David L. Raymer

End-to-end model driven policy based network management

The continued movement towards converged networks changes the focus to building application services that enable customers to move between different types of service providers based on their needs. Policy management becomes paramount for the rapid deployment and management of these application services. This paper presents the concept of a policy continuum and discusses the importance of modeling and natural languages in the presence of the policy continuum, resulting in a novel architecture suitable for autonomic computing

Implementing Next Generation Services Using Policy-Based Management and Autonomic Computing Principles

Motorolas seamless mobility solution is one specific example of generic next generation services. Such services use applications that provide a world of simple, intuitive, and uninterrupted access to information based on the users context. Fundamental to this concept are technology enablers that facilitate gathering and processing of information, supported by communication across diverse environments, devices and networks. This requires technical innovation on many fronts, all anchored by an architecture that can be customized to adapt to different levels of user sophistication. A main goal of the architecture is to provide customizable user experiences. This paper describes a unique architecture that is based on autonomic computing principles. Its decisions are governed by the DEN-ng policy management architecture, and based on a set of extensions to the TeleManagement Forum (TMFs) next generation operational systems and software (NGOSS) technology-neutral architecture

The Design of a New Policy Model to Support Ontology-Driven Reasoning for Autonomic Networking

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 Directory Enabled Networks next generation (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.

The design of a novel context-aware policy model to support machine-based learning and reasoning

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 are all different, and hence require different data in different formats. This complicates the construction of context from diverse information sources. This paper describes a new version of the DEN-ng context-aware policy model, which is part of the FOCALE autonomic network architecture. This model has been built using three guiding principles: (1) both the context model and the policy model are rooted in information models, so that they can govern managed entities, (2) each 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 that supports machine-based reasoning and learning can be developed from it.

A model-based approach to adding autonomic capabilities to network fault management system

Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.

Providing seamless mobility using the FOCALE autonomic architecture

Existing wireless networks have little in common, as they are designed around vendor-specific devices that use specific radio access technologies to provide particular functionality. Next generation networks seek to integrate wired and wireless networks in order to provide seamless services to the end user. Seamless Mobility is an experiential architecture, predicated on providing mechanisms that enable a user to accomplish his or her tasks without regard to technology, type of media, or device. This paper examines how autonomic mechanisms can satisfy some of the challenges in realizing seamless mobility solutions.

A Case Study: A Model-Based Approach to Retrofit a Network Fault Management System with Self-Healing Functionality

Adding self-healing capabilities to network management systems holds great promise for delivering important goals, such as QoS, while simultaneously lowering capital expenditure, operation cost, and maintenance cost. In this paper, we present a model-based approach to add self-healing capabilities to a fault management system for cellular networks. We propose a generic modeling framework to categorize software failures and specify their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting functionality. While self- monitoring oversees the environmental conditions and system behavior, self-healing is accomplished by instrumenting the system with self-adjusting operations. We include a case study on a prototype intelligent network fault management system to illustrate this approach by showing how these autonomic capabilities can be added and deployed. Specifically, these autonomic capabilities are derived from self-model specifications, and are used to mitigate the risk of specified failures and maintain the health of the system in response to different types of faults encountered.

Adding Autonomic Capabilities to Network Fault Management System

Completeness and feasibility of a specification are important properties for the assurance of a valid and correct implementation, but they are extremely difficult to be formally verified. In this paper, we describe an inspection method for analyzing the completeness and feasibility of an operation specified using pre- and postconditions. The characteristic of the method is that it utilizes test case generation criteria in forming questions of checklist and test case generation process as a reading technique for inspection. We formally define the properties, the criteria for test case generation, and discuss how they are used for inspection in practice.In this paper, we propose an adaptive framework for adding the most desired aspects of autonomic capabilities into the critical components of a network fault management system. The aspects deemed as the most desirable are those that have a significant impact on system dependability, which include self-monitoring, self-healing, self-adjusting, and self-configuring. Self-monitoring oversees the environmental conditions and system behavior, building a consciousness ground to support self-awareness capabilities. It is responsible for monitoring the system states and environmental conditions, analyzing them and thus detecting and identifying system faults/failures. Upon detection, self-healing operations is enabled to respond (i.e. take proper actions) to the identified faults /failures. These actions are usually accomplished by self-configuring and self- adjusting the corresponding system configurations and operations. Together, all self-*approaches complete an adaptive framework and offer a sound solution towards high system assurance.