Machine Learning for Cognitive Network Management (original) (raw)

Understanding autonomic network management: A look into the past, a solution for the future

Computer Communications, 2018

The evolution of mobile network technologies and their vertical integration, heterogeneity of applications, and the advent of sophisticated end-user devices have continuously been expanding the complexity of network management tasks. In addition, there is a significant urge for the dynamic reconfiguration of networks to meet operators' costs and to achieve their performance objectives. These facts substantiate the idea of pushing the classical human dependent network management approaches out of the equation to a great extent. The vast scope of network management makes it difficult to have a common understanding and definition, which is often noticeable in different research articles. The situation is further worsened by the network evolution timeline that traverses several technological shifts, such as the time when computer networks and mobile networks were far apart, to the time of fully IP-based and converged networks. Hence, one of the main aims of this paper is to provide a study of the network management evolution in general and in particular the concepts of autonomic network management, so that researchers may be equipped to understand the involved concepts. To achieve the aforementioned objective, the authors carried out an elaborate analysis of the different network management approaches, mapped them to a timeline, and discussed their features. This analysis sets the stage for an extensive discussion of the enabling concepts of autonomic network management, followed by a survey of research projects targeting the advancement of the autonomic networking vision. Having identified incomplete realizations of autonomic network management due to simplifying assumptions, this paper focused on the relevant aspects of architectural construction with the presentation of the core challenges to be addressed so as to realize a fully autonomic network management framework. These challenges led us to reconstruct the design goals that the contributions of this work were built upon. The first proposal of this paper is to deploy intelligent software agents on different hierarchical layers of the proposed mobile network architecture. The agents implement different stages of cognitive control loops and contribute to learning algorithms for various management tasks. CoDIPAS-RL learning framework is used for layer specific learning decisions. To advance the autonomic network management, the authors also propose a novel idea of self-learning that enables the meta-learning vision. This paper concludes with a discussion on the implementation of our autonomic network management framework and with a use case that shows the performance of the proposed approach.

Autonomia: an autonomic computing environment

Conference Proceedings of the 2003 IEEE International

The prolifeation of Internet technologies, services and devices, have made the current networked system designs, and management tools incapable of designing reliable, secure networked systems and services. In fact, we have reached a level of complexity, heterogeneity, and a rapid change rate that our information infiastructure is becoming unmanageable and insecure. This had led researchers to consider alternative designs and management techniques that are based on strategies used by biological systems to deal with complexity, heterogeneity and uncertainty. The approach is referred to as autonomic computing. An autonomic computing system is the system that has the capabilities of being sevdefining, self-healing, self-configwing, self-optimizing, etc. In this paper, we present our approach to implement an autonomic computing infastructure, Autonomia that provides dynamically programmable control and management services to support the development and deployment of smart (intelligent) applications. The A UTONOMIA environment provides the application developers with all the tools required to specifj, the appropriate control and management schemes to maintain any quality of service requirement or application attribute/firnctionality (e.g., perjormance, fault, security, etc.) and the core autonomic middleware services to maintain the autonomic requirements of a wide range of network applications and services. We have successfully implemented a proof-of-concept prototype system that can support the self-configuring, self-deploying and selfhealing of any networked application.

Self-managed Computer Systems: Foundations and Examples

2019

The traditional approach to managing complex computer systems is to use a cadre of skilled IT professionals who use monitoring tools in order to detect when problems arise. They are then able to use their skills and experience to determine what actions should be taken to solve the problems. This approach is no longer viable for highly complex, networked computer information systems that have numerous configuration knobs, and operate in environments that vary with time at a very high rate. In this case, one cannot expect that design-time configurations will make the system operate optimally at run-time. For that reason, complex systems need to manage themselves using controllers that make the systems self-configuring, self-optimizing, self-healing, and self-protecting. This paper provides a formalism to describe self-managed systems and discusses concrete examples that illustrate how these properties are enforced by controllers in a variety of domains including cloud computing, fog/c...

The role of ontologies in autonomic computing systems

Ibm Systems Journal, 2004

The goal of IBM's autonomic computing strategy is to deliver information technology environments with improved self-management capabilities, such as self-healing, selfprotection, self-optimization, and selfconfiguration. Data correlation and inference technologies can be used as core components to build autonomic computing systems. They can also be used to perform automated and continuous analysis of enterprise-wide event data based upon userdefined configurable rules, such as those intended for detecting threats or system failures. Furthermore, they may trigger corrective actions for protecting or healing the system. In this paper, we discuss the use of ontologies as a high-level, expressive, conceptual modeling approach for describing the knowledge upon which the processing of a correlation engine is based. The introduction of explicit models of state-based information technology resources into the correlation technology approach allows the construction of autonomic computing systems that are capable of dealing with policy-based goals on a higher abstraction level. We demonstrate some of the benefits of this approach by applying it to a particular IBM implementation, the eAutomation correlation engine.

Autonomic Computing Now You See It, Now You Don’t

Lecture Notes in Computer Science, 2009

With the rapid growth of web services and socio-technical ecosystems, the management complexity of these modern, decentralized, distributed computing systems presents significant challenges for businesses and often exceeds the capabilities of human operators. Autonomic computing is an effective set of technologies, models, architecture patterns, standards, and processes to cope with and reign in the management complexity of dynamic computing systems using feedback control, adaptation, and self-management. At the core of an autonomic system are control loops which sense their environment, model their behavior in that environment, and take action to change the environment or their own behavior. Computer science researchers often approach the design of such highly dynamical systems from a software architecture perspective whereas engineering researchers start with a feedback control perspective. In this article, we argue that both design perspectives are needed and necessary for autonomic system design.

Autonomic Computing: An Overview

Lecture Notes in Computer Science, 2005

The increasing scale complexity, heterogeneity and dynamism of networks, systems and applications have made our computational and information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for system and application design, which is based on strategies used by biological systems to deal with similar challengesa vision that has been referred to as autonomic computing. The overarching goal of autonomic computing is to realize computer and software systems and applications that can manage themselves in accordance with high-level guidance from humans. Meeting the grand challenges of autonomic computing requires scientific and technological advances in a wide variety of fields, as well as new software and system architectures that support the effective integration of the constituent technologies. This paper presents an introduction to autonomic computing, its challenges, and opportunities.

Autonomic Computing: The New Vision of Computing

2020

Various projects have been undertaken by various software companies to create the world of self-managing and intelligent computers that requires little or no human interaction. Self-management capabilities include self-healing, dynamic workload management, self-protection. The intelligent computers would be capable of handling simple tasks, such as correcting system failures, configuring themselves by installing new operating system software and data automatically, performing a wider variety of tasks, while crashing less often.