Performance Of Large-scale Systems Research Articles

Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity

Optimization on an industrial scale is a complex task that involves fine-tuning the performance of large-scale systems and applications to make them more efficient and effective. This process can be challenging due to the increasing volume of work, growing system complexity, and the need to maintain optimal performance. Due to the significant power required for compression and the high costs of reactant materials, optimizing low-density polyethylene (LDPE) production to provide maximum productivity with a reduction of energy cost is required. However, it is not a simple process because the optimization problem of the LDPE tubular reactor consists of conflicting objective functions. Multi-objective neural network algorithm (MONNA) is a metaheuristic optimization method that provides a versatile and robust approach for solving complex, contradictory targets and diverse optimization problems that do not rely on specific mathematical properties of the problem. It is inspired by the structure and information-processing capabilities of biological neural networks. MONNA iteratively proposes solutions, evaluates its performance, and adjusts its approach based on feedback, which avoids complex mathematical formulations. In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). The results show that the highest productivity, highest conversion, and lowest energy are 545.1 mil. RM/year, 0.314, and 0.672 mil. RM/year. The extreme points in the Pareto Front (PF) for various bi-objective situations provide practitioners with helpful information for selecting the best trade-off for the operational strategy. According to their preferences, decision-makers can use the resulting Pareto to decide on the most acceptable alternative. The decision variable plots show that both initiators in the reacting zone highly affected the optimal solution with the opposite action.

Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning

Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies are imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. We provide experimental measurements of the synaptic characteristics for a 28 nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies, and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data.

Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator

Simulation has become an indispensable technique for modelling and evaluating the performance of large-scale systems efficiently and at a relatively low cost. ElasticSearch (ES) is one of the most popular open source large-scale distributed data indexing systems worldwide. In this paper, we use the RECAP Discrete Event Simulator (DES) simulator, an extension of CloudSimPlus, to model and evaluate the performance of a real-world cloud-based ES deployment by an Irish small and medium-sized enterprise (SME), Opening.io. Following simulation experiments that explored how much query traffic the existing Opening.io architecture could cater for before performance degradation, a revised architecture was proposed, adding a new virtual machine in order to dissolve the bottleneck. The simulation results suggest that the proposed improved architecture can handle significantly larger query traffic (about 71% more) than the current architecture used by Opening.io. The results also suggest that the RECAP DES simulator is suitable for simulating ES systems and can help companies to understand their infrastructure bottlenecks under various traffic scenarios and inform optimisation and scalability decisions.

Differential-game for resource aware approximate optimal control of large-scale nonlinear systems with multiple players

In this paper, we propose a novel differential-game based neural network (NN) control architecture to solve an optimal control problem for a class of large-scale nonlinear systems involving N-players. We focus on optimizing the usage of the computational resources along with the system performance simultaneously. In particular, the N-players’ control policies are desired to be designed such that they cooperatively optimize the large-scale system performance, and the sampling intervals for each player are desired to reduce the frequency of feedback execution. To develop a unified design framework that achieves both these objectives, we propose an optimal control problem by integrating both the design requirements, which leads to a multi-player differential-game. A solution to this problem is numerically obtained by solving the associated Hamilton-Jacobi (HJ) equation using event-driven approximate dynamic programming (E-ADP) and artificial NNs online and forward-in-time. We employ the critic neural networks to approximate the solution to the HJ equation, i.e., the optimal value function, with aperiodically available feedback information. Using the NN approximated value function, we design the control policies and the sampling schemes. Finally, the event-driven N-player system is remodeled as a hybrid dynamical system with impulsive weight update rules for analyzing its stability and convergence properties. The closed-loop practical stability of the system and Zeno free behavior of the sampling scheme are demonstrated using the Lyapunov method. Simulation results using a numerical example are also included to substantiate the analytical results.

Scalability and Performance Analysis of SIP based Multimedia Services over Mission Critical Communication Systems

Various studies have suggested enhancing the performance of large-scale systems, such as mission critical communication systems (MCCSs). However, few have modelled and evaluated the performance of such systems in a way that targets overall system performance in real time. Moreover, it is not enough to define the Key Performance Indicators (KPIs) for a system without using them for system performance measurement and performance evaluation. The Session Initiation Protocol (SIP) and IP Multimedia Subsystem (IMS) both have a set of KPIs, such as the registration process delay, that can be used to measure and thus optimize overall system performance. This article articulates different options for system simulation and evaluation. The registration process affects performance and reflects the overall system performance. The article shows how the registration process is delayed and how the overall system scalability are negatively impacted by system overload.

Semi‐asynchronous approximate parallel DEVS simulation of web search engines

SummaryDiscrete Event System Specification (DEVS) is a formalism for the modeling and analysis of discrete event systems. Parallel DEVS (PDEVS) is an extension of DEVS for supporting Parallel and Discrete Event Simulation, which is a powerful tool for evaluating the performance of large scale systems. In this work, we propose an optimistic approximate and semi‐asynchronous parallel strategy. The level of optimism is efficiently managed throughout the simulation execution, and it is automatically adjusted based on the simulation evolution. Load balance and model partitioning is automatically made by means of an algorithm that takes advantage of the communication pattern of the simulated model. Our proposal is designed for Web search engines, which are complex and highly optimized systems devised to operate on large clusters of processors and dealing with dynamic and unpredictable user query bursts. The results show that our proposal is able to reduce both execution times and memory usage of standard optimistic simulations of Web search engine models, at the expense of small errors.

Fundamental Investigation of E-Coat Systems for Next Generation Corrosion Control

Electrodeposition of coatings is widely used in the auto industry to protect against corrosion. Increased fundamental understanding is needed to enhance the effectiveness of coatings while decreasing the environmental risks associated with surface treatments and coatings, as well as the processes used to form them. In addition, the incorporation of fundamental understanding into modeling tools will help to improve the quality of predictions and ensure that the desired level of corrosion protection is achieved. Fundamentally based models can also provide insight to guide the development and use of new types of environmentally friendly surface pretreatments. Key parameters of interest to the present study include the coulombic efficiency (mass deposited per charge), the coating resistivity, bath conductivity, the degree of neutralization and the extent of amine substitution. The influence of operating conditions such as temperature and flow on these parameters is also critical. In this study, experiments are performed to investigate the influence of factors such as the degree of neutralization and the extent of amine substitution on the size of resin particles in solution and on the processes that render the resin insoluble, leading to deposition. Models are used to describe and quantify critical mechanisms. The impact of key parameters on the local deposition rate and on the coulombic efficiency is demonstrated. The ability to determine the local deposition rate is critical to the performance of large-scale systems and to our ability to predict that performance for a variety of bath compositions, operating conditions, and surface pretreatments.

Advancing High Performance Computing solutions for neuroinformatics applications

Event Abstract Back to Event Advancing High Performance Computing solutions for neuroinformatics applications Andrey Semin1* 1 Intel, Germany Neuroinformatics applications for many years are among the most demanding for computation performance, and scientists foresee continued demands for large computational capacity for wide range of medical and life sciences applications. However there are number of challenges in software and hardware development that needed to be solved in order to provide scientists with scalable supercomputers capable to deliver high sustained performance for large-scale simulations. We first review the history of supercomputer hardware developments, and then outline trends highlighting limiting factors which designers foresee on the way of building large scale supercomputers, usually involved power and cooling requirements of the supercomputers, their size and cost. Though hardware difficulties can be overcome with continued technology development and we’ll likely see continued increase in the peak performance of large-scale systems, but it is important that these supercomputers continue deliver high sustained performance for challenging scientific applications. And this is where the software starts paying the bigger role. By software we understand not just the application itself, but the whole stack of software, starting with programming paradigm and language, spanning to middleware and system software. Many of the previously developed software paradigms and programming languages have not been designed to take the full advantage of hardware of the future. Thus many companies have started research and development of new programming languages and paradigms better suited for the supercomputers we’ll likely see in several years, so we devote significant part of discussion to the software aspects of supercomputing, and impacts that these software paradigm shifts have on software development practices. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Oral Presentation Topic: Workshop 4 - High performance computing and grid infrastructure for neuroinformatics applications Citation: Semin A (2019). Advancing High Performance Computing solutions for neuroinformatics applications. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.134 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Jun 2009; Published Online: 09 May 2019. * Correspondence: Andrey Semin, Intel, Munich, Germany, andrey.semin@intel.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrey Semin Google Andrey Semin Google Scholar Andrey Semin PubMed Andrey Semin Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Development and Ground Tests of a 100-Millinewton Hydrogen Peroxide Monopropellant Microthruster

3, with a throat diameter of 0.5-mm packing, with silver flake as the catalyst, and 92% hydrogen peroxide is adopted with a flow rate of 0:18 g=s. Measurements of the ignition delay at different catalyst-bed preheating temperatures are compared to define the operating parameters for practical applications. Test results show that the hydrogen peroxide microthruster gives a good c efficiency and a short ignition delay compared with the performance of large-scale systems. Under atmospheric pressure at sea level, the developed microthruster can produce 182 mN with a specific impulse of 101 s.

Modelling the performance of large-scale systems

Performance modelling can be used throughout the development, deployment and maintenance of system hardware and application software. In this work the authors illustrate three uses of performance modelling on large-scale systems: the verification of performance during system installation, the comparison of two large-scale systems, and the prediction of performance on possible future architectures. They detail how a performance model gave an expectation of the performance of ASCI Q, a 20Tflop system recently installed at Los Alamos. A comparison between ASCI Q and the Earth Simulator is also detailed, resulting in the sizing of an AlphaServer system that has the same performance as the Earth Simulator. The modelling approach is application centric. A detailed model is developed for each application of interest based on a static analysis of the code but parametrised in terms of its dynamic behaviour.

Analogue Simulation of Marine Power Transmission Systems

This paper shows the application of using distributed-lumped modelling techniques that can be used to investigate the dynamic performance of large scale systems that have pointwise and geometrically dispersed elements. As an example of this technique a marine transmission system is considered in which propulsion shaft is attached to a fixed pitch propeller. Owing to length of the shaft and relatively pointwise location of the propeller a distributed-lumped model of the arrangement is investigated and a z transform analogue simulation model is presented. Analysis and simulated response characteristics are presented.

Using Control Charts to Monitor the Performance of Large-Scale Systems

ABSTRACTThe initial goal of this project was to provide Alcan Rolled Products Company of Kingston, Ontario, with control charts to monitor its new Paint Line Three (PL3) aluminium coil coating facility. As the project progressed, however, it became apparent that the following factors made PL3 ill-suited to traditional statistical quality control methods: the inexact nature of the coil coating science, the level of process interdependence and complexity of the operation, and the economic and technical infeasibility of installing sophisticated measuring devices at all stages of the coating line.Consequently, an effort was made to model the entire system on a macroscopic, rather than a microscopic, level and to use the results of qualitative lab tests to make inferences about the paint line's process capability. To accomplish this, the length of scrap produced by every lot processed in a 1-year period was used to create a statistical model of the of the frequency and severity of the two most costly quality s...

The majorant Lyapunov equation: A nonnegative matrix equation for robust stability and performance of large scale systems

A new robust stability and performance analysis technique is developed. The approach involves replacing the state covariance by its block-norm matrix, i.e., the nonnegative matrix whose elements are the norms of subblocks of the covariance matrix partitioned according to subsystem dynamics. A bound (i.e., majorant) for the block-norm matrix is given by the majorant Lyapunov equation, a Lyapunov-type nonnegative matrix equation. Existence, uniqueness and computational tractability of solutions to the majorant Lyapunov equation are shown to be completely characterized in terms of M matrices. As an example, a pair of nominally uncoupled oscillators with uncertain coupling is considered. The majorant Lyapunov equation shows that the range of nondestabilizing couplings is proportional to the frequency separation between the oscillators, a result not predictable from quadratic or vector Lyapunov functions.