Abstract
Although several cloud storage systems have been proposed, most of them can provide highly efficient point queries only because of the key-value pairs storing mechanism. For these systems, satisfying complex multi-dimensional queries means scanning the whole dataset, which is inefficient. In this paper, we propose a multidimensional index framework, based on the Skip-list and Octree, which we refer to as Skip-Octree. Using a randomized skip list makes the hierarchical Octree structure easier to implement in a cloud storage system. To support the Skip-Octree, we also propose a series of index operation algorithms including range query algorithm, index maintenance algorithms, and dynamic index scaling algorithms. Through experimental evaluation, we show that the Skip-Octree index is feasible and efficient.
Highlights
Large-scale data management is a crucial aspect of most Internet applications
Most existing cloud storage systems generally adopt a distributed hash table (DHT) approach to index data, in which the data are organized in the form of key-value pairs [4]
Design of Skip-Octree Based on the randomizing idea of a skip list, the original dataset is randomly divided into subsets with a probability of 1/2
Summary
Large-scale data management is a crucial aspect of most Internet applications. Emerging cloud computing [1,2,3] systems can provide users with cheap and powerful facilities for storage. Cloud applications are required to deliver scalable and reliable management as well as process extensive data efficiently. Most existing cloud storage systems generally adopt a distributed hash table (DHT) approach to index data, in which the data are organized in the form of key-value pairs [4]. In location-based services, users often need to find an object based on its longitude, latitude, and time. They must query multiple attributes to return results immediately. For an Octree, an original data space is represented as a root node. Compressed Octree is used to index local data in this study. The compressed Octree is called Octree in our cloud index framework
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