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Manifold learning has been successfully applied to hyperspectral dimensionality reduction to embed nonlinear and nonconvex manifolds in the data. However, dimensionality reduction by manifold learning is sensitive to non-uniform data distribution and the selection of neighbors. To address the two issues to some extents, in this work a new manifold framework based on locality linear embedding (LLE),...
Continuously enriched distributed systems in data centers generate much network traffic in push-style one-to-many group mode, raising new requirements for multicast transport in terms of efficiency and robustness. Existing reliable multicast solutions, which suffer from low robustness and inefficiency in either host-side protocols or multicast routing, are not suitable for data centers. In order to...
Chunk-level deduplication plays an important role in backup storage systems. Existing Content-Defined Chunking (CDC) algorithms, while robust in finding suitable chunk boundaries, face the key challenges of (1) low chunking throughput that renders the chunking stage a serious deduplication performance bottleneck, (2) large chunk size variance that decreases deduplication efficiency, and (3) being...
Erasure codes are widely used in modern distributed storage systems to prevent data loss and server failures. Regenerating codes are a class of erasure codes that trades storage efficiency and computation for repair bandwidth reduction. However, their non-unified coding parameters and huge computation overhead prohibit their applications. Hence, we first propose a family of Functional Regenerating...
Range query in Peer-to-Peer networks based on Distributed Hash Table (DHT) is still an open problem. The traditional way uses order-preserving hashing functions to create value indexes that are placed and stored on the corresponding peers to support range query. The way, however, suffers from high index maintenance costs. To avoid the issue, a scalable blind search method over DHTs - recursive partition...
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