Apostolos Kousaridas

Standardizing a reference model and autonomic network architectures for the self-managing future internet

Research efforts at network design in the area of Autonomic Networking and Self-Managing Networks have reached a maturity level that forms a strong foundation toward standardization of architectural principles of the Self-Managing Future Internet. Therefore, an Industry Specification Group (ISG) on Autonomic network engineering for the self-managing Future Internet (AFI) has been established under the auspices of the European Telecommunications Standards Institute (ETSI). Upon its creation, the main stakeholders agreed to harmonize the previous developments and the most recent trends in the very vital field of autonomic and self-managing networks. Particularly, the life cycle of AFI is structured by Work Items providing the foundation for ETSI Group Specifications. So far AFI has been focusing on scenarios, use cases, and requirements for the autonomic/selfmanaging Future Internet, as well as on architectural reference model for autonomic networking and self-management. Most recently, AFI has continued with a new Work Item on requirements analysis and specification of implementation-oriented solutions for autonomics and self-management. At the same time, as a part of the global ecosystem, AFI is establishing strategic liaisons with the standards developing organizations and research community.

Future internet elements: cognition and self-management design issues

The scope of this paper is to introduce an innovative paradigm for cognitive self-managed elements of the Future Internet. The present Internet model is based on clear separation of concerns between protocol layers, with intelligence moved to the edges, and with the existent protocol pool targeting user and control plane operations with less emphasis on management tasks. Future Internet shall be engineered based on cognitive behavior with a high degree of autonomy, by proposing the operation of self-managed Future Internet elements around a novel hierarchical feedback-control cycle. The concepts are based on a hierarchical Distributed Cognitive cycle for System & Network Management (DC-SNM) which aims at facilitating the promotion of distributed management. The management approach encompasses a hierarchical distribution of cognitive cycles, breaking down the execution and decision making levels to (autonomic) network elements, network domain types and up to the service provider realm in order to address management, dynamic organization and (re)configuration of future internet elements.

Optimisation of Radio Access Network Operation Introducing Self-x Functions: Use Cases, Algorithms, Expected Efficiency Gains

With the deployment of next generation (4G) mobile radio systems an additional radio access network is established. A variety of different Radio Access Technologies (RATs) will be operated in parallel. In this framework, the Long Term Evolution (LTE) system, specified by 3GPP, will have to co-exist with WiMAX, mobile 2G/3G networks and Wireless Local Area Networks (WLANs). To cope with this increasing diversity and complexity mechanisms for self-optimisation, self-organisation, self-healing, self-configuration (self-x) are essential to guarantee cost efficient and high quality network operation. Within the project E³ (1) self-x functionalities for different use cases and different elements of a mobile radio access network are developed. Aim of this paper is to give and overview about the interworking of different self-x functionalities and to present three exemplary use cases. I. INTRODUCTION Evolution of mobile radio networks is driven by the demand for new, high bit rate consuming applications and services. The technical answer is the development of new and more powerful radio technologies and integration into existing mobile radio networks. This leads to significantly higher complexity and heterogeneity while pressure for maintaining manageability and cost efficiency of the networks is continuously increasing. E³ answer is introduction of solutions for obtaining higher flexibility and efficiency in usage of radio, hardware and computational resources by cognition, self- organisation and self-optimisation. This paper presents the interworking of cognitive radio self-x functions in E³ environments and hereafter three exemplary self-x use cases namely Handover Parameter Optimisation, Protocol Stack Self- Configuration & Topology Self-Organisation and Knowledge- based Proactive Context Handling.

Enhancing a Fuzzy Logic Inference Engine through Machine Learning for a Self- Managed Network

Existing network management systems have static and predefined rules or parameters, while human intervention is usually required for their update. However, an autonomic network management system that operates in a volatile network environment should be able to adapt continuously its decision making mechanism through learning from the system’s behavior. In this paper, a novel learning scheme based on the network wide collected experience is proposed targeting the enhancement of network elements’ decision making engine. The algorithm employs a fuzzy logic inference engine in order to enable self-managed network elements to identify faults or optimization opportunities. The fuzzy logic engine is periodically updated through the use of two well known data mining techniques, namely k-Means and k-Nearest Neighbor. The proposed algorithm is evaluated in the context of a load identification problem. The acquired results prove that the proposed learning mechanism improves the deduction capability, thus promoting our algorithm as an attractive approach for enhancing the autonomic capabilities of network elements.

On a synergetic architecture for cognitive adaptive behavior of future communication systems

The future area of communication systems is considered as a representative example of a complex adaptive organization, where several elements, with various computational capabilities and network resources, are interconnected. This evolution renders imperative the need for adaptable and scalable systems that operate in unpredictable environments, having self-management features and the ability to handle complexity. The scope of this paper is to describe a coherent architectural framework in order to support adaptive and cognitive behavior of future communication systems, forming synergies from the most microscopic up to the most macroscopic level. Cognitive mechanisms embedded at all scales of a communication system will enable its autonomous hypostasis, facilitating also its self-organization. The synergetic architecture is modeled using autonomic element, dynamical hierarchies, and self-similarity concepts.

Dynamic compartment formation for coverage optimization of cognitive wireless networks

This paper presents the functionalities involved in enabling the use of compartments in wireless access networks for achieving capacity and coverage optimization. Compartments are defined as dynamic medium-term federations of collaborating elements for intelligent organizational purposes. The work presented in the paper uses compartments as a self-management apparatus for emerging wireless access environments and proposes a compartment formation algorithm for achieving self-optimization of the coverage of access points. Facilitation of the compartment apparatus is founded in the synergy of self-management tools and the application of cognition in operations of Future Internet networks. The paper presents the details of cognition managers in access points for localized management by forming compartments and proceeds to the analysis as well as providing results in coverage optimizations of wireless networks.

Resource and Mobility Management in the Network Layer of 5G Cellular Ultra-Dense Networks

The provision of very high capacity is one of the big challenges of the 5G cellular technology. This challenge will not be met using traditional approaches like increasing spectral efficiency and bandwidth, as witnessed in previous technology generations. Cell densification will play a major role thanks to its ability to increase the spatial reuse of the available resources. However, this solution is accompanied by some additional management challenges. In this article, we analyze and present the most promising solutions identified in the METIS project for the most relevant network layer challenges of cell densification: resource, interference and mobility management.

Self-Management for Access Points Coverage Optimization and Mobility Agents Configuration in Future Access Networks

A key challenge for the management systems of future networks is the reduction of human interventions in the fundamental management functions. These include mechanisms that render the networks capable to configure, optimize, heal and protect itself, but also handle the emerging complexity. Demands for the future internet networks mandate the rapid assessment of the feasibility of such cognitive management architectures that shall bridge the gap between conceptual design and practical network deployments. In this paper, a novel architecture is introduced, based on organized distribution of control feedback cycles at locations allocated across network’s operational elements. Two realisations of self-management in the operations of wired and wireless access network segments are presented. The first one is focused on organization of the wireless access regions in networks by the use of compartments of access points for enabling coverage optimization. A compartment-based approach facilitates the more efficient usage of network resources, exploiting local situation awareness and local optimisation features, according to the varying traffic needs. The second realization shows the control of mobility management processes in wired parts of access networks for balancing utilization of network resources. A dynamic deployment and re-configuration of mobility agents permit to tackle the problem of congestion induced by mobility agents in mobile protocols and combined with a dynamic access router assignment, network resources are efficiently balanced within the network. The paper concludes with findings and recommendations on how common principles of self-management evolve from design theory to practice.

Embedding cognition in wireless network management: an experimental perspective

The Future Internet promises to pervade our everyday lives by interweaving an increased variety of access technologies (cellular broadband, wireless hotspots, short range radios). Cognitive network management is a promising approach to cope with such access diversity and to enable dynamic adaptation of the network configurations and parameters. This article reveals the insights and conclusions of a unique real life implementation of a cognitive architecture, comprising software agents and artificial intelligence algorithms, and its deployment within a heterogeneous access network composed of a Broadband WiMAX base station and WiFi access points. We discuss the architectural challenges that this approach poses, and present the distributed software architecture we implemented. We detail the experimental access network we deployed and the algorithms that enable channel reselection and vertical assisted handover. We conclude with our findings and recommendations for the deployment of cognitive network management architectures and technologies in the Future Internet.

Coverage and Capacity Optimization of Self-Managed Future Internet Wireless Networks

Future Internet network management systems are expected to incorporate self-x capabilities in order to tackle the increased management needs that cannot be addressed through human intervention. Towards this end, Self-NET developed a self-management framework based on the introduction of cognitive capabilities in network elements. In this paper, the experimentation platform for “Coverage and Capacity Optimization of Self-managed Future Internet Wireless Network”, incorporating the self-management framework of Self-NET, is presented.