Automatic self-configuration of the logical network using distributed software agents

We present a system for dynamic network resource configuration in environments with bandwidth reservation. The proposed system is completely distributed and automates the mechanisms for adapting the logical network to the offered load. The system is able to manage dynamically a logical network such as a virtual path network in ATM or a label switched path network in MPLS or GMPLS. The system design and implementation is based on a multi-agent system (MAS) which make the decisions of when and how to change a logical path. Despite the lack of a centralised global network view, results show that MAS manages the network resources effectively, reducing the connection blocking probability and, therefore, achieving better utilisation of network resources. We also include details of its architecture and implementation

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.

Towards a User-Friendly Platform for Building Language Resources based on Web Services

This paper presents the platform developed in the PANACEA project, a distributed factory that automates the stages involved in the acquisition, production, updating and maintenance of Language Resources required by Machine Translation and other Language Technologies. We adopt a set of tools that have been successfully used in the Bioinformatics field, they are adapted to the needs of our field and used to deploy web services, which can be combined to build more complex processing chains (workflows). This paper describes the platform and its different components (web services, registry, workflows, social network and interoperability). We demonstrate the scalability of the platform by carrying out a set of massive data experiments. Finally, a validation of the platform across a set of required criteria proves its usability for different types of users (non-technical users and providers).

Interoperability and Technology for a Language Resources Factory

This document describes some of the technological aspects of a project devoted to the creation of a factory for language resources. The project’s objectives are explained, as well as the idea to create a distributed infrastructure of web services. This document focuses on two main topics of the factory: (1) the technological approaches chosen to develop the factory, i.e. software, protocols, servers, etc. (2) and Interoperability as the main challenge is to permit different NLP tools work together in the factory. This document explains why XCES and GrAF are chosen as the main formats used for the linguistic data exchange.

Language Resources Factory: case study on the acquisition of Translation Memories

This paper demonstrates a novel distributed architecture to facilitate the acquisition of Language Resources. We build a factory that automates the stages involved in the acquisition, production, updating and maintenance of these resources. The factory is designed as a platform where functionalities are deployed as web services, which can be combined in complex acquisition chains using workflows. We show a case study, which acquires a Translation Memory for a given pair of languages and a domain using web services for crawling, sentence alignment and conversion to TMX.

Distributed Energy Production in the North-West Region of Russia

The energy system of Russia is the world's fourth largest measured by installed power. The largest are that of the the United States of America, China and Japan. After 1990, the electricity consumption decreased as a result of the Russian industry crisis. The vivid economic growth during the latest few years explains the new increase in the demand for energy resources within the State. In 2005 the consumption of electricity achieved the maximum level of 1990 and continues to growth. In the 1980's, the renewal of power facilities was already very slow and practically stopped in the 1990's. At present, the energy system can be very much characterized as outdated, inefficient and uneconomic because of the old equipment, non-effective structure and large losses in the transmission lines. The aim of Russia's energy reform, which was started in 2001, is to achieve a market based energy policy by 2011. This would thus remove the significantly state-controlled monopoly in Russia's energy policy. The reform will stimulateto decrease losses, improve the energy system and employ energy-saving technologies. The Russian energy system today is still based on the use of fossil fuels, and it almost totally ignores the efficient use of renewable sources such as wind, solar, small hydro and biomass, despite of their significant resources in Russia. The main target of this project is to consider opportunities to apply renewable energy production in the North-West Federal Region of Russia to partly solve the above mentioned problems in the energy system.

Global Distributed Delivery Model for Internet Service Provider

Globaalin talouden rakenteet muuttuvat jatkuvasti. Yritykset toimivat kansainvälisillä markkinoilla aiempaa enemmän. Tuotannon lisäämiseksi monet yritykset ovat ulkoistaneet tuotteidensa tuki- ja ylläpitotoiminnot halvan työvoiman maihin. Yritykset voivat tällöin keskittää toimintansa ydinosamiseensa. Vapautuneita resursseja voidaan käyttää yrityksen sisäisessä tuotekehityksessä ja panostaa seuraavan sukupolven tuotteiden ja teknologioiden kehittämiseen. Diplomityö esittelee Globaalisti hajautetun toimitusmallin Internet-palveluntarjoajalle jossa tuotteiden tuki- ja ylläpito on ulkoistettu Intiaan. Teoriaosassa esitellään erilaisia toimitusmalleja ja keskitytään erityisesti hajautettuun toimitusmalliin. Tämän lisäksi luetellaan valintakriteerejä joilla voidaan arvioida projektin soveltuvuutta ulkoistettavaksi sekä esitellään mahdollisuuksia ja uhkia jotka sisältyvät globaaliin ulkoistusprosessiin. Käytäntöosassa esitellään globaali palvelun toimittamisprosessi joka on kehitetty Internet-palveluntarjoajan tarpeisiin.

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.

Bidding support for computational resources

Peer-reviewed

Integrating distributed generation and Smart Grids into single-family house

This study examines Smart Grids and distributed generation, which is connected to a single-family house. The distributed generation comprises small wind power plant and solar panels. The study is done from the consumer point of view and it is divided into two parts. The first part presents the theoretical part and the second part presents the research part. The theoretical part consists of the definition of distributed generation, wind power, solar energy and Smart Grids. The study examines what the Smart Grids will enable. New technology concerning Smart Grids is also examined. The research part introduces wind and sun conditions from two countries. The countries are Finland and Germany. According to the wind and sun conditions of these two countries, the annual electricity production from wind power plant and solar panels will be calculated. The costs of generating electricity from wind and solar energy are calculated from the results of annual electricity productions. The study will also deal with feed-in tariffs, which are supporting systems for renewable energy resources. It is examined in the study, if it is cost-effective for the consumers to use the produced electricity by themselves or sell it to the grid. Finally, figures for both countries are formed. The figures include the calculated cost of generating electricity from wind power plant and solar panels, retail and wholesale prices and feed-in tariffs. In Finland, it is not cost-effective to sell the produced electricity to the grid, before there are support systems. In Germany, it is cost-effective to sell the produced electricity from solar panels to the grid because of feed-in tariffs. On the other hand, in Germany it is cost-effective to produce electricity from wind to own use because the retail price is higher than the produced electricity from wind.

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Channel Adaptive MAC Protocol with Traffic-Aware Distributed Power Management in Wireless Sensor Networks-Some Performance Issues

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources and in turn can result in the rapid depletion of sensor energy and the partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide the proper flow of information in a WSN. In this paper, we develop a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of the wireless channel also taken into consideration. The proposed protocol calculates a combined weight value based on the channel state and link quality. Then transmission is allowed only for those nodes with weights greater than a minimum quality threshold and nodes attempting to access the wireless medium with a low weight will be allowed to transmit only when their weight becomes high. This results in many poor quality nodes being deprived of transmission for a considerable amount of time. To avoid the buffer overflow and to achieve fairness for the poor quality nodes, we design a Load prediction algorithm. We also design a traffic aware dynamic power management scheme to minimize the energy consumption by continuously turning off the radio interface of all the unnecessary nodes that are not included in the routing path. By Simulation results, we show that our proposed protocol achieves a higher throughput and fairness besides reducing the delay

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.