49,296 research outputs found
A Flow-based Distributed Intrusion Detection System Using Mobile Agents
In recent decade, computer networks have grown in popularity. So, network security measures become highly critical to protect networks against different kind of cyber attacks. One of the security measures is using intrusion detection system (IDS). An IDS aims to detect behaviors that compromise network integrity, availability and confidentiality, by continuously capturing and analyzing events occurring in the network. A challenging problem for current IDSs is that their performance decreases in todayâs high speed and large scale networks. A centralize IDS cannot process such high volume of data and there is a high possibility that it discards some attacks. In this paper we propose a flow-based distributed IDS using mobile agents (MA), which performs both data capturing and data analyzing in a distributed fashion. Our distributed IDS provides a framework for deployment of a scalable and high performance IDS, which by using a grouping mechanism and help of mobile agents, effective collaboration can be established between all network members. We simulated our method in NS2. Then we compared our proposed system with a general network-based IDS and a distributed IDS. Experimental results showed its superiority using several metrics of network load, detection rate and flow loss rate.DOI:http://dx.doi.org/10.11591/ijece.v3i6.393
Towards Knowledge in the Cloud
Knowledge in the form of semantic data is becoming more and more ubiquitous, and the need for scalable, dynamic systems to support collaborative work with such distributed, heterogeneous knowledge arises. We extend the âdata in the cloudâ approach that is emerging today to âknowledge in the cloudâ, with support for handling semantic information, organizing and finding it efficiently and providing reasoning and quality support. Both the life sciences and emergency response fields are identified as strong potential beneficiaries of having âknowledge in the cloudâ
Enabling collaboration in virtual reality navigators
In this paper we characterize a feature superset for Collaborative
Virtual Reality Environments (CVRE), and derive a component
framework to transform stand-alone VR navigators into full-fledged
multithreaded collaborative environments. The contributions of our
approach rely on a cost-effective and extensible technique for
loading software components into separate POSIX threads for
rendering, user interaction and network communications, and adding a
top layer for managing session collaboration. The framework recasts
a VR navigator under a distributed peer-to-peer topology for scene
and object sharing, using callback hooks for broadcasting remote
events and multicamera perspective sharing with avatar interaction.
We validate the framework by applying it to our own ALICE VR
Navigator. Experimental results show that our approach has good
performance in the collaborative inspection of complex models.Postprint (published version
Federated and autonomic management of multimedia services
Over the years, the Internet has significantly evolved in size and complexity. Additionally, the modern multimedia services it offers have considerably more stringent Quality of Service (QoS) requirements than traditional static services. These factors contribute to the ever-increasing complexity and cost to manage the Internet and its services. In the dissertation, a novel network management architecture is proposed to overcome these problems. It supports QoS-guarantees of multimedia services across the Internet, by setting up end-to-end network federations. A network federation is defined as a persistent cross-organizational agreement that enables the cooperating networks to share capabilities. Additionally, the architecture incorporates aspects from autonomic network management to tackle the ever-growing management complexity of modern communications networks. Specifically, a hierarchical approach is presented, which guarantees scalable collaboration of huge amounts of self-governing autonomic management components
The Clarens Web Service Framework for Distributed Scientific Analysis in Grid Projects
Large scientific collaborations are moving towards service oriented architecutres for implementation and deployment of globally distributed systems. Clarens is a high performance, easy to deploy Web Service framework that supports the construction of such globally distributed systems. This paper discusses some of the core functionality of Clarens that the authors believe is important for building distributed systems based on Web Services that support scientific analysis
GRIDKIT: Pluggable overlay networks for Grid computing
A `second generation' approach to the provision of Grid middleware is now emerging which is built on service-oriented architecture and web services standards and technologies. However, advanced Grid applications have significant demands that are not addressed by present-day web services platforms. As one prime example, current platforms do not support the rich diversity of communication `interaction types' that are demanded by advanced applications (e.g. publish-subscribe, media streaming, peer-to-peer interaction). In the paper we describe the Gridkit middleware which augments the basic service-oriented architecture to address this particular deficiency. We particularly focus on the communications infrastructure support required to support multiple interaction types in a unified, principled and extensible manner-which we present in terms of the novel concept of pluggable overlay networks
Distributed Computing Grid Experiences in CMS
The CMS experiment is currently developing a computing system capable of serving, processing and archiving the large number of events that will be generated when the CMS detector starts taking data. During 2004 CMS undertook a large scale data challenge to demonstrate the ability of the CMS computing system to cope with a sustained data-taking rate equivalent to 25% of startup rate. Its goals were: to run CMS event reconstruction at CERN for a sustained period at 25 Hz input rate; to distribute the data to several regional centers; and enable data access at those centers for analysis. Grid middleware was utilized to help complete all aspects of the challenge. To continue to provide scalable access from anywhere in the world to the data, CMS is developing a layer of software that uses Grid tools to gain access to data and resources, and that aims to provide physicists with a user friendly interface for submitting their analysis jobs. This paper describes the data challenge experience with Grid infrastructure and the current development of the CMS analysis system
Data Access for LIGO on the OSG
During 2015 and 2016, the Laser Interferometer Gravitational-Wave Observatory
(LIGO) conducted a three-month observing campaign. These observations delivered
the first direct detection of gravitational waves from binary black hole
mergers. To search for these signals, the LIGO Scientific Collaboration uses
the PyCBC search pipeline. To deliver science results in a timely manner, LIGO
collaborated with the Open Science Grid (OSG) to distribute the required
computation across a series of dedicated, opportunistic, and allocated
resources. To deliver the petabytes necessary for such a large-scale
computation, our team deployed a distributed data access infrastructure based
on the XRootD server suite and the CernVM File System (CVMFS). This data access
strategy grew from simply accessing remote storage to a POSIX-based interface
underpinned by distributed, secure caches across the OSG.Comment: 6 pages, 3 figures, submitted to PEARC1
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many
real-world applications. However, existing methods mainly focus on networks
with single-typed nodes/edges and cannot scale well to handle large networks.
Many real-world networks consist of billions of nodes and edges of multiple
types, and each node is associated with different attributes. In this paper, we
formalize the problem of embedding learning for the Attributed Multiplex
Heterogeneous Network and propose a unified framework to address this problem.
The framework supports both transductive and inductive learning. We also give
the theoretical analysis of the proposed framework, showing its connection with
previous works and proving its better expressiveness. We conduct systematical
evaluations for the proposed framework on four different genres of challenging
datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results
demonstrate that with the learned embeddings from the proposed framework, we
can achieve statistically significant improvements (e.g., 5.99-28.23% lift by
F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link
prediction. The framework has also been successfully deployed on the
recommendation system of a worldwide leading e-commerce company, Alibaba Group.
Results of the offline A/B tests on product recommendation further confirm the
effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn
SimGrid: a Sustained Effort for the Versatile Simulation of Large Scale Distributed Systems
In this paper we present Simgrid, a toolkit for the versatile simulation of
large scale distributed systems, whose development effort has been sustained
for the last fifteen years. Over this time period SimGrid has evolved from a
one-laboratory project in the U.S. into a scientific instrument developed by an
international collaboration. The keys to making this evolution possible have
been securing of funding, improving the quality of the software, and increasing
the user base. In this paper we describe how we have been able to make advances
on all three fronts, on which we plan to intensify our efforts over the
upcoming years.Comment: 4 pages, submission to WSSSPE'1
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