Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

Predictive and distributed routing balancing (PR-DRB): high speed interconnection networks

Current parallel applications running on clusters require the use of an interconnection network to perform communications among all computing nodes available. Imbalance of communications can produce network congestion, reducing throughput and increasing latency, degrading the overall system performance. On the other hand, parallel applications running on these networks posses representative stages which allow their characterization, as well as repetitive behavior that can be identified on the basis of this characterization. This work presents the Predictive and Distributed Routing Balancing (PR-DRB), a new method developed to gradually control network congestion, based on paths expansion, traffic distribution and effective traffic load, in order to maintain low latency values. PR-DRB monitors messages latencies on intermediate routers, makes decisions about alternative paths and record communication pattern information encountered during congestion situation. Based on the concept of applications repetitiveness, best solution recorded are reapplied when saved communication pattern re-appears. Traffic congestion experiments were conducted in order to evaluate the performance of the method, and improvements were observed.

An anonymous reputation mechanism for cloud computing networks using volunteer resources

One of the major problems when using non-dedicated volunteer resources in adistributed network is the high volatility of these hosts since they can go offlineor become unavailable at any time without control. Furthermore, the use ofvolunteer resources implies some security issues due to the fact that they aregenerally anonymous entities which we know nothing about. So, how to trustin someone we do not know?.Over the last years an important number of reputation-based trust solutionshave been designed to evaluate the participants' behavior in a system.However, most of these solutions are addressed to P2P and ad-hoc mobilenetworks that may not fit well with other kinds of distributed systems thatcould take advantage of volunteer resources as recent cloud computinginfrastructures.In this paper we propose a first approach to design an anonymous reputationmechanism for CoDeS [1], a middleware for building fogs where deployingservices using volunteer resources. The participants are reputation clients(RC), a reputation authority (RA) and a certification authority (CA). Users needa valid public key certificate from the CA to register to the RA and obtain thedata needed to participate into the system, as now an opaque identifier thatwe call here pseudonym and an initial reputation value that users provide toother users when interacting together. The mechanism prevents not only themanipulation of the provided reputation values but also any disclosure of theusers' identities to any other users or authorities so the anonymity isguaranteed.

Distributed all-atom simulations of intrinsically unstructured proteins

The Computational Biophysics Group at the Universitat Pompeu Fabra (GRIB-UPF) hosts two unique computational resources dedicated to the execution of large scale molecular dynamics (MD) simulations: (a) the ACMD molecular-dynamics software, used on standard personal computers with graphical processing units (GPUs); and (b) the GPUGRID. net computing network, supported by users distributed worldwide that volunteer GPUs for biomedical research. We leveraged these resources and developed studies, protocols and open-source software to elucidate energetics and pathways of a number of biomolecular systems, with a special focus on flexible proteins with many degrees of freedom. First, we characterized ion permeation through the bactericidal model protein Gramicidin A conducting one of the largest studies to date with the steered MD biasing methodology. Next, we addressed an open problem in structural biology, the determination of drug-protein association kinetics; we reconstructed the binding free energy, association, and dissaciociation rates of a drug like model system through a spatial decomposition and a Makov-chain analysis. The work was published in the Proceedings of the National Academy of Sciences and become one of the few landmark papers elucidating a ligand-binding pathway. Furthermore, we investigated the unstructured Kinase Inducible Domain (KID), a 28-peptide central to signalling and transcriptional response; the kinetics of this challenging system was modelled with a Markovian approach in collaboration with Frank Noe’s group at the Freie University of Berlin. The impact of the funding includes three peer-reviewed publication on high-impact journals; three more papers under review; four MD analysis components, released as open-source software; MD protocols; didactic material, and code for the hosting group.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

On the computational security of a distributed key distribution scheme

In a distributed key distribution scheme, a set of servers helps a set of users in a group to securely obtain a common key. Security means that an adversary who corrupts some servers and some users has no information about the key of a noncorrupted group. In this work, we formalize the security analysis of one such scheme which was not considered in the original proposal. We prove the scheme is secure in the random oracle model, assuming that the Decisional Diffie-Hellman (DDH) problem is hard to solve. We also detail a possible modification of that scheme and the one in which allows us to prove the security of the schemes without assuming that a specific hash function behaves as a random oracle. As usual, this improvement in the security of the schemes is at the cost of an efficiency loss.

The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods.

The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205-217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692-1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

Learning in Product Development Projects - Responding to challenges in global industrial company

Verkostoitunut kansainvälinen tuotekehitys on tärkeä osa menestystä nykypäivän muuttuvassa yritysmaailmassa. Toimintojen tehostamiseksi myös projektitoiminnot on sopeutettava kansainväliseen toimintaympäristöön. Kilpailukyvyn säilyttämiseksi projektitoimintoja on lisäksi jatkuvasti tehostettava. Yhtenäkeinona nähdään projektioppiminen, jota voidaan edistää monin eri tavoin. Tässätyössä keskitytään projektitiedonhallinnan kehittämisen tuomiin oppimismahdollisuuksiin. Kirjallisuudessa kerrotaan, että projektitiedon jakaminen ja sen hyödyntäminen seuraavissa projekteissa on eräs projektioppimisen edellytyksistä. Tämäon otettu keskeiseksi näkökulmaksi tässä tutkimuksessa. Lisäksi tutkimusalueen rajaamiseksi työ tarkastelee erityisesti projektioppimista kansainvälisten tuotekehitysprojektien välillä. Työn tavoitteena on esitellä keskeisiä projektioppimisen haasteita ja etsiä konkreettinen ratkaisu vastaamaan näihin haasteisiin. Tuotekehitystoiminnot ja kansainvälinen hajautettu projektiorganisaatio kohtaavat lisäksi erityisiä haasteita, kuten tiedon hajautuneisuus, projektihenkilöstön vaihtuvuus, tiedon luottamuksellisuus ja maantieteelliset haasteet (esim. aikavyöhykkeet ja toimipisteen sijainti). Nämä erityishaasteet on otettu huomioon ratkaisua etsittäessä. Haasteisiin päädyttiin vastaamaan tietotekniikkapohjaisella ratkaisulla, joka suunniteltiin erityisesti huomioiden esimerkkiorganisaation tarpeet ja haasteet. Työssä tarkastellaan suunnitellun ratkaisun vaikutusta projektioppimiseen ja kuinka se vastaa havaittuihin haasteisiin. Tuloksissa huomattiin, että projektioppimista tapahtui, vaikka oppimista oli vaikea suoranaisesti huomata tutkimusorganisaation jäsenten keskuudessa. Projektioppimista voidaan kuitenkin sanoa tapahtuvan, jos projektitieto on helposti koko projektiryhmän saatavilla ja se on hyvin järjesteltyä. Muun muassa nämä ehdot täyttyivät. Projektioppiminen nähdään yleisesti haastavana kehitysalueena esimerkkiorganisaatiossa. Suuri osa tietämyksestä on niin sanottua hiljaistatietoa, jota on hankala tai mahdoton saattaa kirjalliseen muotoon. Näin olleen tiedon siirtäminen jää suurelta osin henkilökohtaisen vuorovaikutuksen varaan. Siitä huolimatta projektioppimista on mahdollista kehittää erilaisin toimintamallein ja menetelmin. Kehitys vaatii kuitenkin resursseja, pitkäjänteisyyttä ja aikaa. Monet muutokset voivat vaatia myös organisaatiokulttuurin muutoksen ja vaikuttamista organisaation jäseniin. Motivaatio, positiiviset mielikuvat ja selkeät strategiset tavoitteet luovat vakaan pohjan projektioppimisen kehittämiselle.

Data-parallel computation in parallel and distributed environments

The past few decades have seen a considerable increase in the number of parallel and distributed systems. With the development of more complex applications, the need for more powerful systems has emerged and various parallel and distributed environments have been designed and implemented. Each of the environments, including hardware and software, has unique strengths and weaknesses. There is no single parallel environment that can be identified as the best environment for all applications with respect to hardware and software properties. The main goal of this thesis is to provide a novel way of performing data-parallel computation in parallel and distributed environments by utilizing the best characteristics of difference aspects of parallel computing. For the purpose of this thesis, three aspects of parallel computing were identified and studied. First, three parallel environments (shared memory, distributed memory, and a network of workstations) are evaluated to quantify theirsuitability for different parallel applications. Due to the parallel and distributed nature of the environments, networks connecting the processors in these environments were investigated with respect to their performance characteristics. Second, scheduling algorithms are studied in order to make them more efficient and effective. A concept of application-specific information scheduling is introduced. The application- specific information is data about the workload extractedfrom an application, which is provided to a scheduling algorithm. Three scheduling algorithms are enhanced to utilize the application-specific information to further refine their scheduling properties. A more accurate description of the workload is especially important in cases where the workunits are heterogeneous and the parallel environment is heterogeneous and/or non-dedicated. The results obtained show that the additional information regarding the workload has a positive impact on the performance of applications. Third, a programming paradigm for networks of symmetric multiprocessor (SMP) workstations is introduced. The MPIT programming paradigm incorporates the Message Passing Interface (MPI) with threads to provide a methodology to write parallel applications that efficiently utilize the available resources and minimize the overhead. The MPIT allows for communication and computation to overlap by deploying a dedicated thread for communication. Furthermore, the programming paradigm implements an application-specific scheduling algorithm. The scheduling algorithm is executed by the communication thread. Thus, the scheduling does not affect the execution of the parallel application. Performance results achieved from the MPIT show that considerable improvements over conventional MPI applications are achieved.

Improving distributed simulation in a workstation environment

Technological development brings more and more complex systems to the consumer markets. The time required for bringing a new product to market is crucial for the competitive edge of a company. Simulation is used as a tool to model these products and their operation before actual live systems are built. The complexity of these systems can easily require large amounts of memory and computing power. Distributed simulation can be used to meet these demands. Distributed simulation has its problems. Diworse, a distributed simulation environment, was used in this study to analyze the different factors that affect the time required for the simulation of a system. Examples of these factors are the simulation algorithm, communication protocols, partitioning of the problem, distributionof the problem, capabilities of the computing and communications equipment and the external load. Offices offer vast amounts of unused capabilities in the formof idle workstations. The use of this computing power for distributed simulation requires the simulation to adapt to a changing load situation. This requires all or part of the simulation work to be removed from a workstation when the owner wishes to use the workstation again. If load balancing is not performed, the simulation suffers from the workstation's reduced performance, which also hampers the owner's work. Operation of load balancing in Diworse is studied and it is shown to perform better than no load balancing, as well as which different approaches for load balancing are discussed.

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.

How are riparian plants distributed along the riverbank topographic gradient in Mediterranean rivers? Application to minimally altered river stretches in Southern Spain.

Species structure and composition in Mediterranean riparian forests are determined by hydrological features, longitudinal zonation, and riverbank topography. This study assesses the distribution of four native riparian plants along the riverbank topographic gradient in three river stretches in southern Spain, with special emphasis on the occupation of adult and young feet of each species. The studied stretches suffered minimal human disturbances, displayed semi-arid conditions, and had wide riparian areas to allow the development of the target species: black alder (Alnus glutinosa), salvia leaf willow (Salix salviifolia), narrow-leafed ash (Fraxinus angustifolia), and oleander (Nerium oleander). Thalweg height was used to define the riverbank topographic gradient. The results showed a preferential zone for black alder and salvia leaf willow in the range of 0-150 cm from the channel thalweg, with adult alders and willows being more common between 51 and 150 cm and young alders being more common under 50 cm. Conversely, narrow-leafed ash and oleander were much more frequent, and showed greater development, in the ranges of 151-200 cm and 201-250 cm, respectively, whereas the young feet of both species covered the entire topographic range. Adult feet of the four species were spatially segregated along the riverbank topographic gradient, indicating their differential ability to cope with water stress from the non-tolerant alders and willows to more tolerant narrow-leafed ash trees and oleanders. Young feet, however, showed a strategy more closely linked to the initial availability of colonisation sites within riparian areas to the dispersion strategy of each species and to the distribution of adult feet. In Mediterranean areas, where riparian management has traditionally faced great challenges, the incorporation of species preferences along riverbank gradients could improve the performance of restoration projects.

Evaluating the success of software development projects in Russia, Ukraine, and Belarus

The size and complexity of projects in the software development are growing very fast. At the same time, the proportion of successful projects is still quite low according to the previous research. Although almost every project's team knows main areas of responsibility which would help to finish project on time and on budget, this knowledge is rarely used in practice. So it is important to evaluate the success of existing software development projects and to suggest a method for evaluating success chances which can be used in the software development projects. The main aim of this study is to evaluate the success of projects in the selected geographical region (Russia-Ukraine-Belarus). The second aim is to compare existing models of success prediction and to determine their strengths and weaknesses. Research was done as an empirical study. A survey with structured forms and theme-based interviews were used as the data collection methods. The information gathering was done in two stages. At the first stage, project manager or someone with similar responsibilities answered the questions over Internet. At the second stage, the participant was interviewed; his or her answers were discussed and refined. It made possible to get accurate information about each project and to avoid errors. It was found out that there are many problems in the software development projects. These problems are widely known and were discussed in literature many times. The research showed that most of the projects have problems with schedule, requirements, architecture, quality, and budget. Comparison of two models of success prediction presented that The Standish Group overestimates problems in project. At the same time, McConnell's model can help to identify problems in time and avoid troubles in future. A framework for evaluating success chances in distributed projects was suggested. The framework is similar to The Standish Group model but it was customized for distributed projects.

Big Data Processing in the Cloud: a Hydra/Sufia Experience

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014