Performance evaluation of an open distributed platform for realistic traffic generation

Network researchers have dedicated a notable part of their efforts to the area of modeling traffic and to the implementation of efficient traffic generators. We feel that there is a strong demand for traffic generators capable to reproduce realistic traffic patterns according to theoretical models and at the same time with high performance. This work presents an open distributed platform for traffic generation that we called distributed internet traffic generator (D-ITG), capable of producing traffic (network, transport and application layer) at packet level and of accurately replicating appropriate stochastic processes for both inter departure time (IDT) and packet size (PS) random variables. We implemented two different versions of our distributed generator. In the first one, a log server is in charge of recording the information transmitted by senders and receivers and these communications are based either on TCP or UDP. In the other one, senders and receivers make use of the MPI library. In this work a complete performance comparison among the centralized version and the two distributed versions of D-ITG is presented

Genetic Algorithm Modeling with GPU Parallel Computing Technology

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012; Smart Innovation, Systems and Technologies, Vol. 19, Springe

An approach to the identification of network elements composing heterogeneous end-to-end paths

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A scheme for time-dependent resource reservation in QoS-enabled IP networks

Abstract. A number of distributed applications require communication services with Quality of Service (QoS) guarantees. The QoS provisioning issue in the Internet has been addressed by the IETF with the definition of the Integrated Services (IntServ) and Differentiated Services (Diffserv) frameworks. Resource reservation mechanisms on which these models are based are totally time-unaware. Yet, we believe that, in some cases, associating a time interval to network resource reservations could be useful for both users and network providers. In this paper we present a distributed scheme for time-dependent reservations in QoS-enabled IP networks. We also show how the standard signalling protocol RSVP may support this new reservation style, with only a few minor modifications. Finally, we present a first prototype implementation of the major component of the proposed architecture and we provide some hints on future applicability scenarios of the advance reservation paradigm and its impact on related topics such as policing and charging techniques in QoS-enabled IP networks.

Networking for Cloud Robotics: The DewROS Platform and Its Application

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications

Internet traffic modeling by means of Hidden Markov Models

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