Grid Resource Discovery Service is a fundamental problem that has been the focus of research in the recent past. We propose a scheme that has essential characteristics for efficient, self-configuring and fault-tolerant resource discovery and is able to handle dynamic attributes, such as memory capacity. Our approach consists of an overlay network with a hypercube topology connecting the grid nodes and a scalable, fault-tolerant, self-configuring and adaptive search algorithm. Every grid node keeps a small routing table of only log2N entries. The search algorithm is executed in less than (log2N +1) time steps and each grid node is queried only once. By design, the algorithm improves the probability of reaching all working nodes in the system even in the presence of non-alive nodes (inaccessible, crashed or heavily loaded nodes). We analyze the static resilience of the approach presented, which is the measure of how well the algorithm can discover resources without having to update the routing tables. This is done before the routing recovery is processed in order to reconfigure the overlay to avoid non-alive nodes. The results show that our approach has a significantly high static resilience for a Grid environment.

Collaborative tools based on the Web/Internet infrastructure such as E-mail, discussion groups, video/audio conferencing and virtual campuses have been proposed and implemented in many distance learning scenarios. Grids as Internet-wide resource infrastructures that enable access to large resource pools should permit new applications and learning activities. We propose ULabGrid as a new architecture that enables educators to design collaborative, distant laboratories for undergraduate students using the Grid infrastructure. We describe here the prototype that is being developed and present the results of our efforts to date.

A new methodology named CALMANT (CC-cube Algorithms on Meshes and Tori) for mapping a type of algorithm that we call CC-cube algorithm onto multicomputers with hypercube, mesh, or torus interconnection topology is proposed. This methodology is suitable when the initial problem can be expressed as a set of processes that communicate through a hypercube topology (a CC-cube algorithm). There are many important algorithms that fit into the CC-cube type. CALMANT is based on three different techniques: (a) the standard embedding to assign the processes of the algorithm to the nodes of the mesh multicomputer; (b) the communication pipelining technique to increase the level of communication parallelism inherent in the CC-cube algorithms; and (c) optimal message-scheduling algorithms proposed in this work in order to avoid conflicts and minimizing in this way the communication time. Although CALMANT is proposed for multicomputers with different interconnection network topologies, the paper only focuses on the particular case of meshes.

This paper describes a novel, pipelined resource management architecture for computational grids. The design is based on two key realizations. One is that resource management involves a sequence of tasks that is best handled by a pipeline. As shown in the paper, this approach results in a scalable architecture for decentralized scheduling. The other realization is that static aggregation of resources for improved scheduling is inadequate in wide-area computing environments because the needs of users and jobs change with both, location and time. The described architecture addresses this problem by dynamically aggregating resources in a manner that continuously optimizes system response. This is accomplished by way of an active yellow pages directory that allows aggregation constraints to be (re)defined on the fly. An initial prototype of the active yellow pages service has been deployed in the PUNCH network computing environment. Experiences with the production PUNCH system and preliminary results from controlled experiments indicate that the active yellow pages service performs well.

Many parallel algorithms use hypercubes as the communication topology among their processes. When such algorithms are executed on hypercube multicomputers the communication cost is kept minimum since processes can be allocated to processors in such a way that only communication between neighbor processors is required. However, the scalability of hypercube multicomputers is constrained by the fact that the interconnection cost-per-node increases with the total number of nodes. From scalability point of view, meshes and toruses are more interesting classes of interconnection topologies. This paper focuses on the execution of algorithms with hypercube communication topology on multicomputers with mesh or torus interconnection topologies. The proposed approach is based on looking at different embeddings of hypercube graphs onto mesh or torus graphs. The paper concentrates on toruses since an already known embedding, which is called standard embedding, is optimal for meshes. In this paper, an embedding of hypercubes onto toruses of any given dimension is proposed. This novel embedding is called xor embedding. The paper presents a set of performance figures for both the standard and the xor embeddings and shows that the latter outperforms the former for any torus. In addition, it is proven that for a one-dimensional torus (a ring) the xor embedding is optimal in the sense that it minimizes the execution time of a class of parallel algorithms with hypercube topology. This class of algorithms is frequently found in real applications, such as FFT and some class of sorting algorithms.>