The Design of a New Policy Model to Support Ontology-Driven Reasoning for Autonomic Networking John Strassner 1 , José Neuman de Souza 2 , Sven van der Meer 1 , Steven Davy 1 , Keara Barrett 1 , Dave Raymer 3 , Srini Samudrala 3 1 Waterford Institute of Technology, Telecommunications Software & Systems Group, Waterford, Ireland {jstrassner, vdmeer, sdavy, kbarrett}@tssg.org 2 Federal University of Ceará, Brazil neuman.souza@gmail.com 3 Motorola Labs {david.raymer, srini.samudrala}@motorola.com Abstract The purpose of autonomic networking is to manage the business and technical complexity of networked components and systems. However, the lack of a common lingua franca makes it impossible to use vendor-specific network management data to ascertain the state of the network at any given time. Furthermore, the tools used to analyze management data, which include information and data models, ontologies, machine learning algorithms, and policy languages, are all different, and hence require different data in different formats. This paper describes a new version of the DEN- ng policy model, which is part of the FOCALE autonomic network architecture. This new policy model has been built using three guiding principles: (1) the policy model is rooted in information models, so that it can govern managed entities, (2) the model is expressly constructed to facilitate the generation of ontologies, so that reasoning about policies constructed from the model may be done, and (3) the model is expressly constructed so that a policy language can be developed from it. Keywords: Autonomic Networking, FOCALE Autonomic Architecture, Next Generation Services, Ontology-Based Management, Policy Management, Semantic Reasoning. I. INTRODUCTION The business, technical, and even social aspects of systems have increased dramatically in complexity, requiring new technologies, paradigms and functionality to be introduced to cope with these challenges [1]. This increase in complexity has made it almost impossible for a human to manage the different operational scenarios that are possible in today’s communication systems. While IP network management problems have been extensively documented [1][5][8][17][24], wireless systems present even more difficult problems. For example, wireless failures are usually not obtainable from a set of attributes – they must be inferred. Key performance and quality indicators (KPIs and KQIs) are calculated to provide a machine-interpretable view of system quality as perceived by the end user for a particular type of wireless system for a specific set of radio access technologies (RATs). However, current RATs use a set of non-compatible standards and vendor-specific functionality. This is exacerbated by current trends, such as network convergence (which combine different types of wired and wireless networks), as well as future multi access mode devices [2] and cognitive networks [3], in which the type of network access can be dynamically defined. The vision of Seamless Mobility [4] is even more ambitious – the ability for the user to get and use data independent of access mode, device, and media. Part of the allure of Policy-Based Network Management (PBNM) [5] was its simplicity in providing different services to different users while automating device, network and service management. However, most PBNM systems have been low-level systems that manage changes in commands for routers, switches, and firewalls. Hence, there is no link between business needs and the configuration of network resources and services. In addition, relatively new concepts, such as using context changes to determine which network services and resources to modify, are not present. Our approach for solving this problem is realized as the FOCALE autonomic architecture [8], and is based on five key concepts. First, the use of a shared information model is required in order to harmonize the different data models that are used in Operational and Business Support Systems (OSSs and BSSs). Second, since information and data models are not capable of representing the detailed semantics required to reason about behavior, we augment our use of knowledge extracted from information and data models with ontologies. Third, we define a new, enhanced policy model that, while constructed as an information model, has been specifically designed to be able to generate ontologies for governing behavior. Fourth, we link this policy model to a new context model, so that policies can be written that adapt offered resources and services to sensed context changes. Finally, we outline how policies are used, together with machine learning and reasoning, to build a new adaptive control architecture. The organization of this paper is as follows. Section 2 provides a brief introduction to autonomic computing and networking. Section 3 summarizes current PBNM approaches. Section 4 describes our new policy model in detail. Section 5 links this model to a context model. Section 6 describes initial progress that we have made in building an adaptive control architecture. Section 7 summarizes the paper.