Browsing by Author "KangJeong, Hun"

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    i-Code: A New Approach to Practical Network Coding for Content Distribution
    (2011-02-18) KangJeong, Hun; Yun, Aaram; Vasserman, Eugene Y.; Kim, Yongdae
    This paper studies the practicality of network coding to facilitate cooperative content distribution. Network coding is a new data transmission technique which allows any nodes in a network to encode and distribute data. It is a good solution offering reliability and efficiency in distributing content, but network coding has not been widely used because of its dubious performance gains and coding overhead in practice. With the implementation of network coding in a real-world application, this paper measures the performance and overhead of network coding for content distribution in practice. This study also provides a lightweight yet efficient encoding scheme which allows network coding to provide improved performance and robustness with negligible overhead.
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    Why Kad Lookup Fails
    (2009-06-26) KangJeong, Hun; Chan-tin, Eric D.; Hopper, Nicholas J.; Kim, Yongdae
    A Distributed Hash Table (DHT) is a structured overlay network service that provides a decentralized lookup for mapping objects to locations. In this paper, we study the lookup performance of locating nodes responsible for replicated information in Kad - one of the largest DHT networks existing currently. Throughout the measurement study, we found that Kad lookups locate only 18% of nodes storing replicated data. This failure leads to limited reliability and an inefficient use of resources during lookups. Ironically, we found that this poor performance is due to the high level of routing table similarity, despite the relatively high churn rate in the network. This similarity results in duplicated responses from many peers en route to a target, which effectively limits the number of unique nodes found - hence the nodes responsible for storing replicated data are not located. We propose solutions which either exploit the high routing table similarity or avoid the duplicate returns using multiple target keys. Our solutions can locate more than 80% of nodes storing the replicated information while simultaneously balancing the lookup load.