A Robust Fault-Tolerant and Scalable Cluster-wide Deduplication for Shared-Nothing Storage Systems

Deduplication has been largely employed in distributed storage systems to improve space efficiency. Traditional deduplication research ignores the design specifications of shared-nothing distributed storage systems such as no central metadata bottleneck, scalability, and storage rebalancing. Further, deduplication introduces transactional changes, which are prone to errors in the event of a system failure, resulting in inconsistencies in data and deduplication metadata. In this paper, we propose a robust, fault-tolerant and scalable cluster-wide deduplication that can eliminate duplicate copies across the cluster. We design a distributed deduplication metadata shard which guarantees performance scalability while preserving the design constraints of shared- nothing storage systems. The placement of chunks and deduplication metadata is made cluster-wide based on the content fingerprint of chunks. To ensure transactional consistency and garbage identification, we employ a flag-based asynchronous consistency mechanism. We implement the proposed deduplication on Ceph. The evaluation shows high disk-space savings with minimal performance degradation as well as high robustness in the event of sudden server failure.Comment: 6 Pages including reference

ParSCL: A Parallel and Distributed Framework to Process All Nearest Neighbor Queries on a Road Network

The proliferation of current and next-generation mobile and sensing devices has increased at an alarming rate. With these state-of-the-art devices, the global positioning system (GPS) has made remote sensing and location tracking more viable. One such query is the All Nearest Neighbor (ANN) query, which extracts and returns all data objects that are in close vicinity to all query objects. An ANN is a combination of $k$ -nearest neighbors (kNN), and join queries. Hence, ANN has useful for applications in different domains such as transportation optimization, locating safe zones, and ride-sharing. An example of its applications is, “find the nearest gas station for each car parking lot”. Because these applications are responsible for generating a massive number of query requests, a large amount of computation is required to return these query requests. As a single machine cannot meet this demand in this study, we propose a distributed query processing framework to process ANN queries using the Apache Spark framework. In an empirical study, our proposed framework achieved superior query efficiency and scalability compared to other methods and design alternatives