Predicting I/O performance in HPC using artificial neural networks

The prediction of file access times is an important part for the modeling of supercomputer's storage systems. These models can be used to develop analysis tools which support the users to integrate efficient I/O behavior. In this paper, we analyze and predict the access times of a Lustre file system from the client perspective. Therefore, we measure file access times in various test series and developed different models for predicting access times. The evaluation shows that in models utilizing artificial neural networks the average prediciton error is about 30% smaller than in linear models. A phenomenon in the distribution of file access times is of particular interest: File accesses with identical parameters show several typical access times.The typical access times usually differ by orders of magnitude and can be explained with a different processing of the file accesses in the storage system - an alternative I/O path. We investigate a method to automatically determine the alternative I/O path and quantify the significance of knowledge about the internal processing. It is shown that the prediction error is improved significantly with this approach

Network and Parallel Computing: IFIP International Conference, NPC 2010, Zhengzhou, China, September 13-15, 2010, Proceedings

International audienceBook Front Matter of LNCS 628

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Server Virtualization Using Kernel-based Virtual Machine (KVM)

Suomi on pienten laitesalien maa. Monella yrityksellä on omat laitesalinsa, joissa ajetaan suurta osaa organisaation tarvitsemasta IT – infrastruktuurista. Usein näiden laitesalien ongelmana on ahtaus, energian kulutus, jäähdytys ja turvallisuus. Ne muodostavat myös kuluerän, jota on vaikea yksilöidä tiettyyn projektiin tai toimintoon kuuluvaksi. Virtualisoinnista on tullut vakioratkaisu moniin näistä ongelmista. Palvelinvirtualisoinnin pitkät perinteet ulottuvat aina 1960 – luvulle IBM:n keskustietokoneisiin asti. Virtualisointi on tänä aikana kuitenkin muuttunut oleellisesti. Siitä on kasvanut perusta, jonka varaan pilvipalvelut on rakennettu. Tämä työ käy läpi virtualisoinnin perusteita ja sen monia muotoja, sekä esittelee keskeiset virtualisoinnissa käytettävät tekniset menetelmät. Se kuvaa virtualisoinnin tarjoamia etuja, haasteita ja mahdollisia ongelmakohtia. Työ esittelee avoimeen lähdekoodin perustuvan Kernel-based Virtual Machine – sovelluspaketin, kuvaa sen virtualisointiytimen arkkitehtuurin ja avaa sen toimintaa. Samalla perehdytään KVM:n käyttämiin tekniikoihin, joilla parannetaan virtuaalikoneiden suorituskykyä ja tehostetaan niiden resurssien käyttöä. Työssä demonstroidaan KVM – virtualisointialustan asennus ja konfigurointi. Samalla kuvataan lukijalle, miten virtuaalikoneet määritellään ja mitä nämä määritykset pitävät sisällään. Työssä ajetaan joukko mittauksia, jotka kohdistuvat virtuaalikoneissa käytettäviin prosessoreihin, verkkolaitteisiin ja levyohjaimiin. Mittaustulokset analysoidaan ja niitä verrataan muiden julkaisujen vastaaviin tuloksiin

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings