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CCDHRF: cooperative coevolution dynamic hierarchical replication framework in grid computing
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DOI：10.1007/s00607-025-01599-5.png)
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Abstract
En 中文
Large-scale distributed systems, such as grids and clouds, face significant challenges in efficiently managing and accessing vast amounts of data. File replication across multiple sites is a key strategy for improving data accessibility and reducing latency. Existing algorithms, such as Dynamic Hierarchical Replication (DHR), along with its enhanced variants, Dynamic Hierarchical Replication with Threshold (DHRT) and Replication with Dynamic Threshold (RDT), have several limitations: (1) they rely on a limited set of evaluation parameters—DHR and DHRT only use the number of requests, while RDT focuses solely on available storage, (2) they lack consideration for optimal local network and region selection, and (3) their sequential hierarchical structure results in inefficient, time-consuming searches at local and regional network levels. This paper presents the Cooperative Coevolution Dynamic Hierarchical Replication Framework (CCDHRF), a novel approach that overcomes these limitations by integrating the Cooperative Coevolutionary Genetic Algorithm (CCGA). CCDHRF enables parallel evaluation of multiple replica selection parameters across sites, local networks, and regions, resulting in a more adaptive, scalable, and precise replica placement strategy. Our results show that CCDHRF significantly outperforms DHR and its variants, with 15–17% reductions in mean job execution time and 13–14% improvements in network utilization, demonstrating its effectiveness in dynamic, large-scale distributed environments. The proposed method enhances replica selection precision and overall system efficiency, offering a robust solution to data replication challenges in cloud and grid computing.
Keywords:
Hierarchical structure
Data replication
Replica location selection
Optimization
Cooperative coevolutionary genetic algorithm
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