Expander Chunked Codes

Chunked codes are efficient random linear network coding (RLNC) schemes with low computational cost, where the input packets are encoded into small chunks (i.e., subsets of the coded packets). During the network transmission, RLNC is performed within each chunk. In this paper, we first introduce a simple transfer matrix model to characterize the transmission of chunks, and derive some basic properties of the model to facilitate the performance analysis. We then focus on the design of overlapped chunked codes, a class of chunked codes whose chunks are non-disjoint subsets of input packets, which are of special interest since they can be encoded with negligible computational cost and in a causal fashion. We propose expander chunked (EC) codes, the first class of overlapped chunked codes that have an analyzable performance,where the construction of the chunks makes use of regular graphs. Numerical and simulation results show that in some practical settings, EC codes can achieve rates within 91 to 97 percent of the optimum and outperform the state-of-the-art overlapped chunked codes significantly.Comment: 26 pages, 3 figures, submitted for journal publicatio

Efficient and accurate greedy search methods for mining functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.</p> <p>Methods</p> <p>In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.</p> <p>Results</p> <p>The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.</p> <p>Conclusions</p> <p>Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.</p

An Approximate Truthfulness Motivated Spectrum Auction for Dynamic Spectrum Access

Abstract—Secondary Spectrum Auction (SSA) has been proposed as an effective approach to design spectrum sharing mechanism for dynamic spectrum access. However, due to the location-constrained spectrum interference among users, it is a great challenge to provide truthful auction with maximized spectrum utilization. Most previous SSA designs either fail in addressing truthfulness or cause loss on spectrum utilization. In this paper, we focus on providing truthful SSA with maximized spectrum utilization. In order to minimize the computational overhead involved in addressing location-constrained interference, we leverage the truthfulness by introducing approximate truthfulness. Moreover, we define a general spectrum auction model using linear programming. Based on this model, we further propose ETEX, a sealed-bid auction mechanism with approximate truthfulness. Theoretical analysis confirms that ETEX is able to achieve truthfulness in expectation with polynomial complexity. Extensive experimental results show that ETEX outperforms most popular truthful spectrum auctions in terms of social welfare, spectrum utilization and user satisfaction. I

Offloading Performance Evaluation for Network Coding-based Cooperative Mobile Video Streaming

International audienc

Implementing Cooperative Caching in Distributed Streaming Media Server Clusters

Abstract. In distributed streaming media server clusters, by adopting cooperative caching (CC) technique, the free memory of all the servers can be combined to form a bigger, logically integral cooperative cache. It will help raise the hit rate of the cache and reduce disk accesses, resulting in the improvement of the overall throughput of server systems. Traditional CC and streaming buffer replacement algorithms are not quite suitable for streaming server clusters. This paper proposes a new cooperative caching strategy for them, called GLNU (Globally Longest-Not-to-be-Used). It is based on server level cooperation and fully takes the streaming media’s continuous playback requirement into consideration. Compared with traditional CC algorithms and cache algorithms for continuous media, this algorithm is more pertinent and suitable to the distributed streaming server cluster systems. Simulation results show that GLNU has better performance than several other traditional cache algorithms in various conditions