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March 8, 2026SustainabilityOpen Access

Intelligent Hybrid Caching for Sustainable Big Data Processing: Leveraging NVM to Enable Green Digital Transformation

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Authors

LTLei TongQSQing ShenZXZihan Xie

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Overview

This framework integrates data placement and memory management to reduce energy consumption in big data environments, indicating improved sustainability in computing.

Key Points

  • To introduce a framework that optimizes data caching to reduce energy consumption and improve performance in big data processing.
  • Integrated Directed Acyclic Graph dependency analysis with garbage collection behavior monitoring
  • Dynamic prediction of data access patterns
  • Migration of cache blocks between DRAM and non-volatile memory
  • Achieved a 37.5% reduction in execution time compared to default Spark configurations
  • Improved throughput-per-watt
  • Benefited from NVM's near-zero idle power and extended hardware lifespan

Cite This Study

Tong et al. (2026) studied this question.

synapsesocial.com/papers/69acc58f32b0ef16a404fe32https://doi.org/10.3390/su18052601
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Agile-Ant: Self-Managing Distributed Cache Management for Cost Optimization of Big Data Applications2024
  2. 2Blaze: Holistic Caching for Iterative Data Processing2024 · 1 citations
  3. 3Hybrid-Memcached: A Novel Approach for Memcached Persistence Optimization With Hybrid Memory2024
  4. 4Exploration and optimization of novel replacement and prefetching strategies for inefficiencies of advanced MRAM-based hybrid cache systems2024 · 2 citations
  5. 5Assessing the Dynamics of Data Processing2024