Alan Davy

Revenue Optimized IPTV Admission Control Using Empirical Effective Bandwidth Estimation

The paper presents an admission approach for IPTV service providers that is designed to minimize QoS violations whilst effectively utilizing available bandwidth. Central to the approach is an empirical method of estimating the effective bandwidth required to satisfy QoS targets for admitted traffic flows. The paper describes this method and specifies two admission control algorithms based on the use of effective bandwidth estimates. The first algorithm employs a simple evaluation of whether there is sufficient bandwidth available to ensure, with an appropriate degree of confidence, that QoS targets will not be violated if a requested flow is admitted. The second algorithm utilizes information relating to the cost, duration and request frequency of specific IPTV content to prioritize higher revenue flows within the admission control process. Results of a simulation study (employing real traffic traces of long-lived flows) indicate that the proposed algorithms ensure that an adequate, but not overly generous, amount of bandwidth is allocated to ensure that QoS targets for accepted flows are met. Furthermore, they demonstrate the potential advantage of using content specific information in the admission control process to maximize generated revenue.

An efficient process for estimation of network demand for qos-aware IP network planning

Estimations of network demand are an essential input to the IP network planning process. We present a technique for per traffic class IP network demand estimation based on harnessing information gathered for accounting and charging purposes. This technique represents an efficient use of pre-existing information, is easy to deploy, and, crucially, is highly cost-effective in comparison to traditional direct measurement systems employing dedicated traffic metering hardware. In order to facilitate QoS-aware network planning we also introduce a technique for estimation of QoS related effective bandwidth coefficients via analysis of a relatively small number of packet traces. The combination of the demand and effective bandwidth coefficient estimation techniques provide the basis for an effective, low-cost network planning solution. In this paper we present initial results that validate our contention that network accounting records can be reused to create a QoS aware demand matrix for IP networks.

On the use of accounting data for QoS-aware IP network planning

We present an economically efficient framework for provision of essential input for QoS-aware IP network planning. Firstly, we define a process for reuse of network accounting data for construction of a QoS-aware network demand matrix. Secondly, we define a process for estimation of QoS-related effective bandwidth coefficients from packet traces collected per traffic classe. Taken together, these processes provide the necessary input required to plan a network in accordance with QoS constraints. We present results of a sensitivity analysis of the demand estimation process, and of an economic analysis of the relative merit of deployment of our approach in comparison to a traditional direct measurement-based approach. We conclude that although there is a degree of inaccuracy in our network demand estimation process this inaccuracy is within acceptable bounds, and that this is offset by the potential for significant cost reductions for the ISP.

Process for QoS-Aware IP Network Planning Using Accounting Data and Effective Bandwidth Estimation

In this paper we describe QoSPlan - a generalized process for preparing information relevant to QoS-aware IP network planning. QoSPlan is designed to reduce the cost of deployment and maintenance of traditional network monitoring systems for service providers, while maintaining a sufficient degree of precision. The process involves analysis of pre-existing accounting data to estimate a network-wide demand matrix. Part of this estimation process relates to the generalization of QoS-related effective bandwidth measurements taken from a set of collected packet traces. Based on experience, we offer recommendations on how to appropriately realise QoSPlan to maximise its accuracy and effectiveness when applied to different network traffic scenarios.

Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm

With the evolving IoT scenario, computing has spread to the most minuscule everyday activities, leading to a momentous shift in the way applications are developed and deployed. With the volume of impact increasing exponentially, a coherent approach of deploying these applications is critical for an efficient utilization of the network infrastructure. A typical IoT application consists of various modules running together with active interdependencies; traditionally running on the Cloud hosted in global data centres. In this paper, we present a Module Mapping Algorithm for efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. With Fog computing into picture, computation is dynamically distributed across the Fog and Cloud layer, and the modules of an application can thus be deployed closer to the source on devices in the Fog layer. The result of this work can serve as a Micro-benchmark in studies/research related with IoT and Fog Computing, and can be used for Quality of Service (QoS) and Service Level Objective benchmarking for IoT applications. The approach is generic, and applies to a wide range of standardized IoT applications over varied network topologies irrespective of load.

Dynamic channel allocation in electromagnetic nanonetworks for high resolution monitoring of plants

Abstract We investigate techniques to enable communication in the THz band between graphene-based nanoscale devices and microscale network components for agricultural crop-monitoring applications. The properties of THz communications, in particular sensitivity to moisture levels on the communications path and attenuation by obstacles (e.g., leaves) mean that achieving a desired level of throughput of monitoring data can be difficult. Using a simplified model of plant structure and typical plant moisture patterns, we analyze the performance of four frequency selection strategies in terms of throughput and energy utilization for varying numbers of nano and microscale devices, moisture concentration patterns and plant leaf densities. We find that a Two-Phase optimization strategy for frequency selection performs best in a wide range of operational conditions and that leaf density has a significant impact on achievable throughput. Our plant model could serve as a useful basis for planning the necessary concentration of nano and microscale devices to deploy on particular crop types in order to meet given network performance targets.

Performance Analysis of Plant Monitoring Nanosensor Networks at THz Frequencies

Future wireless nanosensor networks are envisioned to operate in the THz band, due to the tiny size of the network components. Among the diverse range of applications that such networks promise, high-resolution plant monitoring systems are the categories which can benefit from the size and high sensitivity of nanosensor devices and also the high bandwidth provided by them. However, communications at the THz frequency band, especially within a hybrid channel like plant foliage, undergo peculiar types of attenuation and distortion. These phenomena, which can challenge the feasibility of the aforementioned applications, need to be addressed/modeled precisely. Therefore, in this paper, we propose the first THz path-loss model within a plant environment. In addition, we provide a simplified model of plant structure as well as a model for the probability of successful transmissions between nanosensors and microscale receivers mounted on the plant stem. The introduced models consider the limited capability of THz radiation to propagate through plant leaves, and also the high free-space path-loss as the main sources of signal loss in the network communications. Furthermore, these models can be customized based on the structural characteristics of a plant, e.g., leaves size and distribution, to account for a variety of plant species. Finally, the performance of communications based on the provided models is evaluated for different network scenarios.

Active techniques for available bandwidth estimation: comparison and application

There are various parameters for analyzing the quality of network communication links and paths, one attracting particular attention is available bandwidth. In this chapter we describe a platform for the available bandwidth estimation, a comparison of different tools for the estimation of this parameter, and an application of such estimation in a real-world application. In details, we describe a novel platform called UANM, capable of properly choosing, configuring, and using different available bandwidth tools and techniques in an autonomic fashion. Moreover, thanks to UANM, we show the results of a comparison of the performance of several tools in terms of accuracy, probing time and intrusiveness. Finally, we show a practical example of the use of the available bandwidth measurement: we describe an approach for server selection and admission control in a content distribution network based on the available bandwidth estimation.

GA-based frequency selection strategies for graphene-based nano-communication networks

We propose and evaluate a number of of frequency selection strategies for nano-scale devices using graphene-based nano-antennas (“graphennas”), which operate in the Terahertz band. The strategies take into account the limitations of Terahertz channel and aim to optimize the overall network transmission rate of a network of nano-devices, while maximizing various objectives. We investigate the trade-off between cases where: 1) frequency duplication within the network is allowed or prevented; 2) limiting the spread of frequencies over the entire Terahertz range is required; and 3) balancing the load between the network sink nodes is required. We compare the network performance for the different objectives proposed against a random frequency selection strategy. Our simulation study demonstrates the efficiency of the proposed algorithms and indicates their usefulness in different application scenarios.

Server selection and admission control for IP-based video on demand using available bandwidth estimation

Service providers offering IP-based video on demand services often replicate video content in multiple content servers with different network points of attachment. When a request for a content item arrives from an end-user, a decision must be made as to whether the request should be admitted and, if so, which server should be used. To ensure adequate quality-of-service this admission control / server selection decision should be cognisant of the current link utilisation on the paths between the end-users point of attachment and those of the servers. We have studied the conditions and parameters under which end-to-end available bandwidth estimation tools (ABETs) pathChirp and Assolo can be used in this decision process. We specify an admission control / server selection algorithm that uses available bandwidth estimations. Simulation study results show pathChirp (but not Assolo), when appropriately parameterised, can generate available bandwidth estimates that can be used by our admission control / server selection algorithm to react appropriately to changes in background loads on network paths.