Chair of Network Architectures and Services Department of Informatics Technical University of Munich Verification of Autonomic Actions in Mobile Communication Networks Dissertation Tsvetko Ivanchev Tsvetkov Network Architectures and Services NET-2017-07-1 TECHNISCHE UNIVERSITÄT MÜNCHEN Institut für Informatik Lehrstuhl für Netzarchitekturen und Netzdienste Verification of Autonomic Actions in Mobile Communication Networks Tsvetko Ivanchev Tsvetkov Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Prof. Dr. Uwe Baumgarten Prüfer der Dissertation: 1. Prof. Dr.-Ing. Georg Carle 2. Prof. Dr. Rolf Stadler KTH Royal Institute of Technology, Stockholm, Sweden Die Dissertation wurde am 27.02.2017 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 29.06.2017 angenommen. Cataloging-in-Publication Data Tsvetko Ivanchev Tsvetkov Verification of Autonomic Actions in Mobile Communication Networks Dissertation, July 2017 Chair of Network Architectures and Services Department of Informatics Technical University of Munich ISBN 978-3-937201-56-6 DOI 10.2313/NET-2017-07-1 ISSN: 1868-2634 (print) ISSN: 1868-2642 (electronic) Network Architectures and Services NET-2017-07-1 Series Editor: Georg Carle, Technical University of Munich, Germany c 2017 Technical University of Munich, Germany Abstract Mobile communication networks are highly complex systems which are comprised of a set of various technologies and automation mechanisms. Today, when we talk about cellular networks we usually think of networks that support the latest standard, which are also backwards compatible to standards developed in the past. The most prominent example is Long Term Evolution (LTE) and the supported fallback to the Global System for Mobile Communications (GSM) or the Universal Mobile Telecommunications Sys- tem (UMTS). Moreover, mobile networks are comprised of a high variety of Network Elements (NEs), e.g., the LTE Radio Access Network (RAN) is typically composed of macro as well as micro and pico cells. As a result, management and automation concepts have been developed to deal with the configuration, optimization, and troubleshooting of the network. One example are Self-Organizing Networks (SONs) which aim to reduce human intervention and automatize those processes. However, having automated entities that actively reconfigure the network raises the question of how to assess their actions and what to do in case they negatively affect the performance of the network. In the terms of SON, those entities are referred to as SON functions, also called online SON, which are implemented as closed control loops that actively monitor Performance Management (PM) / Fault Management (FM) data, and based on their objectives change Configuration Management (CM) parameters. In addition, there are offline SON methods which are comprised of sophisticated optimiza- tion algorithms that require more knowledge about the network and also more time to compute a new configuration. Usually, offline algorithms utilize simulation tools to find the most suitable configuration setup. Nevertheless, both online and offline approaches may face difficulties while they optimize the network and may produce suboptimal or even configurations harming per- formance. The reasons are manifold: they may have inaccurate information about the network, they may not know whether there is another ongoing SON activity, or they may simply have a very limited view on the network. For this reason, in the mobile network area troubleshooting as well as anomaly detection and diagnosis approaches are used to analyze the network performance and state whether configuration changes had a negative impact on the NEs. Thereby, they may also suggest corrective actions that improve the current network state. Unfortunately, such approaches often neglect issues that may occur while rolling back II Abstract configuration changes. First and foremost, verification collisions are often underestimated. They prevent two or more corrective actions from being simultaneously executed and, therefore, delay the process until the network is restored to a previous stable state. Those collisions may also result in the inability to process all corrective actions, that is, we have an over-constrained problem that has no solution for the given conditions. Moreover, the issue of detecting weak collisions, i.e., collisions that may turn out to be false positive, and SON function transactions is neglected as well. Second, dynamic changes in the network topology, e.g., such induced by energy saving mechanisms are often neglected. They may result in incomplete cell profiles and gener- ally complicate the process of assessing the performance impact of other configuration changes. In the worst case scenario, they may lead to the rollback of changes that are necessary for the flawless network operation. In this thesis, a concept for verifying configuration and topology changes is presented. It is realized as three step process that splits the network into areas of interest, assesses their performance, and generates a corrective action plan. The set of corrective actions are undo actions, which restore a cell’s configuration to a previous state, and topology corrective actions, which either enable or disable cells. The presented concept utilizes techniques from graph theory and constraint optimization to resolve the aforementioned issues. For example, it makes use of a Minimum Spanning Tree (MST) clustering technique that eliminates weak collisions. Furthermore, it utilizes Steiner trees to generate the necessary topology corrective actions. The presented concept is evaluated in a simulation environment and observations based on real data are made as well. Acknowledgments This work would not have been possible without the advice and support of many people. Here, I would like to take the opportunity to the express my thankfulness and great appreciation. First of all, I would like to thank Prof. Dr. Georg Carle for giving me the opportunity to write my dissertation under his supervision. I am deeply grateful for his support, advice, and freedom he gave during those four years. I also would like to thank Prof. Dr. Rolf Stadler for being my second examiner and Prof. Dr. Uwe Baumgarten for heading my committee. With the same gratitude, I would like to thank Dr. Henning Sanneck for enabling me to do my research at Nokia. Our numerous discussions over the years were invaluable. Second, I would like to thank all of my colleagues without whom I would not have been able to make it this far. Thank you, Janne Ali-Tolppa, for the various discussions, meetings, and suggestions you gave me. This thesis would not have become reality without your encouragement and your support on the ideas I had. I really enjoyed working with you. Thank you, Christian Mannweiler, for your help, support, and advice. I really do appreciate the time you always had to review my ideas and research results. Thank you, Christoph Frenzel, for your helpfulness and for constantly challenging my ideas. It was amazing to work with you side-by-side, especially during the development phase of the simulation system. I have learned a lot of things from you. Also, special thanks to Szabolcs Nováczki and Christoph Schmelz for making my entry into the research field to go as smooth as possible. Last but not least, I would like to thank my parents, Marusya Monova and Ivancho Monov, for always been there for me whenever I needed them. You have always believed in me, and have always supported me in everything. Thank you, for your unconditional love throughout my life. Munich, July 2017 Tsvetko Ivanchev Tsvetkov Contents I Introduction and Background 1 1 Introduction 3 1.1 Automation of Mobile Networks . .3 1.2 Anomaly Detection and Configuration Restoration . .4 1.3 Research Objectives . .5 1.4 Approach and Contributions . 10 1.4.1 Identification of Requirements, Issues, and Causes . 10 1.4.2 The Concept of SON Verification . 11 1.4.3 Implementation of the Verification Concept . 13 1.4.4 Concept Evaluation . 13 1.5 Thesis Outline . 14 1.6 Publications . 17 1.6.1 Publications in the Context of this Thesis . 17 1.6.2 Publications in the Context of Other SON Related Areas . 19 1.7 Statement on the Author’s Contributions . 19 1.8 Note on Terminology . 20 1.9 Document Structure . 21 1.9.1 Reference to Author’s Publications . 21 1.9.2 Terminology and Notes . 21 1.9.3 Objectives and Tasks . 21 1.9.4 Summary and Findings . 22 1.9.5 Appendix . 22 2 Background 23 2.1 Mobile Communication Networks . 23 2.1.1 Generations of Communication Standards . 23 2.1.2 Collaboration and Standardization Process . 24 2.1.3 Radio Access Network . 25 2.1.4 Core Network . 27 2.1.5 Protocol Architecture . 28 2.2 Mobile Network Management . 28 2.2.1 Operation, Administration and Management Architecture . 29 VI Contents 2.2.2 Network Management Data . 30 2.2.3 Granularity Period . 31 2.3 Self-Organizing Networks . 31 2.3.1 SON Categories . 32 2.3.2 SON Functions . 34 2.3.3 Other Application Areas . 36 2.4 Summary . 38 II Problem Analysis and Related Work 39 3 Problem Analysis 41 3.1 Motivation . 42 3.1.1 Troubleshooting and Anomaly Detection . 42 3.1.2 The Term "Verification" . 44 3.2 Challenges for a Verification Process . 45 3.2.1 SON Function Transactions . 45 3.2.2 Verification Collisions . 47 3.2.3 Over-Constrained Corrective Action Plan . 49 3.2.4 Weak Verification Collisions . 51 3.2.5 Dynamic Topology Changes . 52 3.3 Factors Influencing a Verification Process . 55 3.3.1 Availability of PM and CM Data . 55 3.3.2 Statistical Relevance of PM Data . 57 3.3.3 Network Density, Heterogeneity, and Topology . 58 3.3.4 Characteristics of CM Parameter Changes . 60 3.4 Summary . 63 4 Related work 67 4.1 Pre-Action Analysis .