An optimal cache replacement policy for wireless data dissemination under cache consistency

This chapter introduces advanced client-side data-caching techniques to enhance the performance of mobile data access. The authors address three mobile caching issues. The first is the necessity of a cache replacement policy for realistic wireless data-broadcasting services. The authors present the Min-SAUD policy, which takes into account the cost of ensuring cache consistency before each cached item is used. Next, the authors discuss the caching issues for an emerging mobile data application, that is, location-dependent information services (LDISs). In particular, they consider data inconsistency caused by client movements and describe several location-dependent cache invalidation schemes. Then, as the spatial property of LDISs also brings new challenges for cache replacement policies, the authors present two novel cache replacement policies, called PA and PAID, for location-dependent data.

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On-Bound Selection Cache Replacement Policy for Wireless Data Access

IEEE Transactions on Computers ◽

10.1109/tc.2007.70768 ◽

2007 ◽

Vol 56 (12) ◽

pp. 1597-1611 ◽

Cited By ~ 12

Author(s):

Hui Chen ◽

Yang Xiao

Keyword(s):

Data Access ◽

Replacement Policy ◽

Cache Replacement ◽

Wireless Data ◽

Cache Replacement Policy

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Asymptotical keep-best Cache Replacement Policy for Wireless Data Access

10.1109/icc.2006.254760 ◽

2006 ◽

Cited By ~ 3

Author(s):

Hui Chen ◽

Yang Xiao

Keyword(s):

Data Access ◽

Replacement Policy ◽

Cache Replacement ◽

Wireless Data ◽

Cache Replacement Policy

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Enabling Service Cache in Edge Clouds

ACM Transactions on Internet of Things ◽

10.1145/3456564 ◽

2021 ◽

Vol 2 (3) ◽

pp. 1-24

Author(s):

Chih-Kai Huang ◽

Shan-Hsiang Shen

Keyword(s):

Virtual Machines ◽

Replacement Policy ◽

Cache Replacement ◽

Cache Replacement Policy ◽

Average Latency ◽

Hit Rates ◽

Iot Devices ◽

Multi Access ◽

Computational Resources ◽

The Cost

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.

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