Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System

Resource Discovery (RD) is a key issue in Internet-based distributed sytems such as grid. RD is about locating an appropriate resource/service type that matches the user's application requirements. This is very important, as resource reservation and task scheduling are based on it. Unfortunately, RD in grid is very challenging as resources and users are distributed, resources are heterogeneous in their platforms, status of the resources is dynamic (resources can join or leave the system without any prior notice) and most recently the introduction of a new type of grid called intergrid (grid of grids) with the use of multi middlewares. Such situation requires an RD system that has rich interoperability, scalability, decentralization and dynamism features. However, existing grid RD systems have difficulties to attain these features. Not only that, they lack the review and evaluation studies, which may highlight the gap in achieving the required features. Therefore, this work discusses the problem associated with intergrid RD from two perspectives. First, reviewing and classifying the current grid RD systems in such a way that may be useful for discussing and comparing them. Second, propose a novel RD framework that has the aforementioned required RD features. In the former, we mainly focus on the studies that aim to achieve interoperability in the first place, which are known as RD systems that use semantic information (semantic technology). In particular, we classify such systems based on their qualitative use of the semantic information. We evaluate the classified studies based on their degree of accomplishment of interoperability and the other RD requirements, and draw the future research direction of this field. Meanwhile in the latter, we name the new framework as semantic-based scalable decentralized dynamic RD. The framework further contains two main components which are service description, and service registration and discovery models. The earlier consists of a set of ontologies and services. Ontologies are used as a data model for service description, whereas the services are to accomplish the description process. The service registration is also based on ontology, where nodes of the service (service providers) are classified to some classes according to the ontology concepts, which means each class represents a concept in the ontology. Each class has a head, which is elected among its own class I nodes/members. Head plays the role of a registry in its class and communicates with I the other heads of the classes in a peer to peer manner during the discovery process. We further introduce two intelligent agents to automate the discovery process which are Request Agent (RA) and Description Agent (DA). Eaclj. node is supposed to have both agents. DA describes the service capabilities based on the ontology, and RA I carries the service requests based on the ontology as well. We design a service search I algorithm for the RA that starts the service look up from the class of request origin first, then to the other classes. We finally evaluate the performance of our framework ~ith extensive simulation experiments, the result of which confirms the effectiveness of the proposed system in satisfying the required RD features (interoperability, scalability, decentralization and dynamism). In short, our main contributions are outlined new key taxonomy for the semantic-based grid RD studies; an interoperable semantic description RD component model for intergrid services metadata representation; a semantic distributed registry architecture for indexing service metadata; and an agent-qased service search and selection algorithm. Vll

Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System

Resource Discovery (RD) is a key issue in Internet-based distributed sytems such as grid. RD is about locating an appropriate resource/service type that matches the user's application requirements. This is very important, as resource reservation and task scheduling are based on it. Unfortunately, RD in grid is very challenging as resources and users are distributed, resources are heterogeneous in their platforms, status of the resources is dynamic (resources can join or leave the system without any prior notice) and most recently the introduction of a new type of grid called intergrid (grid of grids) with the use of multi middlewares. Such situation requires an RD system that has rich interoperability, scalability, decentralization and dynamism features. However, existing grid RD systems have difficulties to attain these features. Not only that, they lack the review and evaluation studies, which may highlight the gap in achieving the required features. Therefore, this work discusses the problem associated with intergrid RD from two perspectives. First, reviewing and classifying the current grid RD systems in such a way that may be useful for discussing and comparing them. Second, propose a novel RD framework that has the aforementioned required RD features. In the former, we mainly focus on the studies that aim to achieve interoperability in the first place, which are known as RD systems that use semantic information (semantic technology). In particular, we classify such systems based on their qualitative use of the semantic information. We evaluate the classified studies based on their degree of accomplishment of interoperability and the other RD requirements, and draw the future research direction of this field. Meanwhile in the latter, we name the new framework as semantic-based scalable decentralized dynamic RD. The framework further contains two main components which are service description, and service registration and discovery models. The earlier consists of a set of ontologies and services. Ontologies are used as a data model for service description, whereas the services are to accomplish the description process. The service registration is also based on ontology, where nodes of the service (service providers) are classified to some classes according to the ontology concepts, which means each class represents a concept in the ontology. Each class has a head, which is elected among its own class I nodes/members. Head plays the role of a registry in its class and communicates with I the other heads of the classes in a peer to peer manner during the discovery process. We further introduce two intelligent agents to automate the discovery process which are Request Agent (RA) and Description Agent (DA). Eaclj. node is supposed to have both agents. DA describes the service capabilities based on the ontology, and RA I carries the service requests based on the ontology as well. We design a service search I algorithm for the RA that starts the service look up from the class of request origin first, then to the other classes. We finally evaluate the performance of our framework ~ith extensive simulation experiments, the result of which confirms the effectiveness of the proposed system in satisfying the required RD features (interoperability, scalability, decentralization and dynamism). In short, our main contributions are outlined new key taxonomy for the semantic-based grid RD studies; an interoperable semantic description RD component model for intergrid services metadata representation; a semantic distributed registry architecture for indexing service metadata; and an agent-qased service search and selection algorithm. Vll

Hybrid intelligent system for disease diagnosis based on artificial neural networks, fuzzy logic, and genetic algorithms

Disease diagnosis often involves acquiring medical images using devices such as MRI, CT scan, x-ray, or mammograms of patients' organs. Though many medical diagnostic applications have been proposed; finding subtle cancerous cells is still an issue because they are very difficult to be identified. This paper presents an architecture that utilizes a learning algorithm, and uses soft computing to build a medical knowledge base and an inference engine for classifying new images. This system is built on the strength of artificial neural networks, fuzzy logic, and genetic algorithms. These machine intelligence are combined in a complementary approach to overcome the weakness of each other. Moreover, the system also uses Wavelet Transform and Principal Component Analysis for pre-processing and feature to produce features to be used as input to the learning algorithm. 2011 Springer-Verlag.Scopu