Policy-driven workflows for mobile network management automation

Romeikat, Raphael; Bauer, Bernhard; Bandh, Tobias; Carle, Georg; Sanneck, Henning; Schmelz, Lars Christoph

doi:10.1145/1815396.1815650

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…In [178], the authors use Q-learning to deal with the conflict resolution between two SON instances. Decision trees have been proposed in [186] to properly adjust Remote Electrical Tilt (RET) and transmission power. Additionally, in [179] the authors provide a functional architecture that can be used to deal with the conflicts generated by the concurrent execution of multiple SON functions.…”

Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

170

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In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

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Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

170

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“…Guard functions have been proposed in [4] to detect undesirable network behaviors and trigger countermeasures. Decision trees have been proposed in [20] for properly adjusting remote electrical tilt (RET) and transmission power. Q-learning, as a RL method, has been proposed in [8] to take advantage of experience gained in past decisions, in order to reduce the uncertainty associated with the impact of the SON coordinator decisions when picking an action over another to resolve conflicts.…”

Section: Related Workmentioning

confidence: 99%

Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Moysen

Giupponi

2015

J Wireless Com Network

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We consider a distributed SON (D-SON) architecture where the interaction of different self-organizing network (SON) functions negatively affect the performances of the system. This is referred to in 3rd Generation Partnership Project (3GPP) as a SON conflict, which needs to be handled by means of a self-coordination framework. We focus on a functional architecture and a theoretical framework based on the theory of Markov decision process (MDP) for the self-coordination of different actions taken by different SON functions. In order to cope with the complexity of the overall SON problem, we subdivide the global MDP modeling the long-term evolution (LTE)-enhanced node base station (eNB) onto simpler subMDPs modeling the different SON functions. Each sub-problem is defined as a subMDP and solved independently by means of reinforcement learning (RL), and their individual policies are combined to obtain a global policy. This combined policy can execute several actions per state but can introduce policy conflicts. We focus on the specific SON conflict generated by the concurrent execution of coverage and capacity optimization (CCO) and inter-cell interference coordination (ICIC) SON functions, which may require to update the same parameter, i.e., the transmission power level. The coordination among the different actions selected by the conflicting use cases is achieved by means of a coordination game where the players are the subMDPs and the actions and rewards are those provided by means of a RL approach. Performance evaluation is carried out in a ns3 release 8 compliant LTE system simulator, and it shows that our self-coordination approach provides satisfying solutions in terms of system performances for both the conflicting SON functions.

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“…A well-known application area is network management, where policies are widely used for performing configuration processes. The usage of policy-based systems for the management of mobile networks was recently considered in [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Specification and Refinement of Domain-Specific ECA Policies

Romeikat

Bauer

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Policy-based management is a flexible approach for the management of complex systems as policies make context-sensitive and automated decisions. For the effective development of policies it is desired to specify policies at a high level of abstraction initially, and to refine them until they are represented in a machine-executable way. We present an approach for the specification and the automated refinement of domainspecific event-condition-action (ECA) policies. Domain-specific policies use domain-specific concepts within their event, condition, and action parts. The approach is generic as it can be applied to any domain and supports a flexible number of abstraction layers. It is applied to the network management domain and demonstrated with policies for signal quality management in a mobile network.

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Policy-driven workflows for mobile network management automation

Cited by 12 publications

References 5 publications

From 4G to 5G: Self-organized network management meets machine learning

From 4G to 5G: Self-organized network management meets machine learning

Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Specification and Refinement of Domain-Specific ECA Policies

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