A semantics-based routing scheme for grid resource discovery
Grid technologies enable the sharing of a wide variety of distributed resources. To fully utilize these resources, effective resource discovery mechanisms are necessities. However, the complicated and dynamic characteristics of grid resources make sharing and discovering them a challenging issue. In this paper, we propose a peer-to-peer (P2P) based overlay network to assist the efficient resource discovery and query. The framework is based on the RDF metadata infrastructure, allowing a rich and extensible description of resources. To avoid flooding the network with a query, we propose a comprehensive semantics-based query forwarding strategy, which only forwards queries to semantically related nodes. After the related nodes have been located, the original RDF query is used to do the final query and retrieval. Results from simulation experiments demonstrate that this architecture is scalable and efficient

title: FedGrid: An HLA Approach to Federating Grids
abstract: Research on Grids has received considerable attention in recent years. Whereas existing work on connecting the grids primarily focused on grid services, in our work we propose a unified approach, so-called FederationGrid (or FedGrid for short), which integrates both virtual organization (VO) and grid services, thus enabling formation of virtual communities on top of grids. FedGrid is based on the standard High Level Architecture (HLA) that enjoys the advantages of high-level information modeling, including re-usability of software components and real-time performance of the overall systems. In addition, FedGrid is a fractal grid, comprised of hierarchical HLA federations, e.g. Realms and Lobby, which supports well the concept of virtual communities and scalability.

title: An efficient clustered architecture for P2P networks
abstract: Peer-to-peer (P2P) computing offers many attractive features, such as self-organization, load-balancing, availability, fault tolerance, and anonymity. However, it also faces some serious challenges. In this paper, we propose an Efficient Clustered Super-Peer P2P architecture (ECSP) to overcome the scalability and efficiency problems of existing unstructured P2P system. With ECSP, peers are grouped into clusters according to their topological proximity, and super-peers are selected from regular peers to act as cluster leaders and service providers. These super-peers are also connected to each other, forming a backbone overlay network operating as a distinct, yet integrated, application. To maintain the dynamically adaptive overlay network and to manage the routing on it, we propose an application level broadcasting protocol: Efa. Applying only a small amount of information about the topology of a network, Efa is as simple as flooding, a conventional method used in unstructured P2P systems. By eliminating many duplicated messages, Efa is much more efficient and scalable than flooding, and furthermore, it is completely decentralized and self-organized. Our experimental results prove that ESCP architecture, combined with the super-peer backbone protocol, can generate impressive levels of performance and scalability.

title: Ontology-Based Clustering and Routing in Peer-to-Peer Networks
abstract: How to improve the performance of content searching in peer-to-peer (P2P) systems is a challenging issue. In this paper we attack this problem by proposing a new decentralized P2P architecture – ontology-based community overlays. The system exploits the semantic property of the content in the network to cluster nodes sharing similar interest together to improve the query and searching performance. Specifically, a distributed hash table (DHT) based overlay is constructed to assist peers organizing into communities. Those peers in the same community form a Gnutella-like unstructured overlay. This architecture helps reduce the search time and decrease the network traffic by minimizing the number of messages propagated in the system. Moreover, it retains the desirable properties of existing unstructured architectures, including being fully decentralized with loose structure, and supporting complex queries. We demonstrate by simulation, that with this architecture, peers can get more relevant resources faster and with less traffic generated.

title: Efa: an efficient content routing algorithm in large peer-to-peer overlay networks
abstract: An important issue for peer-to-peer application is to locate content within the network. There are many existing solutions to this problem, however, each of them addresses different aspects and each has its deficiencies. We focus on the unstructured peer-to-peer scenario and present a constrained flooding routing algorithm, Efa, which overcomes some of the deficiencies of those existing strategies. Efa performs application level broadcasting in a potentially very large peer-to-peer network overlaid on the Internet. Efa is completely decentralized and self-organized. It is a more scalable alternative to flooding, which is commonly used in unstructured peer-to-peer systems. Utilizing just a small amount of topology info, Efa is almost as simple as flooding, but it is much more efficient and scalable.

title: Grid Resource Discovery Using Semantic Communities
abstract: Grid technologies enable the sharing and collaborating of a wide variety of resources. To fully utilize these resources, effective resource discovery mechanisms are necessities. However, the complicated and dynamic characteristics of the grid resources make sharing and discovering them a challenging issue. In this paper, we propose a semantic community approach to assisting the efficient resource discovery and query in grids. Semantic community is a natural arrangement of distributed systems. It prunes the searching space and reduces the cost of searching. Moreover, it exhibits many desirable properties, including being fully decentralized and supporting complex queries. Our simulation results show how searching the girds can take advantage of semantic communities to reduce the searching cost and improve the quality of search results.

title: Groups of connected objects

title: A Self-Adjusting Algorithm for Driver Scheduling
abstract: Public transport driver scheduling is a world wide problem, which is NP-hard. Although some mathematically based methods are being used in the transport industry, there is still much scope for improvements. This paper presents a novel evolutionary approach that simulates the self-adjusting process on a single schedule. Five factors characterized by fuzzy membership functions are first aggregated to evaluate the shift structure. This evaluating function is incorporated into a constructing heuristic to make shift selection. A self-adjusting algorithm is then designed to guide the constructing heuristic to improve a given initial schedule iteratively. In each generation an unfit portion of the working schedule is removed. Broken schedules are repaired by the constructing heuristic until stopping condition is met. Experimental results on real-world driver scheduling problems has demonstrated the success of the proposed approach.

title: Incorporating Legacy System into Semantic Grid Framework
abstract: The semantic grid is an extension of the current grid. Resources description framework (RDF) can be used to extend the current grid into semantic grid in which information and services are given well-defined meaning. This paper proposed an approach to recognizing the reusable components from the legacy system and representing these components by using XML and RDF, which could be performed for incorporating legacy system components into the semantic grid framework

title: Towards evolving Web sites into grid services environment
abstract: Grid services are emerged by integrating grid computing and Web services to perform a seamless information processing system across distributed, heterogeneous, dynamic virtual organisations that the user can access from any location. The Web Service Resource Framework (WSRF) was announced as a new way for manipulating "stateful resources" to perform grid services. It defines the concept of "stateful resources" and how they can be discovered, queried and manipulated via Web services. In this paper, we proposed a grid services-oriented reengineering approach to create stateful resources from conventional HTML Web sites, which applies hierarchical cluster and wrapper techniques to extract and translate Web sites resources. It supports services identification and packaging and archives Web site evolution into grid services environment by exploiting WRSF.

title: A source coding approach to classification by vector quantization and the principle of minimum description length
abstract: An algorithm for supervised classification using vector quantization and entropy coding is presented. The classification rule is formed from a set of training data {(X/sub i/, Y/sub i/)}/sub i=1//sup n/, which are independent samples from a joint distribution P/sub XY/. Based on the principle of minimum description length (MDL), a statistical model that approximates the distribution P/sub XY/ ought to enable efficient coding of X and Y. On the other hand, we expect a system that encodes (X, Y) efficiently to provide ample information on the distribution P/sub XY/. This information can then be used to classify X, i.e., to predict the corresponding Y based on X. To encode both X and Y, a two-stage vector quantizer is applied to X and a Huffman code is formed for Y conditioned on each quantized value of X. The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function reflecting the joint penalty of quantization error and misclassification rate. This vector quantizer provides an estimation of the conditional distribution of Y given X, which in turn yields an approximation to the Bayes classification rule. This algorithm, namely discriminant vector quantization (DVQ), is compared with learning vector quantization (LVQ) and CART/sup R/ on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed.