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Alexandra Carpen-Amarie, Alexandru Costan, Jing Cai, Gabriel Antoniu and Luc Bougé

Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system

Introspection is the prerequisite of autonomic behavior, the first step towards performance improvement and resource usage optimization for large-scale distributed systems. In grid environments, the task of observing the application behavior is assigned to monitoring systems. However, most of them are designed to provide general resource information and do not consider specific information for higher-level services. More precisely, in the context of data-intensive applications, a specific introspection layer is required to collect data about the usage of storage resources, data access patterns, etc. This paper discusses the requirements for an introspection layer in a data management system for large-scale distributed infrastructures. We focus on the case of BlobSeer, a large-scale distributed system for storing massive data. The paper explains why and how to enhance BlobSeer with introspective capabilities and proposes a three-layered architecture relying on the MonALISA monitoring framework. We illustrate the autonomic behavior of BlobSeer with a self-configuration component aiming to provide storage elasticity by dynamically scaling the number of data providers. Then we propose a preliminary approach for enabling self-protection for the BlobSeer system, through a malicious client detection component. The introspective architecture has been evaluated on the Grid'5000 testbed, with experiments that prove the feasibility of generating relevant information related to the state and behavior of the system.

Open access

D. Thresh Kumar, Hamed Soleimani and Govindan Kannan


Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

Open access

Adrianna Kozierkiewicz-Hetmańska and Ngoc Nguyen

A method for learning scenario determination and modification in intelligent tutoring systems

Computers have been employed in education for years. They help to provide educational aids using multimedia forms such as films, pictures, interactive tasks in the learning process, automated testing, etc. In this paper, a concept of an intelligent e-learning system will be proposed. The main purpose of this system is to teach effectively by providing an optimal learning path in each step of the educational process. The determination of a suitable learning path depends on the student's preferences, learning styles, personal features, interests and knowledge state. Therefore, the system has to collect information about the student, which is done during the registration process. A user is classified into a group of students who are similar to him/her. Using information about final successful scenarios of students who belong to the same class as the new student, the system determines an opening learning scenario. The opening learning scenario is the first learning scenario proposed to a student after registering in an intelligent e-learning system. After each lesson, the system tries to evaluate the student's knowledge. If the student has a problem with achieving an assumed score in a test, this means that the opening learning scenario is not adequate for this user. In our concept, for this case an intelligent e-learning system offers a modification of the opening learning scenario using data gathered during the functioning of the system and based on a Bayesian network. In this paper, an algorithm of scenario determination (named ADOLS) and a procedure for modifying the learning scenario AMLS with auxiliary definitions are presented. Preliminary results of an experiment conducted in a prototype of the described system are also described.

Open access

Shubham Rawat, Nupur Goyal and Mangey Ram

-11. Lai, R. and Garg, M. (2012). A detailed study of NHPP software reliability models, Journal of Software 7(6): 1296-1306. Matsumoto, K.I., Inoue, K., Kikuno, T. and Torii, K. (1988). Experimental evaluation of software reliability growth models, 11th International Symposium FTCS-18, Tokyo, Japan, pp. 148-153. Musa, J.D., Iannino, A. and Okumoto, K. (1987). Software Reliability: Measurement, Prediction, Application, McGraw-Hill, Inc., New York, NY. Palma, J., Tian, J. and Lu, P. (1993). Collecting data for software

Open access

Wanzeng Kong, Bei Jiang, Qiaonan Fan, Li Zhu and Xuehui Wei

References Armstrong, B.C., Ruiz-Blondet, M.V., Khalifian, N., Kurtz, K.J., Jin, Z. and Laszlo, S. (2015). Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics, Neurocomputing 166(2015): 59-67. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D.U. (2006). Complex networks: Structure and dynamics, Physics Reports 424(4C5): 175-308. Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A. and Pfurtscheller, G. (2008). BCI Competition 2008-Graz data set A

Open access

Maciej Patan

, pp. 482-491. Kubrusly, C. S. and Malebranche, H. (1985). Sensors and controllers location in distributed systems—A survey, Automatica   21 (2): 117-128. Martínez, S. and Bullo, F. (2006). Optimal sensor placement and motion coordination for target tracking, Automatica   42 (4): 661-668. Müller, W. G. (2007). Collecting Spatial Data: Optimum Design of Experiments for Random Fields , 3rd Edn., Physica-Verlag, Heidelberg. Nehorai, A., Porat, B. and Paldi, E

Open access

Tomasz Zięba and Dariusz Uciński

and controllers location in distributed systems — A survey, Automatica 21 (2): 117-128. Le N. D. and Zidek J. V. (2006). Statistical Analysis of Environmental Space-Time Processes , Springer-Verlag, New York, NY. Levy A. V. and Montalvo A. (1985). The tunneling algorithm for the global minimization of functions, SIAM Journal on Scientific and Statistical Computing 6 (1): 15-29. Müller W. G. (2007). Collecting Spatial Data. Optimum Design of Experiments for Random Fields, 3rd Revised

Open access

Dariusz Uciński

, 2nd Edn., Prentice Hall, Upper Saddle River, NJ. Martínez, S. and Bullo, F. (2006). Optimal sensor placement and motion coordination for target tracking, Automatica   42 : 661-668. Müller, W. G. (2001). Collecting Spatial Data. Optimum Design of Experiments for Random Fields , 2nd Revised Edn., Contributions to Statistics, Physica-Verlag, Heidelberg. Munack, A. (1984). Optimal sensor allocation for identification of unknown parameters in a bubble-column loop bioreactor, in A. V

Open access

M. Fernández-Martínez

definition of fractal dimension and provide useful expressions to deal with its effective calculation. We collect some connections of each definition of fractal dimension with the classical definitions of fractal dimension, namely, both the box and the Hausdorff dimensions. In addition, we also provide some links to other fractal dimensions defined from a fractal dimension approach. Interestingly, we shall generalize the box dimension throughout the so-called fractal dimensions I, II, and III, whereas we shall generalize the Hausdorff dimension by means of fractal

Open access

Christian Federmann and Andreas Eisele

MT Server Land: An Open-Source MT Architecure

We describe the implementation of MT Server Land, an open-source architecture for machine translation that is developed by the MT group at DFKI. A broker server collects and distributes translation requests to several worker servers that create the actual translations. Users can access the system via a fast and easy-to-use web interface or use an XML-RPC-based API interface to integrate it into their applications. The source code is published under a BSD-style license and is freely available from GitHub1.