Henning Sanneck

Framework model for packet loss metrics based on loss runlengths

For the same long-term loss ratio, different loss patterns lead to different application-level Quality of Service (QoS) perceived by the users (short-term QoS). While basic packet loss measures like the mean loss rate are widely used in the literature, much less work has been devoted to capturing a more detailed characterization of the loss process. In this paper, we provide means for a comprehensive characterization of loss processes by employing a model that captures loss burstiness and distances between loss bursts. Model parameters can be approximated based on run-lengths of received/lost packets. We show how the model serves as a framework in which packet loss metrics existing in the literature can be described as model parameters and thus integrated into the loss process characterization. Variations of the model with different complexity are introduced, including the well-known Gilbert model as a special case. Finally we show how our loss characterization can be used by applying it to actual Internet loss traces.

Speech Property-Based FEC for Internet Telephony Applications *

In this paper we first analyze the concealment performance of the G.729 decoder. We find that the loss of unvoiced frames can be concealed well. Also, the loss of voiced frames is concealed well once the decoder has obtained sufficient information on them. However the decoder fails to conceal the loss of voiced frames at an unvoiced/voiced transition because it extrapolates internal state (filter coefficients and excitation) for an unvoiced sound. Moreover, once the encoder has failed to build the appropriate linear prediction synthesis filter, it takes a long time for the decoder to resynchronize with the encoder. Using this result, we then develop a new FEC scheme to support frame-based codecs, which adjusts the amount of added redundancy adaptively to the properties of the speech signal. Objective quality measures (ITU P.861A and EMBSD) show that our speech property-based FEC scheme achieves almost the same speech quality as current FEC schemes while approximately halving the amount of necessary redundant data to adequately protect the voice flow.

Concealment of lost speech packets using adaptive packetization

Long-term correlation within a speech signal is usually exploited to achieve higher compression ratios (e.g. RPE-LTP coders). We aim to use the long-term correlation to influence the packetization interval of a voice stream at the sender before sending it over a lossy packet-switched network. If a packet is lost, the receiver can conceal the loss of information by using adjacent signal segments of which (due to the pre-processing/packetization at the sender) a certain similarity to the lost segment can be assumed. Subjective test results show that the adaptive packetization/concealment scheme (AP/C) can alleviate significantly the impact of isolated packet losses to speech quality.

Supporting IP Multicast Integrated Services in ATM networks

This paper presents an integrated, server-based mechanism for the efficient support of the IP integrated services (IIS) model in ATM networks, namely the multicast integration server (MIS) architecture. Instead of viewing IP-ATM multicast address resolution and QoS support separately, the approach in this paper is to consider such issues in an integrated manner. The multicast integration server is capable of IP multicast to ATM NSAP address resolution using the easy multicast routing through ATM clouds (EARTH) protocol, as well as of QoS management using the resource reservation protocol (RSVP). With the use of EARTH, several ATM point-to-multipoint connections with different QoS parameters can be associated to a single IP multicast address. AN RSVP server within the MIS is used to distribute RSVP messages inside the ATM cloud and to set the corresponding QoS state in the address resolution table of EARTH. In addition, this paper defines a quantized heterogeneity model which supports, together with the MIS, advanced IIS features as QoS heterogeneity and dynamic QoS changes in IP-ATM networks.

Anomaly Detection and Diagnosis for Automatic Radio Network Verification

The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

A configuration management assessment method for SON verification

Over the last years the complexity of mobile communication networks has significantly increased. Therefore, Self-Organizing Network (SON) features have been introduced to automate the process of fault-remedying, configuring Network Elements (NEs), and optimizing their operation. Such features are typically implemented by SON functions which actively perform changes to Configuration Management (CM) parameters in order to achieve certain objectives. However, having such features also requires a mechanism that allows us to verify their actions. In case certain NEs experience an undesired behavior, we have to be able to determine whether it is caused by CM changes and, if so, identify the responsible ones for that to happen. In this paper we propose a novel CM change performance assessment method with dynamic scope management for automated CM undo decision making. Our method includes two key properties: the ability to determine the minimal set of cells that are possibly influenced by a certain CM change, and generate a recommendation to accept or reject it based on the observed network performance. Results from our real data evaluation show our method is able to detect anomalous network behavior and identify the responsible changes.

Managing scope changes for cellular network-level anomaly detection

The Self-Organizing Networks (SON) concept is increasingly being used as an approach for managing complex, dynamic mobile radio networks. In this paper we focus on the verification component of SON, which is the ability to automatically detect problems such as performance degradation or network instability stemming from configuration management changes. In previous work, we have shown how Key Performance Indicators (KPIs) that are continuously collected from network cells can be used in an anomaly detection framework to characterize the state of the network. In this study, we introduce new methods designed to handle scope changes. Such changes can include the addition of new KPIs or cells in the network, or even re-scoping the analysis from the level of a cell or group of cells to the network level. Our results, generated using real cellular network data, suggest that the proposed network-level anomaly detection can adapt to such changes in scope and accurately identify different network states based on all types of available KPIs.

A graph coloring approach for scheduling undo actions in self-organizing networks

In a mobile Self-Organizing Network (SON) a coordinator is necessary to avoid the execution of conflicting SON function instances. Typically, such a coordinator bases its decision to accept or reject a network parameter change request on a rule set that considers only known conflicts. Moreover, it does not observe the impact of approved changes on the network. For this reason, SON verification approaches have been specified to assess the impact of deployed configuration changes and identify those that are causing an undesired network behavior. Similarly to anomaly detection techniques, a SON verification mechanism has a mathematical model that specifies how the network behavior should look like and defines any behavior that significantly deviates form the expectations as abnormal. Furthermore, the outcome is a corrective action, also called an undo action, that sets network parameters to some previous configuration. The question that often remains unanswered is how conflicting undo actions should be scheduled. A SON coordinator does not have the knowledge to resolve them and may, therefore, prevent such from being deployed. In this paper we present a scheduling approach of such undo actions that uses minimum graph coloring in order to identify the sets of cells whose configuration can be safely rolled back. Our evaluation is split in two parts. In the first part we highlight the importance of our approach by observing a real Long Term Evolution (LTE) network. The second part is based on simulation data in which we show the ability of our method to keep the performance of the network at a high level.

A Post-Action Verification Approach for Automatic Configuration Parameter Changes in Self-Organizing Networks

In a mobile Self-Organizing Network (SON) a SON coordinator is required to prevent the execution of conflicting SON function instances. Usually, such a coordinator is responsible for conflict prevention and resolution and does not consider the fact that the activity of SON function instances may induce an undesired network behavior, like a performance degradation. In this paper, we propose an approach for the verification of Configuration Management (CM) changes induced by the activity of function instances. We have developed the SON verification function which is triggered when CM change requests get acknowledged by a SON coordinator. It analyses the resulting Performance Management (PM) data and in the case it detects an undesired network behavior it sends a request to the coordinator to revert the changes responsible for that to happen. Furthermore, our function takes the impact area of a SON function instance into account to determine the scope of verification. It also takes the impact time of function instances that have been active into consideration as it tries to identify the CM changes that have possibly caused an undesired network behavior. Simulations have shown that the tight integration of our function with a SON coordinator provides a solution for overcoming such problems and improving the overall network performance.

A Constraint Optimization-Based Resolution of Verification Collisions in Self-Organizing Networks

The verification of Configuration Management (CM) changes is an important step in the operation of a Self-Organizing Network (SON). In order to perform its tasks, a verification mechanism makes use of an observation and a correction time window. In the first window it assesses the impact of deployed CM changes by monitoring the networks Performance Management (PM) data. Furthermore, it partitions the network in one or more verification areas, detects anomalies within them, and generates CM undo requests, each having the purpose to set CM parameters to some previous state. In the second window it deploys those requests to the network. However, two or more verification areas might be overlapping and share anomalous cells. As a consequence, we have verification collisions preventing two or more generated CM undo requests to be deployed at same time. Thereby, the verification mechanism might not be able to deploy all generated CM undo actions for the given correction window. In this paper, we propose a method that makes use of constraint optimization techniques to identify which requests can be merged together in order to meet the time requirement. We achieve our goal by using constraint softening based on so-called performance rating values of the requests. We evaluate our method in two different scenarios. First, we highlight the need for handling verification collisions by observing CM and PM data of a real Long Term Evolution (LTE) network. Second, a simulation study shows the ability of our method to keep the network performance at a high level.