Showing 1 - 20 results of 162 for search 'computing (source OR sources) allocation', query time: 0.11s Refine Results
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    A Development Model for Identifying the Uncertainty Sources and Their Impacts on Bridge Construction Projects by Kayvan Mohammadi Atashgah, Rouzbeh Ghousi, Armin Monir Abbasi, Abbasali Tayefi Nasrabadi

    Published 2023-03-01
    “…This study proposes a SAHP based on group decision making (GSAHP) to prioritise the sources of uncertainty in bridge construction projects. …”
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    Speculation resource provisioning in high-performance computing by Leena Sri, Balaji Narayanan

    Published 2017-01-01
    “…As an attempt to quantify resource allocation we use two level adaptive prediction mechanism to check the computational patterns of past resource allocation to the future requirement. …”
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    Analysis of the Impact of Renewable Energy Sources and Energy Storage Systems on Multi-Stage Transmission Network Expansion Planning by Mario D. Pastrana, Juan Carlos Cortez, Juan Camilo Lopez, Marina Lavorato, Marcos J. Rider

    Published 2025-01-01
    “…This paper presents a mathematical model to solve the multi-stage transmission network expansion planning (MTNEP) problem considering renewable energy sources (RES) and the allocation of energy storage systems (ESSs). …”
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    Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

    Published 2025-01-01
    “…The research addresses these challenges by developing a green, energy-aware video transcoding system that predicts energy availability from renewable sources (solar and wind) using machine learning techniques and optimizes tasks allocation. …”
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    Deep reinforced traffic-aware CPU allocation in centralized RAN by Sanguk Jeong, Syed M. Raza, Huigyu Yang, Sukjae Lee, Min Young Chung, Moonseong Kim, Hyunseung Choo

    Published 2025-07-01
    “…The impact of DRCA scheme is evaluated using network environment and state indicators from an industry-grade simulator and an open-source dataset. The results showcase 30% increase in packet processing throughput of BBU and up to 18% improved radio resource utilization achieved by the TA-DRCA on simulated datasets compared to the conventional static CPU allocation, highlighting the efficacy of DRCA framework in future cellular networks.…”
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