The Grid 2: Blueprint for a New Computing Infrastructure

Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

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Modern Operating Systems

Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this paper, we present a hybrid approach called FUGE that is based on fuzzy theory and a genetic algorithm (GA) that aims to perform optimal load balancing considering execution time and cost. We modify the standard genetic algorithm (SGA) and use fuzzy theory to devise a fuzzy-based steady-state GA in order to improve SGA performance in term of makespan. In details, the FUGE algorithm assigns jobs to resources by considering virtual machine (VM) processing speed, VM memory, VM bandwidth, and the job lengths. We mathematically prove our optimization problem which is convex with well-known analytical conditions (specifically, Karush---Kuhn---Tucker conditions). We compare the performance of our approach to several other cloud scheduling models. The results of the experiments show the efficiency of the FUGE approach in terms of execution time, execution cost, and average degree of imbalance.

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FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory

A P2P grid is a special case of grid networks in which P2P communications are used for communication between nodes and trust management. This technology allows creation of a network with greater distribution and scalability. Semantic grids have appeared as an expansion of grid networks in which rich resource metadata are revealed and clearly handled. In a semantic P2P grid, nodes are clustered into different groups based on the semantic similarities between their services. This paper proposes a reputation model for trust management in a semantic P2P grid. We use fuzzy theory, in a trust overlay network named FR TRUST that models the network structure and the storage of reputation information. We present a reputation collection and computation system for semantic P2P grids. The system uses fuzzy theory to compute a peer trust level, which can be either: low, medium, or high. Experimental results demonstrate that FR TRUST combines desirable good computational complexity with high-ranking accuracy.

/pdf/fr-trust-a-fuzzy-reputation-based-model-for-trust-management-3frnkn60hb.pdf

FR trust: a fuzzy reputation-based model for trust management in semantic P2P grids

A web operating system is an operating system that users can access from any hardware at any location. A peer-to-peer (P2P) grid uses P2P communication for resource management and communication between nodes in a grid and manages resources locally in each cluster, and this provides a proper architecture for a web operating system. Use of semantic technology in web operating systems is an emerging field that improves the management and discovery of resources and services. In this paper, we propose PGSW-OS (P2P grid semantic Web OS), a model based on a P2P grid architecture and semantic technology to improve resource management in a web operating system through resource discovery with the aid of semantic features. Our approach integrates distributed hash tables (DHTs) and semantic overlay networks to enable semantic-based resource management by advertising resources in the DHT based upon their annotations to enable semantic-based resource matchmaking. Our model includes ontologies and virtual organizations. Our technique decreases the computational complexity of searching in a web operating system environment. We perform a simulation study using the Gridsim simulator, and our experiments show that our model provides enhanced utilization of resources, better search expressiveness, scalability, and precision.

PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture

Grid computing is the next generation of distributed system. Its goal is to create a huge, independent and powerful virtual machine, and it has been created by gathering different machines with the aim of sharing them. Resource discovery is the most fundamental phase of resource management which has been considered in our work. Considering the advantages and disadvantages of P2P and grid and their architectural similarity; using some of P2P approaches as a solution in Grid can improve the performance of Grid. Semantic technology and fuzzy logic are the two intelligent approaches, which recently apply on P2P network and Grid environment. In P2P networks, nodes are grouping based on their interests. Moreover semantic can be very useful for grouping nodes. This paper follows the identification of resource discovery in grid environment with the help of semantic and fuzzy theory and seeking to present a resource discovery algorithm with respect to exiting obstacles. The proposed approach is assessed in simulated grid environment and the results are compared with other approaches in same conditions. The goals of our approach are to obtain precious matching by clustering nodes which have services semantically related, improving search expressiveness by considering the distances between nodes (delay) in clustering phase, and decreasing response time and processing time by considering the nodes computational capabilities and free space of each node. We evaluate the performance of our approach with some distributed semantic based grid resource discovery models. The results of the experiments show the efficiency of the proposed approach in term of scalability, precision, search expressiveness and response time.

/pdf/a-novel-decentralized-fuzzy-based-approach-for-grid-resource-2wjeanl0sg.pdf

A Novel Decentralized Fuzzy Based Approach for Grid Resource Discovery

Grid and peer-to-peer (P2P) networks are two ideal technologies for file sharing. A P2P grid is a special case of grid networks in which P2P communications are used for communication between nodes and trust management. Use of this technology allows creation of a network with greater distribution and scalability. Semantic grids have appeared as an expansion of grid networks in which rich resource metadata are revealed and clearly handled. In a semantic P2P grid, nodes are clustered into different groups based on the semantic similarities between their services. This paper proposes a reputation model for trust management in a semantic P2P Grid. We use fuzzy theory, in a trust overlay network named FR TRUST that models the network structure and the storage of reputation information. In fact we present a reputation collection and computation system for semantic P2P Grids. The system uses fuzzy theory to compute a peer trust level, which can be either: Low, Medium, or High. Our experimental results demonstrate that FR TRUST combines low (and therefore desirable) a good computational complexity with high ranking accuracy.

FRTRUST: a fuzzy reputation based model for trust management in semantic P2P grids

Discovering and ranking complex relationships in the semantic web is an important building block of semantic search applications. Although semantic web technologies define relations between objects but there are some complex (hidden) relationships that are valuable in different applications. Currently, users need to discover the relations between objects and find the level of semantic similarity between them. (I.e. find two similar papers).

This paper presents a new approach for ranking semantic similarity association in semantic web document, based on semantic association concept.

/pdf/ranking-semantic-similarity-association-in-semantic-web-353r6qfslf.pdf

Shahdad Shariatmadari

Papers

FR trust: a fuzzy reputation-based model for trust management in semantic P2P grids

PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture

A Novel Decentralized Fuzzy Based Approach for Grid Resource Discovery

FRTRUST: a fuzzy reputation based model for trust management in semantic P2P grids

Ranking semantic similarity association in semantic web