Christoph Frenzel

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.

Mooop – A Hybrid Integration of OWL and Java

Java is a widespread object-oriented programming language for implementing information systems because it provides means to express various domains of interest. Nevertheless, some fields like Health Care and Life Sciences are so complex that Java is not suited for their design. In comparison, the Web Ontology Language (OWL) provides various powerful modelling constructs and is used to formulate large, well-established ontologies of these domains. OWL cannot, however, be used alone to build applications. Therefore, an integration of both languages, which leverages the advantages of each, is desirable, yet not easy to accomplish. We present Mooop (Merging OWL and Object-Oriented Programming), an approach for the hybrid integration of OWL ontologies into Java systems. It introduces hybrid objects, which represent both an OWL and Java entity. We have developed a prototype of Mooop and evaluated it in a case study.

Automated rational recovery selection for self-healing in mobile networks

Self-healing is a key functionality of Self-Organizing Networks (SON). There have already been promising research results on Degradation Detection and Root Cause Diagnosis. However, the complex task of Recovery Selection, i.e., the determination of the best recovery action for an uncertain diagnosis of a degradation considering the operational goals of the network operator, is rarely investigated so far. In this paper, a two-step rational Recovery Selection system and its integration into a self-healing process for mobile networks is presented. Thereby, Recovery Selection first exploits technical recovery knowledge expressed in rules to determine the set of possible recovery actions for a degradation situation. Second, it determines the degree of rationality of these actions based on the operational goals of the operator. Therefore, it draws on decision theory in order to handle the uncertainty in the problem situation and the possibly conflicting preferences of the operator. The presented system enables automatic rational Recovery Selection which provides a baseline for self-healing in mobile networks.

SON management based on weighted objectives and combined SON Function models

The instrumentation of Self-Organising Network (SON) Functions in such way that they contribute to the operational objectives defined by a wireless network operator is a complex task. In order to enable automation of this configuration, we previously described the concept of a SON Objective Manager (SOM) which determines the best configuration based on conditional, ranked objectives and SON Function models describing the Key Performance Indicators (KPIs) optimised by some configuration. However, the simplicity of the models and the complexity of the design-time inference may limit the applicability of the initial concept. In this paper, we present an extended and enhanced run-time SOM with more expressive input models, namely, context-dependent, weighted operator objectives allowing a better trade-off and SON Function models defining the possible values of KPIs for some configuration. These improvements together allow for a broader application of the concept.

Detection and resolution of ineffective function behavior in Self-Organizing Networks

The Self-Organizing Network (SON) paradigm enables automated management of next generation mobile communication networks by introducing a set of autonomous functions that jointly achieve self-configuration, self-optimization, and self-healing. Each SON function is driven by a specific objective to optimize one or more Key Performance Indicators (KPIs). If a function encounters situations in which the given targets cannot be achieved, it can produce undesired behavior, resulting in an impaired SON. This paper presents the idea of SON Operational Troubleshooting function and outlines a concept for the automatic detection, analysis, and mitigation of operational problems.

Operational Troubleshooting-Enabled Coordination in Self-Organizing Networks

A Self-Organizing Network (SON) performs automated network management through the coordinated execution of autonomous functions, each aiming to achieve a specific objective like the optimization of a network Key Performance Indicator (KPI) value. However, there are situations in which a SON function cannot achieve its objective which can lead to disturbed SON operation and inferior performance. We present a SON Operational Troubleshooting (SONOT) SON function that is able to detect such problematic situations and trigger respective countermeasures. Thereby, it can exploit regular SON functions as probes in order to improve problem detection. Simulations show that the tight integration of the SONOT function with SON coordination provides means to automatically overcome the problems and improve overall network performance.

A fuzzy, utility-based approach for proactive policy-based management

Policy-based Management with rules is a wide-spread approach for operations automation. However, the continuous pressure for decreasing operational costs and increasing reliability of the systems lead to new challenges. Unfortunately, current Policy-based Management Systems lack the ability to act proactively along operational objectives in an autonomous manner in order to face these challenges. In this paper, we present a Policy-based Management System based on a Fuzzy Logic System that attempts to avoid problematic system states before they occur and that is guided by operator objectives expressed as utilities. Our approach can be seen as an extension of current rule-based Policy-based Management Systems, thus, requiring a reduced implementation effort.

Demonstrator for utility-based SON management

Self-Organising Networks (SON) are a key enabler for the automation of managing and operating a cellular Radio Access Network (RAN). The purpose of SON management is thereby to jointly instrument and configure a multitude of independent SON functions according to objectives defined by the Mobile Network Operator (MNO). Existing approaches such as Policy-based SON Management (PBSM) use action policies for decision making. Utility-based SON Management (UBSM), instead, uses utility function policies, where the utility reflects the degree of satisfaction of an objective for a certain network state. The demonstrator described in this paper represents a graphical frontend for the simulation environment that allows to compare the effectiveness of UBSM with an unmanaged SON implementation.

Policy-Based SON Management Demonstrator

A Self-Organising Network (SON) represents an approach where the optimisation of a mobile radio network is automated through a set of independently operating SON functions. These SON functions, however, require to be configured in order to allow for an optimised network performance with respect to technical objectives defined by the network operator. The SEMAFOUR demonstrator shows a concept for SON management based on operator objectives, where the SON function configuration is performed in an automated way. The demonstrator illustrates the different aspects and complexity of the management of SON in a heterogeneous network.

Objective-driven coordination in self-organizing networks

The operation of a Self-Organizing Network (SON) such that it achieves the objectives of a cellular network operator is a complex task. The previously presented SON objective manager enables the automatic management of SON functions based on formalized operator objectives on network Key Performance Indicators (KPIs). However, this approach initially did not consider SON coordination which resolves runtime conflicts between concurrently executed SON functions. We present an objective-driven SON coordination which adapts the SON objective manager approach to SON function conflict resolution. Therefore, the SON objective manager concept is based on multiattribute utility theory and the conflict resolution is modeled as a constraint optimization problem. As a result, this approach coordinates the execution of SON functions such that the operator objectives are satisfied as quickly as possible.