demand by rapid provisioning of virtual or physical machines. The idea is to move desktop computing to a service-oriented platform using server clusters and huge databases at data centers. Cloud computing has …
Published in Journal Of Network security · Vol. 2, Issue 2, 2014 · pp. 5–9 Read article
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DR. REVIVE: An AI-Powered Medical Recommendation System for Optimised Resources and Improved Patient Care
Abstract: Dr. Revive is an AI-powered medical recommendation system designed to enhance virtual healthcare interactions by connecting patients, doctors, and healthcare stakeholders. Leveraging advanced machine learning algorithms, it analyses user-reported symptoms to provide initial medical recommendations, serving as a reliable first point of guidance. With access to a comprehensive medical database, the platform delivers accurate and timely advice, empowering patients while supporting healthcare professionals with data-driven decision-making. By offering a complete …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
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Electronic Resources of University Libraries: AI-Driven Management and Optimization
Abstract: In order to maximize accessibility, utilization, and efficiency, university libraries’ growing reliance on electronic resources (ER) has prompted the creation of sophisticated management techniques. The difficulty of handling enormous volumes of data has increased as educational institutions move from conventional physical collections to massive digital repositories. A key instrument in revolutionizing library operations, artificial intelligence (AI) offers creative ways to improve retrieval methods, manage digital resources more effectively, and customize …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 3, 2025 · pp. 8–15 Read article