CONCERT: A Context-Adaptive Content Ecosystem Under Uncertainty

The CONCERT objective is to develop a content ecosystem, encompassing all relevant players, which will be able to perform intelligent content and network adaptation in highly dynamic conditions under uncertainty. This ecosystem will have as basis emerging information-/content-centric networking technologies which support intrinsic in-network content manipulation. The project will consider uncertainty aspects in the following two application domains: a) social media networks based on user generated content and b) CDN-like professional content distribution. Three dimensions of uncertainties will be addressed: heterogeneous and changing service requirements by end users, threats that may have adverse impacts on the content ecosystem, as well as opportunities that can be exploited by specific players in order to have their costs reduced.

In order to manage and exploit these uncertainty aspects, CONCERT defines a two-dimensional content and network adaptation framework that operates both cross-layer and cross-player. First, the decision on any single adaptation action needs to take into account context information from both the content application layer and the underlying network. Second, we consider joint content and network adaptation in order to simultaneously achieve optimised service performance and network resource utilisation. Finally, some complex uncertainty scenarios require coordinated content and network adaptation across different ecosystem players. In this case, inconsistent or even conflicting adaptation objectives and different levels of context knowledge need to be reconciled and are key research issues.

In order to achieve adaptation solutions capable of coping with these different uncertainties, the project will develop advanced learning, decision-making and negotiation techniques. Learning is required for deriving accurate system behavioural patterns according to the acquired context knowledge. This will then drive decision-making functions for taking the most appropriate adaptation actions to address these uncertainties. Negotiation techniques are required for resolving potential tussles between specific content/network adaptation objectives by different players in the content ecosystem. The project will consider both centralised and distributed approaches in which learning and decision-making processes on adaptation actions can be performed either at the central adaptation domain controller or in a decentralised manner across multiple network elements. In the latter case, emerging information-/content-centric networks will become much more intelligent, with content-aware devices performing self-adaptation according to their own context knowledge but through coordination in order to achieve global near-optimality and stability.

CONCERT addresses the next generation of intelligent and adaptive information-/content-centric networks. We envisage empowering it with cross-layer and cross-domain adaptation capabilities so that they can deal with uncertainties, anticipate potential problems, maximise user QoE and optimise resource usage while reducing operational costs. The envisaged content ecosystem will also provide the opportunity to both prosumers and network operators to play an active role in the future content market. The project is expected to lead to significant progress beyond the current state-of-the-art in a number of scientific areas as presented next.

a) Intelligent information- and content-centric networks. Such emerging networks exhibit limited intelligence apart from native in-network content resolution and content-based anycast routing. We intend to combine these with learning and self-adaptation mechanisms so that they are able to deal with uncertainties while always maintaining optimal resource utilisation and service performance. Both distributed and centralised decision making techniques utilising contextual information will be considered, making these networks truly adaptive and intelligent.

b) Holistic network control for multiple uncertainty dimensions. In current networks, traffic, device, energy, content popularity and other uncertainty aspects are dealt with through separate approaches/techniques, which can hardly co-exist in an optimal manner. We intend to provide a systematic approach for network control that involves training/learning and sophisticated decision making exploiting user, traffic, and network behavioural patterns. This novel approach cuts across all the players participating in the content ecosystem, maintaining optimal performance and maximising user QoE.

c) Combined cross-domain content adaptation. In current networks, adaptation is performed in the application layer or in the network but in a non-coordinated fashion. Here we envisage a two-dimensional adaptation framework in which intelligent decision-making will choose where to perform adaptation "vertically", in the application layer or in the network, and "horizontally", across network devices or even across different player domains, e.g., at the prosumers' site. In addition, server selection and fetching from router caches will be combined with content adaptation decisions, hence maintaining optimal media streaming quality.

The commercial/market aspects of the proposed content ecosystem will be manifold. The current content market is oriented towards CDNs and is mostly dominated by large US companies. The advent of the proposed information-centric context-adaptive content ecosystem will unlock its potential and redefine associated business models, favouring SMEs or even individual prosumers. The emerging intelligent context-adaptive information-centric network will effectively behave as a very large CDN, enabling small content providers with innovative ideas to enter the market without the large costs incurred by the current CDN providers. The latter will have to reinvent themselves, offering added-value CDN services by exploiting the networks' increased capabilities. Cloud service providers will also benefit, offering new enhanced services.