Correlation-Aware Replica Prefetching Strategy to Decrease Access Latency in Edge Cloud

With the number of connected devices increasing rapidly,the access latency issue increases drastically in the edge cloud environment.Massive low time-constrained and data-intensive mobile applications require efficient replication strategies to decrease retrieval time.However,the determination of replicas is not reasonable in many previous works,which incurs high response delay.To this end,a correlation-aware replica prefetching(CRP)strategy based on the file correlation principle is proposed,which can prefetch the files with high access probability.The key is to determine and obtain the implicit high-value files effectively,which has a significant impact on the performance of CRP.To achieve the goal of accelerating the acquisition of implicit highvalue files,an access rule management method based on consistent hashing is proposed,and then the storage and query mechanisms for access rules based on adjacency list storage structure are further presented.The theoretical analysis and simulation results corroborate that CRP shortens average response time over 4.8%,improves average hit ratio over 4.2%,reduces transmitting data amount over 8.3%,and maintains replication frequency at a reasonable level when compared to other schemes.

Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations.The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’rule.This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational cost.Furthermore,this enables reconstruction of the original data more accurately than existing methods.We demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid points.The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.