Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (5): 376-387.doi: 10.11896/jsjkx.250300140

• Computer Architecture • Previous Articles     Next Articles

Improved Hippopotamus Algorithm for Energy Efficiency Optimization of HeterogeneousIntelligent Storage Computing

WANG Enliang1,2, XIA Jun2,3, SUN Zhixin2,3   

  1. 1 School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    2 Postal Industry Technology Research and Development Center of National Post Bureau(Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    3 Post Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Received:2025-03-26 Revised:2025-06-16 Published:2026-05-08
  • About author:WANG Enliang,born in 1998,Ph.D candidate.His main research interests include high performance computing,optimization algorithms and deep lear-ning.
    SUN Zhixin,born in 1964,Ph.D,professor,doctoral supervisor.His main research interests include the theory and technology of network communication,computer network and security.
  • Supported by:
    National Natural Science Foundation of China(61972208,62272239) and Jiangsu Agriculture Science and Technology Innovation Fund(JASTIF)(CX(22)1007).

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Abstract: The high energy consumption issue in data centers has become increasingly prominent amid digital transformation and the “dual carbon” strategy,stemming from energy efficiency bottlenecks caused by frequent data transmission in traditional distributed architectures.Existing optimization methods are confined to single dimensions(such as task scheduling or storage hierarchy optimization),struggling to collaboratively adapt to dynamic workloads and heterogeneous resource environments.This paper proposes an energy efficiency optimization framework for intelligent storage-computing systems based on the improved hippopo-tamus optimization algorithm(IHOA),which jointly optimizes task allocation and data placement through a multi-dimensional unified model encompassing computation,storage,and communication energy consumption.The framework innovatively introduces a storage-computing collaborative awareness mechanism that quantifies task-data correlations,combined with energy-efficiency-sensitive adaptive search strategies that dynamically adjust local and global search intensities to accommodate the energy efficiency characteristics of heterogeneous devices.Experimental results demonstrate that compared to mainstream optimization algorithms,IHOA significantly reduces total energy consumption in medium to large-scale systems,with efficiency improvements ranging from 8.1% to 25.6%,deriving its advantages from efficient suppression of remote data transmission energy consumption and dynamic adaptation to heterogeneous resources.Energy composition analysis further validates IHOA’s effectiveness in global collaborative optimization,achieving 17%~32% reduction in data transmission energy consumption by minimizing cross-node data migration.This research provides theoretical support and technical pathways for the green design of intelligent storage-computing systems,driving data centers toward high-efficiency,low-carbon development,while offering methodological references for energy efficiency optimization in emerging scenarios such as edge computing.

Key words: Intelligent storage and computing system, Energy efficiency optimization, Improved hippopotamus optimization algorithm, Heterogeneous computing, Sustainable computing

CLC Number: 

  • TP311.5
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