a b s t r a c tAn efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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The accidents happening to buildings and other human facilitation sectors due to poor water supply pipelining system is a random phenomenon, but an efficient estimation system can help to escape from such accidents. Such a system can be useful in assisting the caretakers to take the initiative measures to avoid the occurrence of the accidents or at least reduce the associated risk. In this paper, we target this issue by proposing a water supply pipelines risk estimation methodology using feed forward backpropagation neural network (FFBPNN). For validation and performance evaluation, real data of water supply pipelines collected in Seoul, Republic of South Korea from 1987 to 2010 is used. A comprehensive analysis is performed in order to get reasonable results with both original and preprocessed input data. Pre-processing consists of two steps: data normalization and statistical moments computation. Statistical moments are mean, variance, kurtosis and skewness. Significant improvement in prediction accuracy is observed with data preprocessing in terms of selected performance metrics, such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean squared error (RMSE).