Liam Fallon

Wireless Mesh Network Monitoring: Design, Implementation and Experiments

Mesh networks conveniently complement infrastructure- based networks, allowing devices to spontaneously form a network and connect with other networks. However, effective service provisioning requires a network monitoring solution with adequate support for topology data dissemination and diagnosis, due to the underlying network dynamics and the absence of pre-existing network infrastructure. In addition, monitoring systems have to face a number of challenges relating to autonomy, robustness and scalability. To this end, we have created a self-organised management overlay that homogeneously and dynamically organises devices into a cluster-based hierarchy on which monitored data is dis- seminated. Scalability of the monitoring system is achieved through the minimisation of the generated traffic, as a result of the optimised design. We have implemented the proposed monitoring system and evaluated through experiments the resulting performance.

Towards real-time management of virtualized telecommunication networks

The idea of virtualizing network functions is driven by recent advances in network-focused hardware. In 2012, several large telecommunication operators issued a call to action for Network Function Virtualization (NFV)1. The underlying idea is that every network service currently delivered on proprietary, application specific hardware should be deliverable using virtual machines. This means that routers, firewalls, load balancers and other network devices run virtualized on commodity hardware. Consequently, ETSI is extending this idea to mobile networks. Here, parts of the core and the radio access network will be virtualized. The immediate advantage is that any Virtual Network Function (VNF) can now be deployed, re-deployed and undeployed in the same way as any traditional virtual machine. Thus, NFV will result in more dynamic and agile networks than seen heretofore. However, this will raise a number of serious issues in managing these future networks. In this paper, we examine issues and challenges in orchestrating these virtualized functions and their interconnections to provide a more agile mobile telecommunication network.

Using a semantic knowledge base for communication service quality management in Home Area Networks

The data required for automatic optimization of user services usually exists in current systems, but that data is not modelled or linked in a way that facilitates automation. Knowledge engineering is a promising approach for managing the disparate communication service quality management information data sets and the links across those data sets. Once a knowledge base is in place, semantic techniques can be used to analyse and suggest optimizations to service quality. This paper describes our work in building, populating and evaluating a knowledge base for an IPTV service in Home Area Networks. Population of the knowledge base was implemented using terminal reports. The characteristics of the approach were evaluated through experimentation and the evaluation results are presented in this paper.

Self-forming Network Management Topologies in the Madeira Management System

This paper describes a novel approach to network management topologies where multiple customized topologies are self-configured, self-optimized, and maintained automatically by the underlying network of elements. An implementation of these self-forming management topologies as developed in the Celtic European research project Madeira is described. The self-forming topologies use peer-to-peer communication facilities provided by the Madeira platform running on each network element and give a view of the complete network topology, with customization optimised for individual management functionality. Finally, experiences in utilising these topologies are described, highlighting the benefits of this novel approach.

The Aesop Approach for Semantic-Based End-User Service Optimization

The need to autonomically optimize end-user service experience in near real time has been identified in the literature in recent years. Management systems that monitor end-user service session context exist but approaches that estimate end-user service experience from session context do not analyze the compliance of that experience with user expectations. Approaches that optimize end-user service delivery are not applicable to arbitrary services; they either optimize specific service types or use general mechanisms that do not consider service experience. The lack of a holistic model for end-user service management is a barrier to autonomic end-user service optimization. This paper presents Aesop, an approach addressing autonomic optimization of end-user service delivery using semantic-based techniques. Its knowledge base uses the End-User Service Analysis and Optimization ontology, which models the end-user service management domain and partitions knowledge that varies over time for efficient access. The Aesop Engine executes an autonomic loop in near real time, which runs semantic algorithms to monitor sessions, analyze their compliance with expectations, and plan and execute optimizations on service delivery networks. The algorithms are efficient because they operate on small partitioned subsets of the Knowledge Base held as separate self-contained models at run time. An Aesop implementation was evaluated on a home area network test bed where compliance of service sessions with expectations when optimization was active was compared with compliance of an identical set of sessions when optimization was inactive. Significant improvements were observed on compliance levels of high priority sessions in all experimental scenarios, with compliance levels more than doubled in some cases.

GSQR: A generic service quality reporting protocol for terminals

The most accurate way of evaluating the customer experience of a service is to monitor the service at the point where the service is delivered; that is at the terminal on which the service is running. There has been some research work and standardization efforts on QoS and QoE reporting on terminals and as yet no unified approach for terminal reporting has been described. This paper describes such a generic mechanism for ordering, collecting, and enhancing terminal reports.

Dynamically adaptive policies for dynamically adaptive telecommunications networks

New technologies are changing the world of communication networks and even more so their management. Cloud computing and predictive analytics have removed the need for specialized compute hardware and created products that continuously search for and find insights in management data. Virtualization of networks and network functions, SDN and NFV, are beginning to be mature enough for production networks resulting in much more flexible and dynamic networks. IoT and M2M traffic and new customer demands are driving new thinking and demands for 5G networks. Almost every aspect in the control and management of networks has seen new dimensions of flexibility and dynamicity, with the notable exception of the policies that drive them. This paper discusses the need to add adaptiveness to classic policies, describes a novel approach for adaptive policies, shows how adaptive policies will form part of future network frameworks and architectures, and finally discusses early use cases developed for mobile operators.

SECCO: A test framework for controlling and monitoring end user service sessions

In order to evaluate the efficacy of new research approaches as well as existing systems for management of end user service sessions, it is necessary to set up controlled sessions in a realistic, measurable, and repeatable manner. While service delivery clients support automation of end user sessions and expose mechanisms for retrieval of end user service experience and context, there is no single manner for collection of service experience and context measurements, or for collation and delivery of those measurements to a management system. The Service Experience and Context COllection (SECCO) framework was developed to fulfil three roles. Firstly, it allows end user service sessions to be run in a controlled manner. Secondly, it records end user service experience and context for running sessions. Thirdly, it compiles terminal reports for end user sessions and forwards them to Aesop for processing. This paper outlines the design and implementation of SECCO and describes three separate cases where SECCO was applied.

An algorithm for load balancing in network management applications managing Virtualised Network Functions

In network management applications, load balancing has typically been achieved by manually configuring the application in-situ or using configuration information generated using offline tools. As networks increase in scale and heterogeneity, management applications are being designed to take advantage of scalable computer hardware and often have multiple instances, each of which bears a portion of the management load of the network elements or functions being managed. The communication between management applications and the network elements they are managing is often stateful, posing a challenge for load balancing because connections must be managed if responsibility for managing some network elements change from one instance to another during load balancing. In this paper, we describe an algorithm that enables load balancing of telecommunication management applications for an arbitrary number of VNFs (Virtual Network Functions). The algorithm, triggered by NFV (Network Function Virtualisaton) orchestration, is horizontally scalable and there is no single point of failure once two or more management application instances are running.

Towards automated analysis and optimization of multimedia streaming services using clustering and semantic techniques

This paper presents an automated approach for analysis and optimization of multimedia streaming services in telecommunication networks that can be used to autonomically manage those services. In the approach, threshold analysis is applied to reported quality metric values for services. Cluster analysis is then applied to the metrics to group metric values with similar characteristics. The resulting clusters are then semantically mapped to various ontologies to highlight service aspects which can be optimized with policies. Changes in service aspects are monitored to assess the effectiveness of applied optimizations.