Jose Manuel Gimenez-Guzman

Measurement-based analysis of the performance of several wireless technologies

Wireless technologies have rapidly evolved and are becoming ubiquitous. An increasing number of users attach to the Internet using these technologies; hence the performance of these wireless access links is a key point when considering the performance of the whole Internet. In this paper we present a measurement-based analysis of the performance of an IEEE 802.16 (WiMAX) client and an UMTS client. The measurements were carried out in a controlled laboratory. The wireless access links were loaded with traffic from a multi-point videoconferencing application and we measured three layer-3 metrics (one-way-delay, IP-delay-variation and packet loss ratio). Additionally we estimate the performance of a WiFi and Ethernet client as a reference. Our results show that Ethernet and WiFi have comparable performances. Both the WiMAX and the UMTS links exhibited an asymmetric behavior, with the uplink showing an inferior performance. We also assessed the causes of the discretization which appears in the jitter distributions of these links.

Design and evaluation of a learning environment to effectively provide network security skills

Information system security and network security are topics of increasing importance in the information society. They are also topics where the adequate education of professionals requires the use of specific laboratory environments where the practical aspects of the discipline may be addressed. However, most approaches currently used are excessively static and lack the flexibility that the education requirements of security professionals demand. In this paper we present NEMESIS, a scenario generation framework for education on system and network security, which is based on virtualization technologies and has been designed to be open, distributed, modular, scalable and flexible. Finally, an example scenario is described and some results validating the benefits of its use in undergraduate computer security courses are shown.

A Reinforcement Learning Approach for Admission Control in Mobile Multimedia Networks with Predictive Information

We study the problem of optimizing admission control policies in mobile multimedia cellular networks when predictive information regarding movement is available and we evaluate the gains that can be achieved by making such predictive information available to the admission controller. We consider a general class of prediction agents which forecast the number of future handovers and we evaluate the impact on performance of aspects like: whether the prediction refers to incoming and/or outgoing handovers, inaccurate predictions, the anticipation of the prediction and the way that predictions referred to different service classes are aggregated. For the optimization process we propose a novel Reinforcement Learning approach based on the concept of afterstates. The proposed approach, when compared with conventional Reinforcement Learning, yields better solutions and with higher precision. Besides it tackles more efficiently the curse of dimensionality inherent to multimedia scenarios. Numerical results show that the performance gains measured are higher when more specific information is provided about the handover time instants, i.e. when the anticipation time is deterministic instead of stochastic. It is also shown that the utilization of the network is maintained at very high values, even when the highest improvements are observed. We also compare an optimal policy obtained deploying our approach with a previously proposed heuristic prediction scheme, showing that plenty of room for technological innovation exists.

Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks

Due to the low cost of CMOS IP-based cameras, wireless surveillance sensor networks have emerged as a new application of sensor networks able to monitor public or private areas or even country borders. Since these networks are bandwidth intensive and the radioelectric spectrum is limited, especially in unlicensed bands, it is mandatory to assign frequency channels in a smart manner. In this work, we propose the application of automated negotiation techniques for frequency assignment. Results show that these techniques are very suitable for the problem, being able to obtain the best solutions among the techniques with which we have compared them.

Generalized truncated methods for an efficient solution of retrial systems.

We are concerned with the analytic solution of multiserver retrial queues including the impatience phenomenon. As there are not closed-form solutions to these systems, approximate methods are required. We propose two different generalized truncated methods to effectively solve this type of systems. The methods proposed are based on the homogenization of the state space beyond a given number of users in the retrial orbit. We compare the proposed methods with the most well-known methods appeared in the literature in a wide range of scenarios. We conclude that the proposed methods generally outperform previous proposals in terms of accuracy for the most common performance parameters used in retrial systems with a moderated growth in the computational cost.

All-Path bridging

Display Omitted Today, link-state routing protocols that compute multiple shortest paths predominate in data center and campus networks, where routing is performed either in layer three or in layer two using link-state routing protocols. But current proposals based on link-state routing do not adapt well to real time traffic variations and become very complex when attempting to balance the traffic load. We propose All-Path bridging, an evolution of the classical transparent bridging that forwards frames over shortest paths using the complete network topology, which overcomes the limitations of the spanning tree protocol. All-Path is a new frame routing paradigm based on the simultaneous exploration of all paths of the real network by a broadcast probe frame, instead of computing routes on the network graph. This paper presents All-Path switches and their differences with standard switches and describes ARP-Path protocol in detail, its path recovery mechanisms and compatibility with IEEE 802.1 standard bridges. ARP-Path is the first protocol variant of the All-Path protocol family. ARP-Path reuses the standard ARP Request and Reply packets to explore reactively the network and find the fastest path between two hosts. We compare its performance in terms of latency and load distribution with link-state shortest-path routing bridges, showing that ARP-Path distributes the load more evenly and provides lower latencies. Implementations on different platforms prove the robustness of the protocol. The conclusion is that All-Path bridging offer a simple, resilient and scalable alternative to path computation protocols.

Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision.

Analysis of a cellular network with user redials and automatic handover retrials

In cellular networks, repeated attempts occur as result of user behavior but also as automatic retries of blocked requests. Both phenomena play an important role in the system performance and therefore should not be ignored in its analysis. On the other hand, an exact Markovian model analysis of such systems has proven to be infeasible and resorting to approximate techniques is mandatory. We propose an approximate methodology which substantially improves the accuracy of existing methods while keeping computation time in a reasonable value. A numerical evaluation of the model is carried out to investigate the impact on performance of the parameters related to the retry phenomena. As a result, some useful guidelines for setting up the automatic retries are provided. Finally, we also show how our model can be used to obtain a tight performance approximation in the case where reattempts have a deterministic nature.

Using a scenario generation framework for education on system and internet security

Most approaches currently used for Education on Internet Security and Information System Security are excessively static and lack the flexibility that the education requirements of security professionals demand. They can greatly benefit from the use of specific laboratory environments. In this paper we show how a tool which allows to generate scenarios to be used in education on system and network security, based on virtualization technologies could be used to improve such scenarios because of its open, distributed, modular, scalable and flexible features. An example scenario is described and finally some results showing the benefits of its use in undergraduate computer security courses are shown.

Optimal Admission Control in Mobile Cellular Networks with Movement Prediction

In this paper we study the impact of incorporating handover prediction information into the session admission control process in mobile cellular networks. The comparison is done between the performance of optimal policies obtained with and without the predictive information. A prediction agent classifies mobile users into two classes, those that will probably produce a handover to and/or from the cell under study and those that probably will not produce such handover. Moreover, the time instant has also been used in the prediction by means of a deterministic size window. Two different approaches to compute the optimal admission policy were studied: dynamic programming and reinforcement learning. Results show significant performance gains when the incoming predictive information is used in the admission process, and that higher gains are obtained when temporal information is used.