Technical support for Life Sciences communities on a production grid infrastructure

Production operation of large distributed computing infrastructures (DCI) still requires a lot of human intervention to reach acceptable quality of service. This may be achievable for scientific communities with solid IT support, but it remains a show-stopper for others. Some application execution environments are used to hide runtime technical issues from end users. But they mostly aim at fault-tolerance rather than incident resolution, and their operation still requires substantial manpower. A longer-term support activity is thus needed to ensure sustained quality of service for Virtual Organisations (VO). This paper describes how the biomed VO has addressed this challenge by setting up a technical support team. Its organisation, tooling, daily tasks, and procedures are described. Results are shown in terms of resource usage by end users, amount of reported incidents, and developed software tools. Based on our experience, we suggest ways to measure the impact of the technical support, perspectives to decrease its human cost and make it more community-specific.Comment: HealthGrid'12, Amsterdam : Netherlands (2012

Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art

Design and implementation of a near maximum likelihood decoder for Cortex codes

International audienceThe Cortex codes form an emerging family among the rate-1/2 self-dual systematic linear block codes with good distance properties. This paper investigates the challenging issue of designing an efficient Maximum Likelihood (ML) decoder for Cortex codes. It first reviews a dedicated architecture that takes advantage of the particular structure of this code to simplify the decoding. Then, we propose a technique to improve the architecture by the generation of an optimal list of binary vectors. An optimal stopping criterion is also proposed. Simulation results show that the proposed architecture achieves an excellent performance/complexity trade-off for short Cortex codes. The proposed decoder architecture has been implemented on an FPGA device for the (24,12,8) Cortex code. This implementation supports an information throughput of 225 Mb/s. At a signal-tonoise ratio Eb/No=8 dB, the Bit Error Rate equals 2 × 10^−10, which is close to the performance of the Maximum Likelihood decoder

LAYSI: A layered approach for SLA-violation propagation in self-manageable cloud infrastructures

Cloud computing represents a promising comput ing paradigm where computing resources have to be allocated to software for their execution. Self-manageable Cloud in frastructures are required to achieve that level of flexibility on one hand, and to comply to users' requirements speci fied by means of Service Level Agreements (SLAs) on the other. Such infrastructures should automatically respond to changing component, workload, and environmental conditions minimizing user interactions with the system and preventing violations of agreed SLAs. However, identification of sources responsible for the possible SLA violation and the decision about the reactive actions necessary to prevent SLA violation is far from trivial. First, in this paper we present a novel approach for mapping low-level resource metrics to SLA parameters necessary for the identification of failure sources. Second, we devise a layered Cloud architecture for the bottom-up propagation of failures to the layer, which can react to sensed SLA violation threats. Moreover, we present a communication model for the propagation of SLA violation threats to the appropriate layer of the Cloud infrastructure, which includes negotiators, brokers, and automatic service deployer. © 2010 IEEE

Порівняння ефективності групового навчання багатошарового персептрону на паралельному комп’ютері та обчислювальному кластері

Паралельний метод групового навчання багатошарового персептрону (БШП) на основі алгоритму зворотного поширення помилки та дослідження ефективності розпаралелення цього методу на паралельному комп’ютері та обчислювальних кластерах представлено в цій статті. Модель БШП та послідовний метод групового навчання описані теоретично. Представлено алгоритмічний опис паралельного методу групового навчання. Ефективність розпаралелення методу досліджена на поступово збільшуваних розмірностях сценаріїв навчання. Результати експериментальних досліджень показали, що (I) обчислювальний кластер з комунікаційним інтерфейсом Infiniband показав кращу ефективність розпаралелення, ніж паралельний комп’ютер загального призначення з ccNuma архітектурою через менші комунікаційні втрати та (II) ефективність розпаралелення представленого методу є достатньо високою для його успішного застосування на паралельних комп’ютерах та кластерах загального призначення, наявних в сучасних обчислювальних ГРІД-системах.The development of a parallel method for batch pattern training of a multilayer perceptron with the back propagation training algorithm and the research of its efficiency on general-purpose parallel computer and computational clusters are presented in this paper. The model of a multilayer perceptron and the usual sequential batch pattern training method are theoretically described. An algorithmic description of the parallel version of the batch pattern training method is presented. The efficiency of parallelization of the developed method is investigated on the progressive increasing the dimension of the parallelized problem. The results of the experimental researches show that (I) the computational cluster with Infiniband interconnection shows better values of parallelization efficiency in comparison with general-purpose parallel computer with ccNuma architecture due to lower communication overhead and (II) the parallelization efficiency of the method is high enough for its appropriate usage on general-purpose parallel computers and clusters available within modern computational grids

dOpenCL: Towards a Uniform Programming Approach for Distributed Heterogeneous Multi-/Many-Core Systems

Modern computer systems are becoming increasingly heterogeneous by comprising multi-core CPUs, GPUs, and other accelerators. Current programming approaches for such systems usually require the application developer to use a combination of several programming models (e. g., MPI with OpenCL or CUDA) in order to exploit the full compute capability of a system. In this paper, we present dOpenCL (Distributed OpenCL) – a uniform approach to programming distributed heterogeneous systems with accelerators. dOpenCL extends the OpenCL standard, such that arbitrary computing devices installed on any node of a distributed system can be used together within a single application. dOpenCL allows moving data and program code to these devices in a transparent, portable manner. Since dOpenCL is designed as a fully-fledged implementation of the OpenCL API, it allows running existing OpenCL applications in a heterogeneous distributed environment without any modifications. We describe in detail the mechanisms that are required to implement OpenCL for distributed systems, including a device management mechanism for running multiple applications concurrently. Using three application studies, we compare the performance of dOpenCL with MPI+OpenCL and a standard OpenCL implementation

Reducing Memory Requirements of Stream Programs by Graph Transformations

International audienceStream languages explicitly describe fork-join parallelism and pipelines, offering a powerful programming model for many-core Multi-Processor Systems on Chip (MPSoC). In an embedded resource-constrained system, adapting stream programs to fit memory requirements is particularly important. In this paper we present a new approach to re- duce the memory footprint required to run stream programs on MPSoC. Through an exploration of equivalent program variants, the method selects parallel code minimizing mem- ory consumption. For large program instances, a heuristic accelerating the exploration phase is proposed and evalu- ated. We demonstrate the interest of our method on a panel of ten significant benchmarks. Using a multi-core modulo scheduling technique, our approach lowers considerably the minimal amount of memory required to run seven of these benchmarks while preserving throughput

A Performance Metrics Scorecard Based Approach to Intrusion Detection System Evaluation for Wireless Network

Wireless Intrusion Detection System (IDS) performance metrics are used to measure the ability of a wireless IDS to perform a particular task and to fit within the performance constraints. These metrics measure and evaluate the parameters that impact the performance of a wireless IDS. Wireless IDS analyze wireless specific traffic including scanning for external users trying to connect to the network through access points and play important role in security to the wireless network. Design of wireless IDS is a difficult task as wireless technology is advancing every day, performance metrics can play an important role in the design of efficient wireless IDS by measuring the factors concern with the performance of a wireless IDS. In this paper we provide a performance metrics scorecard based approach to evaluate intrusion detection systems that are currently popular for wireless networks in the commercial sector. We provide a set of performance metrics that are relevant to wireless IDS and use a 201C;scorecard201D; containing the set of values as the centerpiece of testing and evaluating a wireless IDS. Evaluation of a wireless IDS is done by assigning score to various performance metrics concern with wireless IDS. We apply our performance metrics scorecard evaluation based approach to three popular wireless IDS Snort-wireless, AirDefense Guard, and Kismet. Finally we discuss the results and the opportunities for further work in this area