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1st International Conference on Technology Systems and Management (ICTSM 2011)

作者： Wangikar, Varsha Jain, Kavita Shah, Seema Lecturer KC Coll Engn Thana India Xavier Inst Engn Bombay Maharashtra India Vidyalankar Inst Technol Bombay Maharashtra India

ISBN: (纸本)9783642202087

grid computing is an emerging computing infrastructure which is a collection of computing resources connected by a network, to form a distributed system used for solving complex problems. This system tries to solve these problems or applications by allocating the idle computing resources over a network or the internet commonly known as the computational grid. In computational grid main emphasis is Oven on performance in terms of Execution time. Resources in computational grid are heterogeneous and are owned and managed by other organizations with different access policies. This can be achieved by Scheduling. Scheduling algorithms increases performance by reducing the execution time so Scheduling is an important issue. Scheduling is the decision process by which application components are assigned to available resources to optimize various performance metrics. Hence in this paper we have specifically focused on improving computational grid performance in terms of Execution time. We have first presented a concept of Scheduling in computational grid. Followed by detailed analysis of two scheduling strategies simulated by gridSim. Next we have proposed a dynamic Scheduling strategy based on this analysis. We are sure that this will improve the performance by reducing execution time.

关键词： grid Computing computational grid Scheduling gridSim etc

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Applying computational grid Technology to Power System

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International Conference on Information Technology for Manufacturing Systems (ITMS 2011)

作者： Luo, Fengji Dong, Zhaoyang Wan, Can Chen, Yingying Meng, Ke Wong, Kit-Po Hong Kong Polytech Univ Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783037851494

This paper proposes a computational grid platform for solving the large-scale power system applications. The platform is based on Globus Toolkit middleware and grid Way meta-scheduler. It can enable large-scale sharing of computational resources across institutional boundary. This paper first discusses the architecture and each component of the platform, and then the test bed is described. Finally, the test results of probabilistic load flow (PLF) by Monte-Carlo simulation are presented. The test results show that the computational grid system can provide comparable performance.

关键词： grid Computing computational grid power system probabilistic load flow (PLF) Monte-Carlo simulation

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An energy-efficient reliable grid scheduling model using NSGA-II

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ENGINEERING WITH COMPUTERS 2016年第3期32卷 355-376页

作者： Kaushik, Achal Vidyarthi, Deo Prakash Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Non-dominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multi-objective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.

关键词： computational grid Scheduling Reliability Load Energy consumption NSGA-II

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A Parallel Job Execution Time Estimation Approach Based on User Submission Patterns within computational grids

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2015年第3期43卷 440-454页

作者： Liang, Feng Liu, Yunzhen Liu, Hai Ma, Shilong Schnor, Bettina Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Univ Potsdam Inst Comp Sci D-14482 Potsdam Germany

Scheduling performance in computational grid can potentially benefit a lot from accurate execution time estimation for parallel jobs. Most existing approaches for the parallel job execution time estimation, however, require ample past job traces and the explicit correlations between the job execution time and the outer layout parameters such as the consumed processor numbers, the user-estimated execution time and the job ID, which are hard to obtain or reveal. This paper presents and evaluates a novel execution time estimation approach for parallel jobs, the user-behavior clustering for execution time estimation, which can give more accurate execution time estimation for parallel jobs through exploring the job similarity and revealing the user submission patterns. Experiment results show that compared to the state-of-art algorithms, our approach can improve the accuracy of the job execution time estimation up to 5.6 %, meanwhile the time that our approach spends on calculation can be reduced up to 3.8 %.

关键词： User submission pattern Parallel job execution time estimation computational grid

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HGASA: An Efficient Hybrid Technique for Optimizing Data Access in Dynamic Data grid 12th

HGASA: An Efficient Hybrid Technique for Optimizing Data Acc...

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12th International Conference on Distributed Computing and Internet Technology (ICDCIT)

作者： Grace, R. Kingsy Manimegalai, R. Sri Ramakrishna Engn Coll Coimbatore Tamil Nadu India Pk Coll Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9783319280349;9783319280332

grid computing uses computers that are distributed across various geographical locations in order to provide enormous computing power and massive storage. Scientific applications produce large quantity of sharable data which requires efficient handling and management. Replica selection is one of the data management techniques in grid computing and is used for selecting data from large volumes of distributed data. Replica selection is an interesting data access problem in data grid. Genetic Algorithms (GA) and Simulated Annealing (SA) are two popularly used evolutionary algorithms which are different in nature. In this paper, a hybrid approach which combines Genetic Algorithm with Simulated Annealing, namely, HGASA, is proposed to solve replica selection problem in data grid. The proposed algorithm, HGASA, considers security, availability of file, load balance and response time to improve the performance of the grid. gridSim simulator is used for evaluating the performance of the proposed algorithm. The results show that the proposed algorithm, HGASA, outperforms Genetic Algorithms (GA) by 9 % and Simulated Annealing (SA) by 21 % and Ant Colony Optimization (ACO) by 50 %.

关键词： Replica selection Data grid computational grid Genetic algorithm Simulated annealing

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A computational grid Platform for Large-Scale Power System Analysis

A Computational Grid Platform for Large-Scale Power System A...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Kit Po Wong Department of Electrical Engineering The Hong Kong Polytechnic University

This paper describes a computational grid platform for solving large-scale power system analysis tasks. The platform is based on Globus Toolkit middleware and gridWay Meta-scheduler. It can enable large-scale sharing of computational resources across institutional boundaries. This paper firstly discusses the architecture and each component of the platform, and then the test bed is described. Using Probabilistic Load Flow by Monte-Carlo simulation as the test case, the Internet latency is sampled to simulate the real grid system. The efficiency of the grid system for running the test case is observed. The test results show that the platform model can provide comparable performance for solving large-scale power system problems.

关键词： grid Computing computational grid power system probabilistic load flow (PLF) Monte-Carlo simulation (MCS)

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New Heuristic for Scheduling of Independent Tasks in computational grid

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INTERNATIONAL JOURNAL OF grid AND DISTRIBUTED COMPUTING 2011年第3期4卷 25-36页

作者： Chaturvedi, Anand K. Sahu, Rajendra ABV Indian Inst Informat Technol & Management Gwalior India

The Scheduling of tasks on heterogeneous grid resources is known to be a NP-complete problem;therefore, to get a near optimal solution within finite duration, heuristics/metaheuristics are used for task scheduling instead of exact optimization methods. In this paper, we proposed a new heuristic method for scheduling of independent tasks on heterogeneous grid resources and compared the result with ten other scheduling heuristics. Benchmark instances of Expected Time to Complete (ETC) model, suggested by Braun [3] are used to test the proposed heuristic. New heuristics give best Makespan as well as Flow-time values under much prevalent consistent resource and task heterogeneity conditions.

关键词： computational grid Scheduling Heuristic

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The effect of numerical diffusion and the influence of computational grid over gas-solid two-phase flow in a bubbling fluidized bed

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MATHEMATICAL AND COMPUTER MODELLING 2010年第9-10期52卷 1390-1402页

作者： de Souza Braun, Meire Pereira Mineto, Andreza Tangerino Navarro, Helio Aparecido Cabezas-Gomez, Luben da Silva, Renato Cesar Univ Sao Paulo Escola Engn Sao Carlos Dept Engn Mecan BR-13566590 Sao Carlos SP Brazil PUC MG Dept Engn Mecan BR-30535901 Belo Horizonte MG Brazil Univ Fed Mato Grosso do Sul Dept Ciencias Exatas BR-79620080 Tres Lagoas MS Brazil

The numerical diffusion effects appear due to the discretization process of the convective terms of the transport equations. This phenomenon takes place also in the numerical simulation of gas-solid two-phase flows in bubbling fluidized beds (BFB). In the present work a comparative analysis of the numerical results obtained using two interpolation schemes for convective terms, namely FOUP (First Order UPwind) and a high order scheme (Superbee) is presented. The equations are derived by considering the Eulerian-Eulerian gas-solid two-fluid model and the kinetic theory of granular flows (KTGF) for modeling solid phase constitutive equations. For that purpose the MFIX (Multiphase Flow with Interphase eXchanges) code developed at NETL (National Energy Technology Laboratory, US Department of Energy) is used. The numerical diffusion is analyzed by considering a single bubbling detachment and its hydrodynamic process in a two-dimensional BFB. The bubble shape is used as a metric for the description of the results. The influence of the computational grid is also analyzed. It is concluded that the Superbee scheme produces better results and this scheme is recommended for discretizations of the convective terms in coarse grids. The FOUP scheme can be used only in fine grids but it requires a high computational effort. In this study it is also verified that the analysis about estimating uncertainty in grid refinement can be applied in specific points of the grid when a monotonic convergence in time and space occurs. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gas-solid flows Bubbling fluidized beds Interpolation schemes computational grid

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Improving the Scheduler's Energy Saving Capability by Noting both Job and Resource Characteristics

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COMPUTER JOURNAL 2015年第6期58卷 1482-1493页

作者： Saadatfar, Hamid Deldari, Hossein Naghibzadeh, Mahmoud Ferdowsi Univ Mashhad Parallel & Distributed Proc Lab Dept Comp Engn Fac Engn Mashhad Khorasan Razavi Iran

Although the growth in the scale and complexity is the response of High Performance Computing (HPC) systems like computational grids to the ever-increasing demand for high processing capacity, it also makes these systems considerable energy consumers. In fact, high energy consumption is the new challenge in front of performance improvement of HPC systems and thus power management is now a necessity for them. One of the major components that can have a great role in the power-saving process is the scheduler. In this paper, a new power-aware scheduling algorithm is proposed by incorporating the characteristics of both job and resource into the jobmapping and ordering, and frequency-setting decision steps. In addition to the analytical study, the proposed scheduler has been evaluated based on results obtained from experiments in different resources heterogeneity levels and workload conditions. The results show the greater capability of the proposed scheduling algorithm in comparison with other related approaches.

关键词： computational grid power-aware scheduler job mapping frequency setting job ordering

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A hierarchical approach for job scheduling in grid computing based on resource prediction and meta-heuristic algorithms

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MULTIAGENT AND grid SYSTEMS 2015年第4期11卷 227-244页

作者： Goudarzi, Elahe Ravanmehr, Reza Islamic Azad Univ Cent Tehran Branch Dept Comp Engn Tehran Iran

The computational grids as a distributed system are hardware and software infrastructures that are capable of solving large-scale issues, and they use heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling is a critical and prominent issue in grid computing. An appropriate prediction method for allocating jobs to resources may significantly affect quality of service parameters. In this paper, a hierarchical approach is presented for job scheduling in computational grid utilizing a resource prediction method based on the scoring system. It is inspired by meta-heuristic algorithms in order to improve parameters such as makespan, load balancing and the rate of meeting deadlines. To evaluate the proposed method, gridSim toolkit is exploited. According to the simulation results and comparison with some recent well-known methods, this approach has been successful in improving the mentioned parameters.

关键词： computational grid scheduling quality of service heuristic algorithm gridSim

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