Grid computing is a virtualized distributed computing environment aimed at enabling the sharing of geographically distributed resources. Grid resources have traditionally consisted of dedicated supercomputers, clusters, or storage units. With the present ubiquitous network connections and the growing computational and storage capabilities of modem everyday-use computers, more resources such as PCs, devices (e.g., PDAs and sensors), applications, and services are on grid networks. Grid is expected to evolve from a computing and data management facility to a pervasive, world-wide resource-sharing infrastructure. To fully utilize the wide range of grid resources, effective resource discovery mechanisms are required. However, resource discovery in a global-scale grid is challenging due to the considerable diversity, large number, dynamic behavior, and geographical distribution of the resources. The resource discovery technology required to achieve the ambitious global grid vision is still in its infancy, and existing applications have difficulties in achieving both rich searchability and good scalability. In this thesis, we investigate the resource discovery problem for open-networked global-scale grids. In particular, we propose a distributed semantics-based discovery framework. We show how this framework can be used to address the discovery problem in such grids and improve three aspects of performance: expressiveness, scalability, and efficiency. Expressiveness is the first characteristic that a grid resource-searching mechanism should have. Most existing search systems use simple keyword-based lookups, which limit the searchability of the system. Our framework improves search expressiveness from two directions: First, it uses a semantic metadata scheme to provide users with a rich and flexible representation mechanism, to enable effective descriptions of desired resource properties and query requirements. Second, we employ ontological domain knowledge to assist in the search process. The system is thus able to understand the semantics of query requests according to their meanings in a specific domain; this procedure helps the system to locate only semantically related results. The more expressive the resource description and query request, however, the more difficult it is to design a scalable and efficient search mechanism. We ensure scalability by reconfiguring the network with respect to shared ontologies. This reconfiguration partitions the large unorganized search space into multiple well-organized semantically related sub-spaces that we call semantic virtual organizations. Semantic virtual organizations help to discriminatively distribute resource information and queries to related nodes, thus reducing the search space and improving scalability. To further improve the efficiency of searching the virtual organizations, we propose two semantics-based resource-integrating and searching systems: GONID and OntoSum. These two systems address searching problems for applications based on different network topologies: structured and unstructured peer-to-peer overlay networks. Queries in the search systems are processed in a transparent way, so that users accessing the data can be insulated from the fact that the information is distributed across different sources and represented with different formats. In both systems, ontological knowledge is decomposed into different coarse-grained elements, and then these elements are indexed with different schemes to fit the requirements of different applications. Resource metadata reasoning, integrating, and searching are based on the index. A complex query can be evaluated by performing relational operations such as select, project, and join on combinations of the indexing elements. We evaluate the performance of our system with extensive simulation experiments, the results of which confirm the effectiveness of the design. In addition, we implement a prototype that incorporates our ontology-based virtual organization formation and semantics-based query mechanisms. Our deployment of the prototype verifies the system's feasibility and its applicability to real-world applications.

Grid computing is a virtualized distributed computing environment aimed at enabling the sharing of geographically distributed resources. Grid resources have traditionally consisted of dedicated supercomputers, clusters, or storage units. With the present ubiquitous network connections and the growing computational and storage capabilities of modem everyday-use computers, more resources such as PCs, devices (e.g., PDAs and sensors), applications, and services are on grid networks. Grid is expected to evolve from a computing and data management facility to a pervasive, world-wide resource-sharing infrastructure. To fully utilize the wide range of grid resources, effective resource discovery mechanisms are required. However, resource discovery in a global-scale grid is challenging due to the considerable diversity, large number, dynamic behavior, and geographical distribution of the resources. The resource discovery technology required to achieve the ambitious global grid vision is still in its infancy, and existing applications have difficulties in achieving both rich searchability and good scalability. In this thesis, we investigate the resource discovery problem for open-networked global-scale grids. In particular, we propose a distributed semantics-based discovery framework. We show how this framework can be used to address the discovery problem in such grids and improve three aspects of performance: expressiveness, scalability, and efficiency. Expressiveness is the first characteristic that a grid resource-searching mechanism should have. Most existing search systems use simple keyword-based lookups, which limit the searchability of the system. Our framework improves search expressiveness from two directions: First, it uses a semantic metadata scheme to provide users with a rich and flexible representation mechanism, to enable effective descriptions of desired resource properties and query requirements. Second, we employ ontological domain knowledge to assist in the search process. The system is thus able to understand the semantics of query requests according to their meanings in a specific domain; this procedure helps the system to locate only semantically related results. The more expressive the resource description and query request, however, the more difficult it is to design a scalable and efficient search mechanism. We ensure scalability by reconfiguring the network with respect to shared ontologies. This reconfiguration partitions the large unorganized search space into multiple well-organized semantically related sub-spaces that we call semantic virtual organizations. Semantic virtual organizations help to discriminatively distribute resource information and queries to related nodes, thus reducing the search space and improving scalability. To further improve the efficiency of searching the virtual organizations, we propose two semantics-based resource-integrating and searching systems: GONID and OntoSum. These two systems address searching problems for applications based on different network topologies: structured and unstructured peer-to-peer overlay networks. Queries in the search systems are processed in a transparent way, so that users accessing the data can be insulated from the fact that the information is distributed across different sources and represented with different formats. In both systems, ontological knowledge is decomposed into different coarse-grained elements, and then these elements are indexed with different schemes to fit the requirements of different applications. Resource metadata reasoning, integrating, and searching are based on the index. A complex query can be evaluated by performing relational operations such as select, project, and join on combinations of the indexing elements. We evaluate the performance of our system with extensive simulation experiments, the results of which confirm the effectiveness of the design. In addition, we implement a prototype that incorporates our ontology-based virtual organization formation and semantics-based query mechanisms. Our deployment of the prototype verifies the system's feasibility and its applicability to real-world applications.

Grid computing is a virtualized distributed computing environment aimed at enabling the sharing of geographically distributed resources. Grid resources have traditionally consisted of dedicated supercomputers, clusters, or storage units. With the present ubiquitous network connections and the growing computational and storage capabilities of modem everyday-use computers, more resources such as PCs, devices (e.g., PDAs and sensors), applications, and services are on grid networks. Grid is expected to evolve from a computing and data management facility to a pervasive, world-wide resource-sharing infrastructure. To fully utilize the wide range of grid resources, effective resource discovery mechanisms are required. However, resource discovery in a global-scale grid is challenging due to the considerable diversity, large number, dynamic behavior, and geographical distribution of the resources. The resource discovery technology required to achieve the ambitious global grid vision is still in its infancy, and existing applications have difficulties in achieving both rich searchability and good scalability. In this thesis, we investigate the resource discovery problem for open-networked global-scale grids. In particular, we propose a distributed semantics-based discovery framework. We show how this framework can be used to address the discovery problem in such grids and improve three aspects of performance: expressiveness, scalability, and efficiency. Expressiveness is the first characteristic that a grid resource-searching mechanism should have. Most existing search systems use simple keyword-based lookups, which limit the searchability of the system. Our framework improves search expressiveness from two directions: First, it uses a semantic metadata scheme to provide users with a rich and flexible representation mechanism, to enable effective descriptions of desired resource properties and query requirements. Second, we employ ontological domain knowledge to assist in the search process. The system is thus able to understand the semantics of query requests according to their meanings in a specific domain; this procedure helps the system to locate only semantically related results. The more expressive the resource description and query request, however, the more difficult it is to design a scalable and efficient search mechanism. We ensure scalability by reconfiguring the network with respect to shared ontologies. This reconfiguration partitions the large unorganized search space into multiple well-organized semantically related sub-spaces that we call semantic virtual organizations. Semantic virtual organizations help to discriminatively distribute resource information and queries to related nodes, thus reducing the search space and improving scalability. To further improve the efficiency of searching the virtual organizations, we propose two semantics-based resource-integrating and searching systems: GONID and OntoSum. These two systems address searching problems for applications based on different network topologies: structured and unstructured peer-to-peer overlay networks. Queries in the search systems are processed in a transparent way, so that users accessing the data can be insulated from the fact that the information is distributed across different sources and represented with different formats. In both systems, ontological knowledge is decomposed into different coarse-grained elements, and then these elements are indexed with different schemes to fit the requirements of different applications. Resource metadata reasoning, integrating, and searching are based on the index. A complex query can be evaluated by performing relational operations such as select, project, and join on combinations of the indexing elements. We evaluate the performance of our system with extensive simulation experiments, the results of which confirm the effectiveness of the design. In addition, we implement a prototype that incorporates our ontology-based virtual organization formation and semantics-based query mechanisms. Our deployment of the prototype verifies the system's feasibility and its applicability to real-world applications. / Science, Faculty of / Computer Science, Department of / Graduate

The success of the internet over last few decades has mainly depended on various infrastructure technologies to run distributed applications. Due to diversification and multi-provider nature of the internet, radical architectural improvements which require mutual agreement between infrastructure providers have become highly impractical. This escalating resistance towards the further growth has created a rising demand for new approaches to address this challenge. Network virtualization is regarded as a prominent solution to surmount these limitations. It decouples the conventional Internet service provider’s role into infrastructure provider (InP) and service provider (SP) and introduce a third player known as virtual network Provider (VNP) which creates virtual networks (VNs). Resource discovery aims to assist the VNP in selecting the best InP that has the best matching resources for a particular VN request. In the current literature, resource discovery focuses mainly on static attributes of network resources highlighting the fact that utilization on dynamic attributes imposes significant overhead on the network itself. In this thesis we propose a resource discovery approach that is capable of utilizing the dynamic resource attributes to enhance the resource discovery and increase the overall efficiency of VN creation. We realize that recourse discovery techniques should be fast and cost efficient, enough to not to impose any significant load. Hence our proposed scheme calculates aggregation values of the dynamic attributes of the substrate resources. By comparing aggregation values to VN requirements, a set of potential InPs is selected. The potential InPs satisfy basic VN embedding requirements. Moreover, we propose further enhancements to the dynamic attribute monitoring process using a vector based aggregation approach.

The success of the internet over last few decades has mainly depended on various infrastructure technologies to run distributed applications. Due to diversification and multi-provider nature of the internet, radical architectural improvements which require mutual agreement between infrastructure providers have become highly impractical. This escalating resistance towards the further growth has created a rising demand for new approaches to address this challenge. Network virtualization is regarded as a prominent solution to surmount these limitations. It decouples the conventional Internet service provider’s role into infrastructure provider (InP) and service provider (SP) and introduce a third player known as virtual network Provider (VNP) which creates virtual networks (VNs). Resource discovery aims to assist the VNP in selecting the best InP that has the best matching resources for a particular VN request. In the current literature, resource discovery focuses mainly on static attributes of network resources highlighting the fact that utilization on dynamic attributes imposes significant overhead on the network itself. In this thesis we propose a resource discovery approach that is capable of utilizing the dynamic resource attributes to enhance the resource discovery and increase the overall efficiency of VN creation. We realize that recourse discovery techniques should be fast and cost efficient, enough to not to impose any significant load. Hence our proposed scheme calculates aggregation values of the dynamic attributes of the substrate resources. By comparing aggregation values to VN requirements, a set of potential InPs is selected. The potential InPs satisfy basic VN embedding requirements. Moreover, we propose further enhancements to the dynamic attribute monitoring process using a vector based aggregation approach.

In this body of work, we present the details of a novel method for passive resource discovery in cluster grid environments where resources constantly utilize inter-node communication. Our method offers the ability to non-intrusively identify resources that have available memory or CPU cycles; this is critical for lowering queue wait times in large cluster grid networks, and for memory-intensive cluster grid applica-tions such as Gaussian (computational chemistry package) and the Weather Research and Forecasting (WRF) modeling package. The benefits include: (1) low message complexity, (2) scalability, (3) load bal-ancing support, and (4) low maintainability. Using several test-beds (i.e., a small local test-bed and a 50-node Deterlab test-bed), we demonstrate the feasibility of our method with experiments utilizing TCP, UDP and ICMP network traffic. Using this technique, we observed a correlation between memory or CPU load and the timely response of network traffic. In such situations, we have observed that in highly utilized (due to multi-programming) nodes there will be numerous, active processes which require context switching or paging. The latency associated with numerous context switches or paging manifests as a de-lay signature within the packet transmission process. Our method detects this delay signature to determine the utilization of network resources. The aforementioned delay signature is the keystone that provides a correlation between network traffic and the internal state of the source node. We characterize this delay signature due to CPU utilization by (1) identifying the different types of assembly language instructions that source this delay and (2) describing how performance-enhancing techniques (e.g., instruction pipelin-ing, caching) impact this delay signature by using the LEON3, implemented as a 40 MHz development board. At the software level, results for medium sized networks show that our method can consistently and accurately identify nodes with available memory or CPU cycles (< 70% availability). At the hardware level, our results show that excessive context switching in active applications increases the average mem-ory access time, thus adding additional delay to the execution of LD instructions. Additionally, internal use of these instructions in heavily utilized situations to send network packets induces the delay signature into network traffic.

A fundamental problem in building large scale Grid resource sharing system is the need for efficient and scalable techniques for discovery and provisioning of resources for delivering expected Quality of Service (QoS) to users’ applications. The current approaches to Grid resource sharing based on resource brokers are non-coordinated since these brokers make scheduling related decisions independent of the others in the system. Clearly, this worsens the load-sharing and utilisation problems of distributed Grid resources as sub-optimal schedules are likely to occur. Further, existing brokering systems rely on centralised information services for resource discovery. Centralised or hierarchical resource discovery systems are prone to single-point failure, lack scalability and fault-tolerance ability. In the centralised model, the network links leading to the server are very critical to the overall functionality of the system, as their failure might halt the entire distributed system operation.

The success of the internet over last few decades has mainly depended on various infrastructure technologies to run distributed applications. Due to diversification and multi-provider nature of the internet, radical architectural improvements which require mutual agreement between infrastructure providers have become highly impractical. This escalating resistance towards the further growth has created a rising demand for new approaches to address this challenge. Network virtualization is regarded as a prominent solution to surmount these limitations. It decouples the conventional Internet service provider’s role into infrastructure provider (InP) and service provider (SP) and introduce a third player known as virtual network Provider (VNP) which creates virtual networks (VNs). Resource discovery aims to assist the VNP in selecting the best InP that has the best matching resources for a particular VN request. In the current literature, resource discovery focuses mainly on static attributes of network resources highlighting the fact that utilization on dynamic attributes imposes significant overhead on the network itself. In this thesis we propose a resource discovery approach that is capable of utilizing the dynamic resource attributes to enhance the resource discovery and increase the overall efficiency of VN creation. We realize that recourse discovery techniques should be fast and cost efficient, enough to not to impose any significant load. Hence our proposed scheme calculates aggregation values of the dynamic attributes of the substrate resources. By comparing aggregation values to VN requirements, a set of potential InPs is selected. The potential InPs satisfy basic VN embedding requirements. Moreover, we propose further enhancements to the dynamic attribute monitoring process using a vector based aggregation approach.

One of the fundamental requirements of Grid computing is efficient and effective resource discovery mechanism. Resource discovery involves discovery of appropriate resources required by user applications. In this regard various resource discovery mechanisms have been proposed during the recent years. These mechanisms range from centralized to hierarchical information servers approach. Most of the techniques developed based on these approaches have scalability and fault tolerance limitations. To overcome these limitations Peer to Peer based discovery mechanisms are proposed. / shairbaz@gmail.com

By 2007, one half of higher education students are expected to enroll in distributed learning courses. Higher education institutions need to attract students searching the Internet for courses and need to provide students with enough information to select courses. Internet resource discovery tools are readily available, however, users have difficulty selecting relevant resources. In part this is due to the lack of a standard for representation of Internet resources. An emerging solution is metadata. In the educational domain, the IEEE Learning Technology Standards Committee (LTSC) has specified a Learning Object Metadata (LOM) standard. This exploratory study (a) determined criteria students think are important for selecting higher education courses, (b) discovered relationships between these criteria and students' demographic characteristics, educational status, and Internet experience, and (c) evaluated these criteria vis-à-vis the IEEE LTSC LOM standard. Web-based questionnaires (N=209) measured (a) the criteria students think are important in the selection of higher education courses and (b) three factors that might influence students' selections. Respondents were principally female (66%), employed full time (57%), and located in the U.S. (89%). The chi square goodness-of-fit test determined 40 criteria students think are important and exploratory factor analysis determined five common factors among the top 21 criteria, three evaluative factors and two descriptive. Results indicated evaluation criteria are very important in course selection. Spearman correlation coefficients and chi-square tests of independence determined the relationships between the importance of selection criteria and demographic characteristics, educational status, and Internet experience. Four profiles emerged representing groups of students with unique concerns. Side by side analysis determined if the IEEE LTSC LOM standard included the criteria of importance to students. The IEEE LOM by itself is not enough to meet students course selection needs. Recommendations include development of a metadata standard for course evaluation and accommodation of group differences in information retrieval systems.