Abstract
The need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain the required performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of software-defined networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and suggestions for further work are listed.
Highlights
In the past few years, the telecommunications industry has woken up to the potential use of artificial intelligence (AI) and machine learning to automate and simplify network design, management, and operations
In [3], mobile radio access networks (RANs), especially for 5G, are discussed, and it is suggested that AI can streamline RANs for massive multiple-input–multipleoutput optimization with reinforcement learning (RL) [4] so that each cell self-adapts to changing scenarios and traffic, increasing throughput by 20% and optimizing speed for users that have low throughput
We will go further into how software-defined networks (SDNs) can incorporate self-awareness using the cognitive packet protocol based on smart packets (SPs) that gather measurements and information in the network and using an RL-based decision engine to modify the paths of flows dynamically to achieve better Quality of Service (QoS)
Summary
In the past few years, the telecommunications industry has woken up to the potential use of artificial intelligence (AI) and machine learning to automate and simplify network design, management, and operations. In [3], mobile radio access networks (RANs), especially for 5G, are discussed, and it is suggested that AI can streamline RANs for massive multiple-input–multipleoutput optimization with reinforcement learning (RL) [4] so that each cell self-adapts to changing scenarios and traffic, increasing throughput by 20% and optimizing speed for users that have low throughput. This article reviews the advances made since those early days in support of the current vision to use data analytics and machine learning to automate network operations through self-aware networks. Other work has considered the relation between internal representation of self-awareness and the capacity to take action [14]
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