Abstract In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework the decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing a strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.
A survey of the most important approaches in autonomic network management may be found in @cite_39 and @cite_25 . However, it is worth noting that the most common concepts towards self-management are related to control theoretic approach @cite_64 , biological inspired mechanisms @cite_43 and game theory @cite_26 .
@cite_64 @cite_26 @cite_39 @cite_43 @cite_25
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The current complexity of network management has helped drive the need for autonomic capabilities. The vision of autonomic network management provides the ability for network devices to cooperatively self-organise and self-govern in the support of high level business goals. These principles are inspired by biological systems. In this paper, we propose key self-organisation and self-governance techniques that are drawn from principles of molecular biology including (i) blood glucose homeostasis, (ii) reaction diffusion like principles, (iii) microorganism mobility using chemotaxis techniques, and (iv) hormone signaling. Preliminary simulation results have also been presented to validate our model. Autonomic Network Management (ANM) has the goal of increasing reliability and performance while reducing management cost using various automated techniques. These range from agent-based approaches relying on explicit models and ontologies to emergent techniques relying on gossip protocols, swarming algorithms or other biologically inspired work. In this paper, we review the failures, growing pains and successes of earlier techniques for automated and adaptive network control and management, from the simple control loops in TCP and OSPF to the more complicated emergent behaviors of BGP dynamics and overlay routing. From these examples we extract several lessons relevant to ongoing research in autonomic network management. Over the last decade, a significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing networks. Autonomic networking calls for automated decisions for management actions. This can be realized through a set of pre-defined network management policies engineered from human expert knowledge. However, engineering sufficiently accurate knowledge considering the high complexity of today's networking environment is a difficult task. This has been a particularly limiting factor in the practical deployment of autonomic systems. ML is a powerful technique for extracting knowledge from data. However, there has been little evidence of its application in realizing practical management solutions for autonomic networks. Recent advances in network softwarization and programmability through SDN and NFV, the proliferation of new sources of data, and the availability of lowcost and seemingly infinite storage and compute resource from the cloud are paving the way for the adoption of ML to realize cognitive network management in support of autonomic networking. This article is intended to stimulate thought and foster discussion on how to defeat the bottlenecks that are limiting the wide deployment of autonomic systems, and the role that ML can play in this regard. The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems, either from new components or layering on top of existing components. Further, we propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing key research problems in autonomic computing. The initial DTAC architecture is described along with several problems that it can be used to investigate.