A Scalable Name Resolution System for Information Centric Networking

Information Centric Networking (ICN) is a new paradigm, aimed at shifting to the future Internet from host centric to a content centric approach. ICN focuses on retrieval and dissemination of information between pairwise communications of hosts. Information are organized in the form of Information Objects (IO), known as Named Data Objects (NDO). These NDO are location independent. Objects in ICN are stored in the system overlay; popularly known as Name Resolution System (NRS). NDOs are requested by the Subscribers in the network to get the needed information from the Publishers, through NRS. Thus, the NRS is responsible in forwarding the interest packets based on the names of NDOs. This application of ICN depends on the scalability of the NRS. To design NRS, the most significant issue is scalability due to the ever-increasing number of NDOs. This paper aims to present the issues, by proposing balanced binary tree data structure to organize and store the NDOs. The methodology proposed in this work is thus; for every new insertion in the tree, a Balance Factor (BF) is computed to balance the height of left and right sub-tree. According to our investigation, balanced binary tree provides less searching time when compared to the Distributed Hash Table (DHT) approach. Simulation results show that End-to-End delay decreases by increasing the throughput in the network

BATON: A Balanced Tree Structure for Peer-to-Peer Networks

We propose a balanced tree structure overlay on a peer-to-peer network capable of supporting both exact queries and range queries efficiently. In spite of the tree structure causing distinctions to be made between nodes at different levels in the tree, we show that the load at each node is approximately equal. In spite of the tree structure providing precisely one path between any pair of nodes, we show that sideways routing tables maintained at each node provide sufficient fault tolerance to permit efficient repair. Specifically, in a network with N nodes, we guarantee that both exact queries and range queries can be answered in O(logN) steps and also that update operations (to both data and network) have an amortized cost of O(logN). An experimental assessment validates the practicality of our proposal.Singapore-MIT Alliance (SMA

A scalable name resolution system for information centric networking

Information Centric Networking (ICN) is a new paradigm, aimed at shifting to the future Internet from host centric to a content centric approach. ICN focuses on retrieval and dissemination of information between pairwise communications of hosts.Information are organized in the form of Information Objects (IO), known as Named Data Objects (NDO). These NDO are location independent. Objects in ICN are stored in the system overlay; popularly known as Name Resolution System (NRS). NDOs are requested by the Subscribers in the network to get the needed information from the Publishers, through NRS.Thus, the NRS is responsible in forwarding the interest packets based on the names of NDOs.This application of ICN depends on the scalability of the NRS.To design NRS, the most significant issue is scalability due to the ever-increasing number of NDOs.This paper aims to present the issues, by proposing balanced binary tree data structure to organize and store the NDOs. The methodology proposed in this work is thus; for every new insertion in the tree, a Balance Factor (BF) is computed to balance the height of left and right sub-tree.According to our investigation, balanced binary tree provides less searching time when compared to the Distributed Hash Table (DHT) approach.Simulation results show that End-to-End delay decreases by increasing the throughput in the network

Yellow Tree: A Distributed Main-memory Spatial Index Structure for Moving Objects

Mobile devices equipped with wireless technologies to communicate and positioning systems to locate objects of interest are common place today, providing the impetus to develop location-aware applications. At the heart of location-aware applications are moving objects or objects that continuously change location over time, such as cars in transportation networks or pedestrians or postal packages. Location-aware applications tend to support the tracking of very large numbers of such moving objects as well as many users that are interested in finding out about the locations of other moving objects. Such location-aware applications rely on support from database management systems to model, store, and query moving object data. The management of moving object data exposes the limitations of traditional (spatial) database management systems as well as their index structures designed to keep track of objects\u27 locations. Spatial index structures that have been designed for geographic objects in the past primarily assume data are foremost of static nature (e.g., land parcels, road networks, or airport locations), thus requiring a limited amount of index structure updates and reorganization over a period of time. While handling moving objects however, there is an incumbent need for continuous reorganization of spatial index structures to remain up to date with constantly and rapidly changing object locations. This research addresses some of the key issues surrounding the efficient database management of moving objects whose location update rate to the database system varies from 1 to 30 minutes. Furthermore, we address the design of a highly scaleable and efficient spatial index structure to support location tracking and querying of large amounts of moving objects. We explore the possible architectural and the data structure level changes that are required to handle large numbers of moving objects. We focus specifically on the index structures that are needed to process spatial range queries and object-based queries on constantly changing moving object data. We argue for the case of main memory spatial index structures that dynamically adapt to continuously changing moving object data and concurrently answer spatial range queries efficiently. A proof-of concept implementation called the yellow tree, which is a distributed main-memory index structure, and a simulated environment to generate moving objects is demonstrated. Using experiments conducted on simulated moving object data, we conclude that a distributed main-memory based spatial index structure is required to handle dynamic location updates and efficiently answer spatial range queries on moving objects. Future work on enhancing the query processing performance of yellow tree is also discussed

Towards an Architecture for Efficient Distributed Search of Multimodal Information

The creation of very large-scale multimedia search engines, with more than one billion images and videos, is a pressing need of digital societies where data is generated by multiple connected devices. Distributing search indexes in cloud environments is the inevitable solution to deal with the increasing scale of image and video collections. The distribution of such indexes in this setting raises multiple challenges such as the even partitioning of data space, load balancing across index nodes and the fusion of the results computed over multiple nodes. The main question behind this thesis is how to reduce and distribute the multimedia retrieval computational complexity? This thesis studies the extension of sparse hash inverted indexing to distributed settings. The main goal is to ensure that indexes are uniformly distributed across computing nodes while keeping similar documents on the same nodes. Load balancing is performed at both node and index level, to guarantee that the retrieval process is not delayed by nodes that have to inspect larger subsets of the index. Multimodal search requires the combination of the search results from individual modalities and document features. This thesis studies rank fusion techniques focused on reducing complexity by automatically selecting only the features that improve retrieval effectiveness. The achievements of this thesis span both distributed indexing and rank fusion research. Experiments across multiple datasets show that sparse hashes can be used to distribute documents and queries across index entries in a balanced and redundant manner across nodes. Rank fusion results show that is possible to reduce retrieval complexity and improve efficiency by searching only a subset of the feature indexes