Traditional Distributed Hash Table Research Articles

Data Location Management Protocol for Object Stores in a Fog Computing Infrastructure

Fog computing infrastructures have been proposed as an alternative to cloud computing to provide low latency computing for the Internet of Things (IoT). But no storage solutions have been proposed to work specifically in this environment. Existing solutions, relying on a distributed Hash Table to locate the data, are not efficient because location record may be placed far away from the object replicas. In this paper, we propose to use a tree-based approach to locate the data, inspired by the domain name system (DNS) protocol. In our protocol, servers look for the location of an object by requesting successively their ancestors in a tree built with a modified version of the Dijkstra’s algorithm applied to the physical topology. Location records are replicated close to the object replicas to limit the network traffic when requesting an object. We evaluate our approach on the Grid’5000 testbed using micro experiments with simple network topologies and a macro experiment using the topology of the French National Research and Education Network (RENATER). In this macro benchmark, we show that the time to locate an object in our approach is less than 15 ms on average which is around 20% shorter than using a traditional distributed Hash Table (DHT).

A New Algorithm of Service Discovery Based on DHT for Mobile Application

In order to solve how to enhance the discovery efficiency and coverage, based on DHT (Distributed Hash Table) and Small World Theory, we put forward a new algorithm of service discovery for mobile application. In traditional DHT discovery algorithm, each node maintains the finger-table that store node information of adjacent node. By using Small-World Theory, we put forward adding a remote node into the finger-table and adding the corresponding remote index. It is different from selecting the remote connection node randomly. We select the remote connection node by calculating local node and it can assure not only the cove range of service discovery but also not increase the length of finger-table, which simplifies the calculation of the finger-table and maintenance work. The simulation proved that the algorithm can reduce the path length of service discovery effectively, improve success rate of service discovery

A Read-Only Distributed Hash Table

A distributed hash table (DHT) is an infrastructure to support resource discovery in large distributed systems. In a DHT, data items such as resources, indexes of resources or resource metadata, are distributed across an overlay network based on a hash function. However, this may not be desirable in commercial applications such as Grid and cloud computing whereby the presence of multiple administrative domains leads to the issues of data ownership and self-economic interests. In this paper, we present R-DHT (Read-only DHT), a DHT-based resource discovery scheme without distributing data items. To map each data item back onto its resource owner, a physical host, we virtualize each host into virtual nodes. Nodes are further organized as a segment-based overlay network which increases node failure resiliency without replicating data items. We demonstrate the feasibility of our proposed scheme by presenting R-Chord, an implementation of R-DHT using Chord as the underlying overlay graph, with lookup and maintenance optimizations. Through analytical and simulation analyses, we evaluate the performance of R-DHT and compare it with traditional DHTs in terms of lookup path length, resiliency to node failures, and maintenance overhead. Overall, we found that R-DHT is effective and efficient for resource indexing and discovery in large distributed systems with a strong commercial requirement.