3,007 research outputs found
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities
Critical Infrastructures (CIs), such as smart power grids, transport systems,
and financial infrastructures, are more and more vulnerable to cyber threats,
due to the adoption of commodity computing facilities. Despite the use of
several monitoring tools, recent attacks have proven that current defensive
mechanisms for CIs are not effective enough against most advanced threats. In
this paper we explore the idea of a framework leveraging multiple data sources
to improve protection capabilities of CIs. Challenges and opportunities are
discussed along three main research directions: i) use of distinct and
heterogeneous data sources, ii) monitoring with adaptive granularity, and iii)
attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201
Transparent Orchestration of Task-based Parallel Applications in Containers Platforms
This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.This work is partly supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316 project, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation program under grant 690116 (EUBra-BIGSEA Project). Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation.Peer ReviewedPostprint (author's final draft
Survey and Analysis of Production Distributed Computing Infrastructures
This report has two objectives. First, we describe a set of the production
distributed infrastructures currently available, so that the reader has a basic
understanding of them. This includes explaining why each infrastructure was
created and made available and how it has succeeded and failed. The set is not
complete, but we believe it is representative.
Second, we describe the infrastructures in terms of their use, which is a
combination of how they were designed to be used and how users have found ways
to use them. Applications are often designed and created with specific
infrastructures in mind, with both an appreciation of the existing capabilities
provided by those infrastructures and an anticipation of their future
capabilities. Here, the infrastructures we discuss were often designed and
created with specific applications in mind, or at least specific types of
applications. The reader should understand how the interplay between the
infrastructure providers and the users leads to such usages, which we call
usage modalities. These usage modalities are really abstractions that exist
between the infrastructures and the applications; they influence the
infrastructures by representing the applications, and they influence the ap-
plications by representing the infrastructures
Issues relating to the low uptake of Networks Of Workstations for commercial applications
Networks of Workstations are a new approach to parallel computing which promise performance gains and more efficient use of resources, resulting in lower technology costs. Despite these benefits, there are anecdotal reports that the business community has been reluctant to use the technology and that the adoption rate of cluster computing for commercial applications remains extremely low. These anecdotal reports are confirmed. Some concerns preventing the commercial usage of Networks of Workstations are revealed, however these concerns are contrary to the experiences of those organisations that have implemented the technology
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