PDDRA: A new pre-fetching based dynamic data replication algorithm in data grids

In recent years, grid technology has had such a fast growth that it has been used in many scientific experiments and research centers. A large number of storage elements and computational resources are combined to generate a grid which gives us shared access to extra computing power. In particular, data grid deals with data intensive applications and provides intensive resources across widely distributed communities. Data replication is an efficient way for distributing replicas among the data grids, making it possible to access similar data in different locations of the data grid. Replication reduces data access time and improves the performance of the system. In this paper, we propose a new dynamic data replication algorithm named PDDRA that optimizes the traditional algorithms. Our proposed algorithm is based on an assumption: members in a VO (Virtual Organization) have similar interests in files. Based on this assumption and also file access history, PDDRA predicts future needs of grid sites and pre-fetches a sequence of files to the requester grid site, so the next time that this site needs a file, it will be locally available. This will considerably reduce access latency, response time and bandwidth consumption. PDDRA consists of three phases: storing file access patterns, requesting a file and performing replication and pre-fetching and replacement. The algorithm was tested using a grid simulator, OptorSim developed by European Data Grid projects. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, effective network usage, total number of replications, hit ratio and percentage of storage filled.     

Application of ion-engineered Persian Gulf seawater in EOR:effects of different ions on interfacial tension,contact angle,zeta potential,and oil recovery

In this study,we initially performed interfacial tension(IFT)tests to investigate the potential of using the Persian Gulf sea-water(PGSW)as smart water with dif...     

Wettability alteration and retention of mixed polymer-grafted silica nanoparticles onto oil-wet porous medium

Enhanced oil recovery(EOR)processes are applied to recover trapped or residual oil in the reservoir rocks after primary and secondary recovery methods.Changing ...     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

A hybrid metaheuristic approach for the capacitated p-median problem

The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.     

考虑电容器电压特性的配电网无功规划

传统的配电网无功规划中,补偿电容器的容量往往被当作固定值,事实上,电容器的实际补偿容量和其接入点的电压水平有着很大的关系。考虑电容器的电压特性,以年综合费用最小为目标,建立了配电网无功规划模型。针对该模型的特点,提出采用自适应遗传算法进行求解。通过33节点和69节点配电网的仿真分析对模型进行验证。     

Thermal degradation behavior and kinetic studies of polyacrylamide gel in TiO2 nanoparticles synthesis

Polyacrylamide gel (PAMG) method is a simple, fast and cheap method used for the synthesis of a wide variety of nanopowders. However, no adequate results have been reported on the thermal degradation behavior of PAMG which can be very effective on the final product properties. In this work, thermal degradation behavior of PAMG in the presence of TiCl₄ as a precursor salt for synthesis of TiO₂ nanoparticles was examined in comparison with linear polyacrylamide (LPAM) and pure PAMG by thermogravimetry/differential thermal analysis. Their thermal degradation kinetics was investigated, as well. The results showed that thermal degradation of all samples occurred in two stages at different onset temperatures. Despite the high thermal stability of pure PAMG compared to LPAM, the presence of TiCl₄ as a mineral material in PAMG structure decreases the thermal degradation onset temperature, considerably. Furthermore for LPAM and PAMG, majority of weight loss occurs in the second stage, but in PAMG with TiCl₄ the weight loss occurs mainly at the first stage. For more detailed investigation, residual materials were characterized by Fourier transform infrared spectroscopy and X-ray diffraction (XRD) techniques, attributing this trend to the presence of mineral materials in PAMG structure. XRD and transmission electron microscopy were also applied to confirm anatase crystalline structure and nanoscale distribution of the TiO₂ particles synthesized via PAMG method.     

A new evaluation measure for color image segmentation based on genetic programming approach

One of the greatest challenges while working on image segmentation algorithms is a comprehensive measure to evaluate their accuracy. Although there are some measures for doing this task, but they can consider only one aspect of segmentation in evaluation process. The performance of evaluation measures can be improved using a combination of single measures. However, combination of single measures does not always lead to an appropriate criterion. Besides its effectiveness, the efficiency of the new measure should be considered. In this paper, a new and combined evaluation measure based on genetic programming (GP) has been sought. Because of the nature of evolutionary approaches, the proposed approach allows nonlinear and linear combinations of other single evaluation measures and can search within many and different combinations of basic operators to find a good enough one. We have also proposed a new fitness function to make GP enable to search within search space effectively and efficiently. To test the method, Berkeley and Weizmann datasets besides several different experiments have been used. Experimental results demonstrate that the GP based approach is suitable for effective combination of single evaluation measures.