Abstract
An efficient intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the Support vector machine (SVM) with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called SVM-LRU. The SVM-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented SVM-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The SVM algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the SVM-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies like LRU, LFU, SIZE and GDS.
Highlights
The picture archiving and communication systems (PACS) is a computer application system dedicated to the process, storage and the transmission of medical images
This paper proposes an intelligent cache substitution strategy for PACS, which combines the Support vector machine (SVM) with the least recently used (LRU) to form the SVM-LRU intelligent cache replacement strategy
The SVM-LRU strategy oriented towards PACS obtains the eigenvalues in the medical process that belongs to the patients corresponding to the cached object through mining the medical data
Summary
The PACS is a computer application system dedicated to the process, storage and the transmission of medical images. As a cache medium, the SDD is of relatively high cost and limited storage capacity Another cache replacement strategy is needed, good enough to manage the hybrid storage. In PACS, the classic cache replacement strategy has a relatively poor performance, whereas these strategies only consider a certain influence of the cache object (such as size, final access time and frequency). Based on the analysis of the cache architecture of PACS and the features of both cache object features and users’ habits, this paper combines the SVM algorithm with the classic LRU cache replacement strategy to form the SVM-LRU strategy This strategy uses a relatively simple SVM algorithm to establish a model that includes influencing factors, features, and users’ habits of the cache object. High prediction accuracy with improved hit ratio and byte hit ratio performance
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