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computational grid scheduling architecture using MapReduce model-based non-dominated sorting genetic algorithm

SOFT COMPUTING 2019年第18期23卷 8335-8347页

作者： Rajeswari, D. Prakash, M. Suresh, J. SRM Inst Sci & Technol Dept Comp Sci & Engn Kancheepuram Tamil Nadu India CK Coll Engn & Technol Dept Comp Sci & Engn Cuddalore Tamil Nadu India

computational grid (CG) environment has a group of autonomous dissimilar distributed computing systems to provide service to user tasks. To attain the auspicious usefulness of CG resources, basically best scheduling algorithms are important. The CG service users are very cautious in time needed for task completion. To meet multiple requirements of the user, the job allocation problem in CG is designed as a multi-objective problem. Evolutionary algorithms have an efficient meta-heuristic technique for optimization problem solving. This work introduces a MapReduce model for non-dominated sorting genetic algorithm (NSGA-II) for independent job scheduling in a CG. This research work attempts to find the optimal schedules by considering makespan and flowtime minimization. A fuzzy membership function is used to analyze the efficiency of the schedule. Set of benchmark instances is used to test the algorithm. Experimental results show that MapReduce model-based NSGA-II generated finer solutions in less time than the MapReduce model-based weighted sum multi-objective genetic algorithm (WMOGA) which is also implemented in this paper.

关键词： computational grid MapReduce Multi-objective Task scheduling Non-dominated sorting Genetic algorithm

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computational grid based Neural Network Ensemble Learning Platform and Its Application

Computational Grid based Neural Network Ensemble Learning Pl...

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3rd International Conference on Management of e-Commerce and e-Government

作者： Liu, Yue Teng, Zaixia Zhang, Bofeng Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

ISBN: (纸本)9780769537788

Neural network ensemble is a recently developed technology, which trains a few of neural networks and then combines their prediction results. The generalization ability of an ensemble can be much better than that of a single learner. However, it is time-consuming to train so many neural networks. In order to overcome this disadvantage, MPI (Message Passing Interface) technique was employed to the Parallel Implementation of the RBF neural network ensemble, which is named PDNNE in our previous work [1]. In this paper, a unified parallelization strategy based on PDNNE is proposed to reduce the complexity and improve the efficiency of the ensemble's construction for the bagging-based neural network ensembles (UPDNNE). And then a neural network ensemble learning platform (NNELP) based on the Shanghai University E-grid Platform is designed and implemented to make the user conveniently and efficiently apply the ensemble methods to solve the real-world problems furthermore. The experiments on UCI datasets and the time sequence forecasting of strong earthquakes in China's mainland demonstrate the efficiency of the NNELP.

关键词： Neural network ensemble computational grid Earthquake Prediction

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A Green Energy Model for Resource Allocation in computational grid

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COMPUTER JOURNAL 2015年第7期58卷 1530-1547页

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

A computational grid helps in faster execution for compute-intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in a computational grid. Most often, the green energy aspect, where one tries for better energy utilization, is ignored while allocating grid resources to jobs. The present work tries to optimize energy to make it a green energy model for resource allocation in a computational grid. It explores how effectively the jobs submitted to the grid can be executed, making no compromise on other expected characteristic parameters, and at the same time makes optimal use of energy. The proposed green energy model has been experimentally verified by its simulation. The result reveals the benefits and gives a better insight for effective resource allocation.

关键词： computational grid green energy resource allocation DVFS DPM quality of service

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Integration of a computational grid and virtual geographic environment to facilitate air pollution simulation

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COMPUTERS & GEOSCIENCES 2013年 54卷 184-195页

作者： Xu, Bingli Lin, Hui Gong, Jianhua Tang, Sammy Hu, Ya Nasser, Ibrahim Abdoul Jing, Tao Acad Armored Forces Engn Dept Informat Engn Beijing 100072 Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Shatin Hong Kong Peoples R China Chinese Acad Sci Inst Remote Sensing Applicat State Key Lab Remote Sensing Sci Beijing 100110 Peoples R China Chinese Univ Hong Kong Informat Technol Serv Ctr Shatin Hong Kong Peoples R China Southwest Jiaotong Univ Sch Civil Engn Dept Surveying Engn Chengdu 610031 Peoples R China

Air pollution, which is a global environmental problem, has been the hot research area among the scientists in the geoscience community. Air pollution simulation is of low-efficiency caused by the computation-intensive models, such as MM5 or WRF, and the complicated and unfriendly user interface. These issues are addressed in this paper by integrating computational grid and virtual geographic environment (VGE). The computational grid is employed to improve the computation efficiency of air pollution models. The VGE is used as a straightforward and easy to use interface to navigate the air pollution modeling workflow and improve the operational efficiencies of the models with respect to initiation, computation, and output visualization. On the aspect of technique implementation, this paper designs a framework and addresses the methodologies of the integration of computational grid and VGE. The prototype system, which integrates the computation grid of the Chinese University of Hong Kong (CUgrid) and a VGE to facilitate air pollution simulation based on the Mesoscale Model Version 5 (MM5), was developed. Based on the prototype system, a case was tested and the results indicate that the efficiencies of air pollution simulation on the model computation and workflow operation based on MM5 are increased significantly. This success also proves the reasonability of our general contribution of integrating computational grid and VGE to facilitate air pollution simulation. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： computational grid Virtual geographic environment Air pollution simulation

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A distributed utility-based two level market solution for optimal resource scheduling in computational grid

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PARALLEL COMPUTING 2005年第3-4期31卷 332-351页

作者： Li, CL Li, LY Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Peoples R China

This paper investigates the interactions between agents representing users, services and resources to solve resource scheduling optimization in computational grid. In order to reduce the computational complexity, we further decompose the grid resource allocation optimization into subproblems: grid user agent-grid service agent in service market and grid service agent-grid resource agent in resource market. Two-level market converges to its optimal points;a globally optimal point is achieved. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the service market level optimization and resource market level optimization. It demonstrates a practical approach to market responsive resource pricing that can benefit grid providers and users alike. The paper presents two-level market grid resource pricing that is an iterative algorithm used to perform optimal resource allocation. The experiment shows that two-level market based resource pricing scheme outperforms one level market scheme in terms of task completion time and resource allocation efficiency. (c) 2005 Elsevier B.V. All rights reserved.

关键词： two-level market computational grid grid agent resource scheduling

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A novel scheduling model for computational grid using quantum genetic algorithm

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JOURNAL OF SUPERCOMPUTING 2013年第2期65卷 742-770页

作者： Prakash, Shiv Vidyarthi, Deo Prakash Jawaharlal Nehru Univ New Delhi 110067 India

The computational grid (CG) provides a wide distributed platform for high end computing intensive applications. Scheduling on computational grid is known to be NP-Hard problem and requires an efficient solution. Recently, quantum inspired computing has been introduced in the literature to solve such a complex combinatorial optimization problem efficiently. Combination of Genetic Algorithm (GA) and quantum concept evolves a new meta-heuristic technique known as Quantum Genetic Algorithms (QGA). QGA is a search procedure based on evolutionary computation and Quantum Computing (QC). This paper proposes a novel technique of scheduling in computational grid using QGA. The work simulates the model to study its performance. It also makes a comparative study with a GA-based scheduling model. Simulation results reveal the effectiveness of the model.

关键词： Scheduling computational grid Quantum computing Quantum genetic algorithms Arrival rate Makespan

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A Load Distribution Based Resource Allocation Strategy for Bag of Tasks (BoT) in computational grid Environment

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期135卷 47-80页

作者： Sheikh, Sophiya Shahid, Mohammad Sambare, Manas Haidri, Raza Abbas Prakash, Shiv Lovely Profess Univ Sch Comp Applicat Phagwara 144411 Punjab India Aligarh Muslim Univ Dept Commerce Aligarh 202001 Uttar Pradesh India FLAME Univ Dept Data Sci Pune 412115 Maharastra India Khwaja Moinuddin Chishti Language Univ Dept Comp Sci & Informat Technol Lucknow Uttar Pradesh India Univ Allahabad Dept Elect & Commun Allahabad Uttar Pradesh India

In the ever-evolving landscape of computational grid systems, the meticulous selection of resources tailored to specific tasks is a formidable challenge. This paper introduces an efficient load distribution strategy known as Load Distribution Based Resource Allocation (LDRA), one of the foremost goals is to allocate resources to gain enhanced resource utilization and also try to achieve least possible execution time to fulfil the need for grid systems. A comprehensive performance evaluation unfolds to elevate grid efficiency, pitting LDRA against existing heuristics using the ETC Simulation Benchmark. The study expands further on the real-world dataset from the Gaia Cluster Configurations (https://***/systems/gaia/) to verify its significance in the real environment. The LDRA algorithm emerges with superior performance when compared to state-of-the-art such as Max-Min, Opportunistic Load Balancing, AlgHybrid_LB, and Resource Aware Load Balancing for resource utilization, makespan, flowtime, and energy efficiency in the majority of the cases in experimental evaluation. In some cases, the experimental results show that LDRA's usage of the grid resources is remarkable, reaching over 99% in four cases and approaching 98% in two cases of the ETC simulation benchmark. These accomplishments are further mirrored in the evaluation against real datasets, where LDRA's performance among peers is nothing short of exemplary in the cases under study.

关键词： computational grid Resource Allocation Scheduling Load Distribution Scheme Resource Utilization

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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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Joint QoS optimization for layered computational grid

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INFORMATION SCIENCES 2007年第15期177卷 3038-3059页

作者： Li Chunlin Li Layuan Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Peoples R China

Many existing grid resource allocation and scheduling algorithms mainly focus on isolated layers of the grid architecture. The inflexibility of the strict layering structure results in an inefficient utilization of the grid resources. This paper takes a system view of the computational grid and aims to jointly optimize global QoS by adopting cross-layer design. Cross-layer design is based on information exchange and joint optimization among multiple grid layers. Parameters from different layers are provided to a cross-layer optimizer, which selects the values of the layer specific parameters maximizing joint global QoS. The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer. In simulation part, we compare the performance of the global joint QoS optimization approach with application layer local optimization and resource layer local optimization approach, respectively. (c) 2007 Elsevier Inc. All rights reserved.

关键词： joint QoS optimization computational grid layered architecture decomposition

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Multiple QoS modeling and algorithm in computational grid

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Journal of Systems Engineering and Electronics 2007年第2期18卷 412-417页

作者： Li Chunlin Feng Meilai Li Layuan Dept. of Computer Science Wuhan Univ. of Technology Wuhan 430063 P. R. China

Multiple QoS modeling and algorithm in grid system is considered. grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.

关键词： QoS modeling computational grid Scheduling algorithm.

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