International Journal of Grid and Utility Computing 

About: International Journal of Grid and Utility Computing is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Computer science & Cloud computing. It has an ISSN identifier of 1741-847X. Over the lifetime, 408 publications have been published receiving 2740 citations. The journal is also known as: IJGUC.

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

Abstract: 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.

Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud

Abstract: Task scheduling and resource allocation are the key challenges of cloud computing. Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the cost arising from data transfers between resources as well as execution costs must also be taken into account during scheduling based upon user's Quality of Service QoS constraints. In this paper, we present Deadline Constrained Heuristic based Genetic Algorithms HGAs to schedule applications to cloud resources that minimise the execution cost while meeting the deadline for delivering the result. Each workflow's task is assigned priority using bottom-level b-level and top-level t-level. To increase the population diversity, these priorities are then used to create the initial population of HGAs. The proposed algorithms are simulated and evaluated with synthetic workflows based on realistic workflows. The simulation results show that our proposed algorithms have a promising performance as compared to Standard Genetic Algorithm SGA.

Goodput and PDR analysis of AODV, OLSR and DYMO protocols for vehicular networks using CAVENET

Abstract: A Vehicular Ad-Hoc Network (VANET) is a form of Mobile Ad-Hoc Network (MANET), which enables communication between vehicles and nearby roadside infrastructure through a wireless sensing device installed in vehicles. In VANETs and MANETs, the topology of network changes very often, therefore implementation of efficient routing protocols is very important problem. In VANETs, the mobility pattern of nodes is restricted along roads and is affected by movement of neighbour nodes. In this paper, we present a simulation system for VANET called CAVENET. In CAVENET, the mobility patterns of nodes are generated by one-dimensional cellular automaton. We implemented three routing protocols on CAVENET (AODV, OLSR and DYMO) and investigated their performance. We considered as evaluation metrics for simulation goodput and Packet Delivery Ratio (PDR). The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.

Evaluation prediction techniques to achievement an optimal biomedical analysis

Abstract: Intelligent analysis of prediction data mining techniques is widely used to support optimising future decision-making in many different fields including healthcare and medical diagnoses. These techniques include Chi-squared Automatic Interaction Detection (CHAID), Exchange Chi-squared Automatic Interaction Detection (ECHAID), Random Forest Regression and Classification (RFRC), Multivariate Adaptive Regression Splines (MARS), and Boosted Tree Classifiers and Regression (BTCR). This paper presents the general properties, summary, advantages, and disadvantages of each one. Most importantly, the analysis depends upon the parameters that have been used for building a prediction model for each one. Besides, classifying those techniques according to their main and secondary parameters is another task. Furthermore, the presence and absence of parameters are also compared in order to identify the better sharing of those parameters among the techniques. As a result, the techniques with no randomness and mathematical basis are the most powerful and fast compared with the others.

An adaptive meta-scheduler for data-intensive applications

Abstract: In data-intensive applications, such as high-energy physics, bio-informatics, we encounter applications involving numerous jobs that access and generate large datasets. Effective scheduling of such applications is a challenge, due to the need to consider for both computational resources and data storage resources. In this paper, we describe an adaptive scheduling model that considers availability of computational, storage and network resources. Based on this model we implement a scheduler used in our campus grid. The results achieved by our scheduler have been analysed by comparing with greedy algorithm that is widely used in computational grids and some data grids.