Abstract — This paper presents a self-organized grouping (SOG) method that achieves efficient Grid resource discovery by forming and maintaining autonomous resource groups. Each group dynamically aggregates a set of resources that are similar to each other in some pre-specified resource characteristic. The SOG method takes advantage of the strengths of both centralized and decentralized approaches that were previously developed for Grid/P2P resource discovery. The design of the SOG method minimizes the overhead incurred in forming and maintaining groups and maximizes resource discovery performance. The way SOG method handles resource discovery queries is metaphorically similar to searching for a word in an English dictionary by identifying its alphabetical groups at the first place. It is shown from a series of computational experiments that SOG method achieves more stable (i.e., independent of the factors such as resource densities, and Grid sizes) and efficient lookup performance than other existing approaches.

This paper presents the quality of partitions induced by the Lebesgue curve in average case. The surface that surrounds an arbitrary index range is compared with the optimal partition in the grid, i. e. the square. The upper bound on the surface is asymptotically 3 times the optimal size.

Grid superscheduling requires support for efficient and scalable discovery of resources. Resource discovery activities involve searching for the appropriate resource types that match the user’s job requirements. To accomplish this goal, a resource discovery system that supports the desired look-up operation is mandatory. Various kinds of solutions to this problem have been suggested, including the centralised and hierarchical information server approach. However, both of these approaches have serious limitations in regards to scalability, fault-tolerance and network congestion. To overcome these limitations, organising resource information using Peer-to-Peer (P2P) network model has been proposed. Existing approaches advocate an extension to structured P2P protocols, to support the Grid resource information system (GRIS). In this paper, we identify issues related to the design of such an efficient, scalable, fault-tolerant, consistent and practical GRIS system using a P2P network model. We compile these issues into various taxonomies in sections 3 and 4. Further, we look into existing works that apply P2P based network protocols to GRIS. We think that this taxonomy and its mapping to relevant systems would be useful for academic and industry based researchers who are engaged in the design of scalable Grid systems. 1

Efficient Resource discovery mechanism is one of the fundamental requirement for Grid computing systems, as it aids in resource management and scheduling of applications. Resource discovery activity involve searching for the appropriate resource types that match the user’s application requirements. Various kinds of solutions to grid resource discovery have been suggested, including the centralised and hierarchical information server approach. However, both of these approaches have serious limitations in regards to scalability, fault-tolerance and network congestion. To overcome these limitations, indexing resource information using a decentralised (such as Peer-to-Peer (P2P)) network model has been actively proposed in the past few years. This article investigates various decentralised resource discovery techniques primarily driven by P2P network model. To summarise, this article presents a: (i) summary of current state of art in grid resource discovery; (ii) resource taxonomy with focus on computational grid paradigm; (iii) P2P taxonomy with focus on extending the current structured systems (such as Distributed Hash Tables) for indexing d-dimensional grid resource queries; (iv) detailed survey of existing works that can support d-dimensional grid resource queries; and (v) classification of the surveyed approaches based on the proposed P2P taxonomy. We believe that this taxonomy and its mapping to relevant systems would be useful for academic and industry based researchers who are engaged in the design of scalable Grid and P2P systems. 1

Efficient Resource discovery mechanism is one of the fundamental requirement for Grid computing systems, as it aids in resource management and scheduling of applications. Resource discovery activity involve searching for the appropriate resource types that match the user’s application requirements. Various kinds of solutions to grid resource discovery have been suggested, including the centralised and hierarchical information server approach. However, both of these approaches have serious limitations in regards to scalability, fault-tolerance and network congestion. To overcome these limitations, indexing resource information using a decentralised (such as Peer-to-Peer (P2P)) network model has been actively proposed in the past few years. This article investigates various decentralised resource discovery techniques primarily driven by P2P network model. To summarise, this article presents a: (i) summary of current state of art in grid resource discovery; (ii) resource taxonomy with focus on computational grid paradigm; (iii) P2P taxonomy with focus on extending the current structured systems (such as Distributed Hash Tables) for indexing d-dimensional grid resource queries 1; (iv) detailed survey of existing works that can support d-dimensional grid resource queries; and (v) classification of the surveyed approaches based on the proposed P2P taxonomy. We believe that this taxonomy and its mapping to relevant systems would be useful for academic and industry based researchers who are engaged in the design of scalable Grid and P2P systems. 1

Data structures based on space-filling curves have shown to be a good approach in several applications. For the monitoring of moving objects, e. g. necessary for the contact detection in finite-element simulations, we need a special metrics to compare the quality of different curves. This paper

An overview is presented of object-based and image-based representations of objects by their interiors. The representations are distinguished by the manner in which they can be used to answer two fundamental queries in database applications: (1) Feature query: given an object, determine its constituent cells (i.e., their locations in space). (2) Location query: given a cell (i.e., a location in space), determine the identity of the object (or objects) of which it is a member as well as the remaining constituent cells of the object (or objects). Regardless of the representation that is used, the generation of responses to the feature and location queries is facilitated by building an index (i.e., the result of a sort) either on the objects or on their locations in space, and implementing it using an access structure that correlates the objects with the locations. Assuming the presence of an access structure, implicit (i.e., image-based) representations are described that are good for finding the objects associated with a particular location or cell (i.e., the location query), while requiring that all cells be examined when determining the locations associated with a particular object (i.e., the feature query). In contrast, explicit (i.e., object-based) representations are good for the feature query,

A discrete space-filling curve provides a linear traversal/indexing of a multi-dimensional grid space. This paper presents an application of random walk to the study of inter-clustering of space-filling curves and an analytical study on the inter-clustering performances of 2-dimensional Hilbert and z-order curve families. Two underlying measures are employed: the mean inter-cluster distance over all inter-cluster gaps and the mean total inter-cluster distance over all subgrids. We show how approximating the mean inter-cluster distance statistics of continuous multi-dimensional space-filling curves fits into the formalism of random walk, and derive the exact formulas for the two statistics for both curve families. The excellent agreement in the approximate and true mean inter-cluster distance statistics suggests that the random walk may furnish an effective model to develop approximations to clustering and locality statistics for space-filling curves. Based upon the analytical results, the asymptotic comparisons indicate that z-order curve family performs better than Hilbert curve family with respect to both statistics.

A new quality of service (QoS) aware disk scheduling algorithm is presented. It is applicable in environments where data requests arrive with different QoS requirements such as real-time deadline, and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. A unified framework for QoS disk scheduling is presented that scales with the number of scheduling parameters. The general idea is based on modeling the disk scheduler requests as points in the multi-dimensional space, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number, and a third dimension represents the priority of the request, etc.). Then the disk scheduling problem reduces to the problem of finding a linear order to traverse these multi-dimensional points. Space-filling curves are adopted to define a linear order for sorting and scheduling objects that lie in the multi-dimensional space. This generalizes the one-dimensional disk scheduling algorithms (e.g., EDF, SATF, FIFO). Several techniques are presented to show how a QoS-aware disk scheduler deals with the progressive arrival of requests over time. Simulation experiments are presented to show a comparison of the alternative techniques and to demonstrate the scalability of the proposed QoS-aware disk scheduling algorithm over other traditional approaches.

Dynamic and heterogeneous characteristics of large-scale Grids make the fun-damental problem of resource discovery a great challenge. This thesis presents a self-organized grouping (SOG) infrastructure that achieves efficient Grid resource discovery by forming and maintaining autonomous resource groups. Each group dy-namically aggregates a set of resources that are similar to each other in some pre-specified resource characteristic. The SOG method takes advantage of the strengths of both centralized and decentralized approaches that were previously developed for Grid/P2P resource discovery. The design of the SOG method minimizes the overhead incurred in forming and maintaining groups and maximizes resource discovery perfor-mance. The way SOG method handles resource discovery queries is metaphorically similar to searching for a word in an English dictionary by identifying its alphabetical groups at the first place and then performing a lexical search within the group. The algorithms implemented in SOG method are illustrated with details. This thesis also illustrates a generalized approach using a space-filling curve