Reliable and timely event notification for publish/subscribe services over the internet

The publish/subscribe paradigm is gaining attention for the development of several applications in wide area networks (WANs) due to its intrinsic time, space, and synchronization decoupling properties that meet the scalability and asynchrony requirements of those applications. However, while the communication in a WAN may be affected by the unpredictable behavior of the network, with messages that can be dropped or delayed, existing publish/subscribe solutions pay just a little attention to addressing these issues. On the contrary, applications such as business intelligence, critical infrastructures, and financial services require delivery guarantees with strict temporal deadlines. In this paper, we propose a framework that enforces both reliability and timeliness for publish/subscribe services over WAN. Specifically, we combine two different approaches: gossiping, to retrieve missing packets in case of incomplete information, and network coding, to reduce the number of retransmissions and, consequently, the latency. We provide an analytical model that describes the information recovery capabilities of our algorithm and a simulation-based study, taking into account a real workload from the Air Traffic Control domain, which evidences how the proposed solution is able to ensure reliable event notification over a WAN within a reasonable bounded time window. © 2013 IEEE

Worse-than-Rayleigh Fading: Experimental Results and Theoretical Models

This article is motivated by the recent recognition that channel fading for new wireless applications is not always well described by traditional models used for mobile communication systems. In particular, fading data collected for vehicleto- vehicle and wireless sensor network applications has motivated new models for conditions in which channel fading statistics can be worse than Rayleigh. We review the use of statistical channel models, describe our example applications, and provide both measured and modeling results for these severe fading conditions

Determining model accuracy of network traces

Accurate network modeling is critical to the design of network protocols. Traditional modeling approaches, such as Discrete Time Markov Chains (DTMC) are limited in their ability to model time-varying characteristics. This problem is exacerbated in the wireless domain, where fading events create extreme burstiness of delays, losses, and errors on wireless links. In this paper, we describe the data preconditioning modeling technique that is capable of capturing the statistical characteristics of wired and wireless network traces. We revise our previous developed data preconditioning modeling algorithm, the Markov-based Trace Analysis (MTA), and present the Multiple states MTA (MMTA) algorithm. Our main contributions are methodologies created to quantify the accuracy of network models, methodology to choose the most accurate model for a given network and characteristic of interest (e.g., delay, loss, or error process), and the validation of our data preconditioning modeling algorithms. (C) 2006 Elsevier Inc. All rights reserved

Determining Model Accuracy of Network Traces Abstract

Accurate network modeling is critical to the design of network protocols. Traditional modeling approaches, such as Discrete Time Markov Chains (DTMC) are limited in their ability to model time-varying characteristics. This problem is exacerbated in the wireless domain, where fading events create extreme burstiness of delays, losses, and errors on wireless links. In this paper, we describe the data preconditioning modeling technique that is capable of capturing the statistical characteristics of wired and wireless network traces. We revise our previous developed data preconditioning modeling algorithm, the Markov-based Trace Analysis (MTA), and present the Multiple states MTA (MMTA) algorithm. Our main contributions are methodologies created to quantify the accuracy of network models, methodology to choose the most accurate model for a given network and characteristic of interest (e.g., delay, loss, or error process), and the validation of our data preconditioning modeling algorithms. Key words: model accuracy, network traces, wireless networking, Markov chains, stationarity, data preconditioning