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Cache replacement strategy is the core for a distributed high-speed caching system, and effects the cache hit rateand utilization of a limited cache space directly. Many reports show that there are temporal and spatial localchanges in access patterns of geospatial data, and there are popular hot spots which change over time. Therefore,the key issue for cache replacement strategy for geospatial data is to get a combination method which considersboth temporal local changes and spatial local changes in access patterns, and balance the relationship between thechanges. And the cache replacement strategy should fit the distribution and changes of hotspot. This paperproposes a cache replacement strategy based on access pattern which have access spatiotemporal localities. Firstly,the strategy builds a method to express the access frequency and the time interval for geospatial data access basedon a least-recently-used replacement (LRU) algorithm and its data structure; secondly, considering both the spatialcorrelation between geospatial data access and the caching location for geospatial data, it builds access sequencesbased on a LRU stack, which reflect the spatiotemporal locality changes in access pattern. Finally, for achievingthe aim of balancing the temporal locality and spatial locality changes in access patterns, the strategy chooses thereplacement objects based on the length of access sequences and the cost of caching resource consumption.Experimental results reveal that the proposed cache replacement strategy is able to improve the cache hit rate whileachieving a good response performance and higher system throughput. Therefore, it can be applied to handle theintensity of networked GISs data access requests in a cloud-based environment

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A replacement strategy for a distributed caching system based on the spatiotemporal access pattern of geospatial data