scholarly journals An efficient Intelligent Cache Replacement Policy Suitable for PACS

2021 ◽  
Vol 11 (3) ◽  
pp. 250-255
Author(s):  
Yinyin Wang ◽  
◽  
Yuwang Yang ◽  
Qingguang Wang

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.

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 58073-58084 ◽  
Author(s):  
Yinyin Wang ◽  
Yuwang Yang ◽  
Chen Han ◽  
Lei Ye ◽  
Yaqi Ke ◽  
...  

2014 ◽  
Vol 41 (11) ◽  
pp. 871-877
Author(s):  
Cong Thuan Do ◽  
Dong Oh Son ◽  
Jong Myon Kim ◽  
Cheol Hong Kim

Author(s):  
Pratheeksha P ◽  
◽  
Revathi S A ◽  

Despite extensive developments in improving cache hit rates, designing an optimal cache replacement policy that mimics Belady’s algorithm still remains a challenging task. Existing standard static replacement policies does not adapt to the dynamic nature of memory access patterns, and the diversity of computer programs only exacerbates the problem. Several factors affect the design of a replacement policy such as hardware upgrades, memory overheads, memory access patterns, model latency, etc. The amalgamation of a fundamental concept like cache replacement with advanced machine learning algorithms provides surprising results and drives the development towards cost-effective solutions. In this paper, we review some of the machine-learning based cache replacement policies that outperformed the static heuristics.


2021 ◽  
Vol 2 (3) ◽  
pp. 1-24
Author(s):  
Chih-Kai Huang ◽  
Shan-Hsiang Shen

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache , which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.


2018 ◽  
Vol 15 (2) ◽  
pp. 20171099-20171099 ◽  
Author(s):  
Duk-Jun Bang ◽  
Min-Kwan Kee ◽  
Hong-Yeol Lim ◽  
Gi-Ho Park

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