This paper studies the problem of querying Bounded Spatial Datasets (BSDs). A BSD contains i) objects with known locations, and ii) unknown regions, each of which bounds an unknown number of objects, within a coverage area. We consider applications where each BSD is hosted on a server or site connected to a communication network and the BSDs overlap in their coverage areas. The challenge is to query the distributed BSDs to retrieve all objects and to minimize the unknown regions which may contain objects satisfying the query, while minimizing the data transmission volume and number of interactions between the query client and the sites. We develop query processing algorithms for two important types of spatial queries, namely, range and k-nearest-neighbor (kNN) queries. We develop the site-based approach and the area-based approach for efficiently processing range and kNN queries on distributed BSDs. They aim to process only a subset of the sites to obtain the full answer for a query. Thus, optimal site selection and the corresponding site querying methods are important problems studied in this paper. In the area-based approach, we prove an optimal division and derive a practical heuristic to partition a query and select the best processing site for each partition, hence achieving even better efficiency than the site-based approach. Simulation results based on three real spatial datasets show that our proposed approaches significantly outperform the baseline that uses a centralized approach in terms of data transmission volume and the number of interactions between the query client and the distributed sites.

We propose a novel collaborative caching framework to support spatial query processing in Mobile Peer-to-Peer Networks (MP2PNs). To maximize cache sharing among clients, each client caches not only data objects but also parts of the index structure built on the spatial objects. Thus, we call the proposed method structure-embedded collaborative caching (SECC). By introducing a novel index structure called Signature Augment Tree (SAT), we address two crucial issues in SECC. First, we propose a cost-efficient collaborative query processing method in MP2PNs, including peer selection and result merge from multiple peers. Second, we develop a novel collaborative cache replacement policy which maximizes cache effectiveness by considering not only the peer itself but also its neighbors. We implement two SECC schemes, namely, the periodical and adaptive SAT-based schemes, with different SAT maintenance policies. Simulation results show that our SECC schemes significantly outperform other collaborative caching methods which are based on existing spatial caching schemes in a number of metrics, including traffic volume, query latency and power consumption.

The increasing complexity of modern computer systems makes fault detection and localization prohibitively expensive, and therefore fast recovery from failures is becoming more and more important. A significant fraction of failures can be cured by executing specific repair actions, e.g. rebooting, even when the exact root causes are unknown. However, designing reasonable recovery policies to effectively schedule potential repair actions could be difficult and error prone. In this paper, we present a novel approach to automate recovery policy generation with reinforcement learning techniques. Based on the recovery history of the original user-defined policy, our method can learn a new, locally optimal policy that outperforms the original one. In our experimental work on data from a real cluster environment, we found that the automatically generated policy can save 10% of machine downtime.