An OLS regression model for context-aware tile prefetching in a web map cache

The increasing popularity of web map services has motivated the development of more scalable services in the spatial data infrastructures. Tiled map services have emerged as a scalable alternative to traditional map services. Instead of rendering map images on the fly, a collection of pre-generated image tiles can be served very fast from a server-side cache. However, during the start-up of the service, the cache is initially empty and users experience a poor quality of service. Tile prefetching attempts to improve hit rates by proactively fetching map images without waiting for client requests.

While most popular prefetching policies in traditional web caching consider only the previous access history to make predictions, significant improvements could be achieved in web mapping by taking into account the background geographic information.

This work proposes a regressive model to predict which areas are likely to be requested in the future based on spatial cross-correlation between an unconstrained catalog of geographic features and a record of past cache requests. Tiles that are anticipated to be most frequently requested can be pre-generated and cached for faster retrieval. Trace-driven simulations with several million cache requests from two different nation-wide public web map services in Spain demonstrate that accurate predictions and performance gains can be obtained with the proposed model.     

一种与地图服务结合的栅格瓦片计算模型

随着遥感影像数据的快速增长,对于栅格数据高效的信息处理和价值挖掘方式提出了更大的挑战,传统地图服务聚焦于内容的共享与可视化,缺乏对影像实时分析处理功能。本研究以地图服务的形式实现了对栅格瓦片数据实时分析处理能力,将云优化的GeoTIFF（Cloud Optimized GeoTIFF,COG）作为数据组织方式,设计了分布式协同预取策略,实现了栅格瓦片数据的冷热加载,优化了从云端读取影像数据的效率。在栅格瓦片数据高效加载的基础下,提出了一种基于表达式的栅格瓦片处理模型,通过对表达式转换建模为计算工作流,在地图服务的请求中实现对栅格瓦片的实时处理,对存储在云端的海量遥感数据进行快速分析,实现原始数据到数据产品的直接可视化转换。针对全量数据参与的场景,使用合适的重采样数据进行简化计算,以满足地图服务的实时性。使用了NDVI、地物分类、植被覆盖度三类不同复杂度模型,在地图服务中对Landsat 8影像进行了实时计算分析。实验结果表明,该处理模型能对栅格瓦片进行有效分析,且能进行分布式扩展,在高并发场景下能够提供稳定的地图服务能力,适应各层级尺度的计算,对未来地图服务的发展提供了一种新思路。     

Ensemble adaptive tile prefetching using fuzzy logic

Prefetching is a process in which the necessary portion of data is predicted and loaded into memory beforehand. The increasing usage of geographic data in different types of applications has motivated the development of different prefetching techniques. Each prefetching technique serves a specific type of application, such as two-dimensional geographic information systems or three-dimensional visualization, and each one is crafted for the corresponding navigation patterns. However, as the boundary between these application types blurs, these techniques become insufficient for hybrid applications (such as digital moving maps), which embody various capabilities and navigation patterns. Therefore, a set of techniques should be used in combination to handle different prefetching requirements. In this study, a priority-based tile prefetching approach is proposed, which enables the ensemble usage of various techniques at the same time. The proposed approach manages these techniques dynamically through a fuzzy-logic-based inference engine to increase prefetching performance and to adapt to various exhibited behaviours. This engine performs adaptive decisions about the advantages of each technique according to their individual accuracy and activity level using fuzzy logic to determine how each prefetching technique performs. The results obtained from the experiments showed that up to a 25% increase in prefetching performance is achieved with the proposed ensemble usage over individual usage. A generic model for prefetching techniques was also developed and used to describe the given approach. Finally, a cross-platform software framework with four different prefetching techniques was developed to let other users utilize the proposed approach.     

数字海洋云计算服务流中数据预部署研究

Data pre-deployment in the HDFS （Hadoop distributed file systems） is more complicated than that in traditional file systems. There are many key issues need to be addressed, such as determining the target location of the data prefetching, the amount of data to be prefetched, the balance between data prefetching services and normal data accesses. Aiming to solve these problems, we employ the characteristics of digital ocean information service flows and propose a deployment scheme which combines input data prefetching with output data oriented storage strategies. The method achieves the parallelism of data preparation and data processing, thereby massively reducing I/O time cost of digital ocean cloud computing platforms when processing multi-source information synergistic tasks. The experimental results show that the scheme has a higher degree of parallelism than traditional Hadoop mechanisms, shortens the waiting time of a running service node, and significantly reduces data access conflicts.