To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution ***,the distributed machine learning is difficult to train due to that the correspondi...
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To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution ***,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory *** overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training *** validate the claims,we have conducted several experiments on multiple classical *** show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
This paper investigates the use of ATM for cluster-based computing. The need for a native ATM API is discussed as well as the performance of message passing libraries (MPL) that are written to use such an API to explo...
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This paper investigates the use of ATM for cluster-based computing. The need for a native ATM API is discussed as well as the performance of message passing libraries (MPL) that are written to use such an API to exploit the advantages of a high-speed network for cluster-based computing. The MPLs offer a standard interface, such as PVM or MPI, and interoperate with existing TCP/IP- and UDP/IP-based versions in addition to the ATM API environment. The interoperability extensions made to two MPLs, MPI and Prowess, which allow a hybrid environment of both ATM and TCP-based legacy network technology will be described. Shared object space (SOS), an extension to the MPLs, is described that helps support the geographically distributed computing (GDC) environment through latency hiding. It allows a user to develop applications in a shared memory type of environment. The native ATM API which supports cluster-based computing is described in this paper. This API provides a reliable transport interface to the MPL which has been optimized for an ATM environment. The transport protocol is a low-state design that optimizes the performance based on the available bandwidth, buffer constraints, propagation delay characteristics and security requirements of a particular connection. (C) 1998 Elsevier Science B.V.
SimSET is a package for simulation of emission tomography data sets. Condor is a popular distributed computing environment. Simple C/C++ applications and shell scripts are presented which allow the execution of SimSET...
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SimSET is a package for simulation of emission tomography data sets. Condor is a popular distributed computing environment. Simple C/C++ applications and shell scripts are presented which allow the execution of SimSET on the Condor environment. This is accomplished without any modification to SimSET by executing multiple instances and using its combinebin utility. This enables research facilities without dedicated parallel computing systems to utilize the idle cycles of desktop workstations to greatly reduce the run times of their SimSET simulations. The necessary steps to implement this approach in other environments are presented along with sample results.
Generally, distributed computing environments have a number of properties that significantly complicate unification of processes of computing control such as their scheduling and resource allocation. Such properties i...
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Generally, distributed computing environments have a number of properties that significantly complicate unification of processes of computing control such as their scheduling and resource allocation. Such properties include, for instance, functional-organizing heterogeneity, dynamics and non-complete description of integrated resources;diversity of the spectrum of problems solved using these resources;different classes of users pursuing their own goals and tasks while working with the computing system. Analysis of world trends in this research domain allows stating that solving these problems is directly connected with intellectualization of middleware of distributed computing environments with decentralized control. In this work, a multiagent approach to controlling distributed computing in a cluster Grid system, which is a virtual software-hardware infrastructure with its nodes being computer clusters, is presented. Characteristics of such system are considered. Architecture and principles of operation of the multiagent system are given. A number of important technological features of the proposed approach is singled out. The multiagent system is developed using JADE tools (Java Agent DEvelopment framework). The results of imitation simulation of the processes of operation of the system of agents being developed are given.
Compared with Computer Aided Design (CAD) to use computer graphics technologies to describe geometric information for the product design, many new methodologies and systems have been developed in the past decades in p...
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Compared with Computer Aided Design (CAD) to use computer graphics technologies to describe geometric information for the product design, many new methodologies and systems have been developed in the past decades in product modeling to extend the functions of the traditional CAD systems. In the product representation aspect, in addition to the design solution usually modeled by geometric descriptions, modeling of product knowledge has been widely investigated to describe the rationale for creation of the product solution. In the information technology aspect, in addition to the traditional computing tools such as computer programming languages and object oriented design, distributed computing technologies particularly Internet, web and cloud computing tools have been widely employed to implement product modeling systems. In the product development process aspect, in addition to the design solution, other product lifecycle aspects such as manufacturing, maintenance and service have been modeled and integrated into the same environment. In this literature review, the recent advances of research on product modeling in product representation, information technologies and product development process are investigated from these three new perspectives: knowledge, distributed computing, and product lifecycle. In addition, traditional methodologies and future challenges in computer-based product modeling are also discussed. (C) 2016 Elsevier B.V. All rights reserved.
Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data c...
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Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor to gather data from visitors of the hotels and the analysis was supported with an optimization approach through the deep autoencoder network. According to the data density gathered from the structure developed with distributed computing and the API software in C# language, server group numbers were increased to list the most affordable hotel in the desired times. Thanks to the developed architecture and software, response times of the servers were significantly reduced. In detail, it was seen that the HAProxy responded 11 times faster than NetScaler as the new architecture responded 1160 times faster than the old one. Also, the HashSet system in the newly developed architecture responded 18 times faster than the List system and as general, the new architecture was found to be 9 times faster than the old architecture.
The problem of optimally balancing a given workload among identical processors with preassigned loads is considered. An improved algorithm which solves the problem in O(n) time under both cases-the case where perfect ...
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The problem of optimally balancing a given workload among identical processors with preassigned loads is considered. An improved algorithm which solves the problem in O(n) time under both cases-the case where perfect load balancing is possible and the case where only approximate load balancing is possible-are presented. Properties used to develop the algorithm are also provided. Finally, it is shown bow this algorithm can be modified for the case in which the processing rates are not equal.
Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve effici...
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Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for nonlinear plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.
Data placement is an essential part of today's distributed applications since moving the data close to the application has many benefits. The increasing data requirements of both scientific and commercial applicat...
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Data placement is an essential part of today's distributed applications since moving the data close to the application has many benefits. The increasing data requirements of both scientific and commercial applications, and collaborative access to these data make it even more important. In the current approach, data placement is regarded as a side affect of computation. Our goal is to make data placement a first class citizen in distributed computing systems just like the computational jobs. They will be queued, scheduled, monitored, managed, and even checkpointed. Since data placement jobs have different characteristics than computational jobs, they cannot be treated in the exact same way as computational jobs. For this purpose, we are proposing a framework which can be considered as a "data placement subsystem" for distributed computing systems, similar to the I/O subsystem in operating systems. This framework includes a specialized scheduler for data placement, a high level planner aware of data placement jobs, a resource broker/policy enforcer and some optimization tools. Our system can perform reliable and efficient data placement, it can recover from all kinds of failures without any human intervention, and it can dynamically adapt to the environment at the execution time. (c) 2005 Elsevier Inc.
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