Distributed learning algorithms orchestration for mobile networks resource management – NETLEARN

The NETLEARN project intends to tackle these two technical bottlenecks by considering distributed learning algorithms with the aim of designing scalable, dynamic and energy efficient solutions. There are however several scientific bottlenecks in this field:

1) Several such algorithms exist in the literature, each one with its own characteristics in terms of reached equilibrium, convergence speed, stability, etc. Each one can also be tuned by one or several parameters.

2) Environments we are considering are characterized by stochastic variations (due to variations of the traffic demand, of the radio channels, due to mobility), non-stationary situations (sudden changes/discontinuities of the system).

The NETLEARN project intends to adopt a novel approach to address these issues: First building a portfolio of distributed learning algorithms; Second, proposing an orchestration of this portfolio based on a ‘learn to learn’ approach. Our goal will thus be to devise adaptive learning schemes that select dynamically between different learning schemes so that their long-term learning power exceed the regret of any individual algorithm. Based on this framework, we will propose distributed, scalable, dynamic and energy efficient learning algorithms for managing interference in a RAN, contents and cache servers in a mobile CDN. We will propose possibly architecture and protocols enhancements to existing mobile networks in order to allow the implementation of above proposed algorithms. We will demonstrate the effectiveness of the approach through extensive simulations and a demonstrator (testbed).

-

-

-

The main objective of the project is to propose a novel approach of distributed, scalable, dynamic and energy efficient algorithms for managing resources in a mobile network. This new approach relies on the design of an orchestration mechanism of a portfolio of algorithms. The ultimate goal of the proposed mechanism is to enhance the user experience, while at the same time to better utilize the operator resources. User mobility and new services are key elements to take into account if the operator wants to improve the user quality of experience. Future autonomous network management and control algorithms will thus have to deal with a real-time dynamicity due to user mobility and to traffic variations resulting from various usages. To achieve this goal, we focus on two central aspects of mobile networks and intend to design distributed learning mechanisms in non-stationary environments, as well as an orchestration mechanism that applies the best algorithms depending on the situation.
The first main aspect to be addressed is the management of radio resources at the RAN (Radio Access Network) level. In current (LTE) and future (LTE-A) cellular systems, interference appears as a bottleneck for providing high data rates and seamless connectivity to the end-user. To reduce interference it is possible either to coordinate the transmissions of neighboring base stations (BS) so as to avoid simultaneous transmissions on the same radio resources or to allow BSs to cooperate: two or more BSs combine their transmissions towards a single user in order to increase its data rate. Both cases require distributed learning algorithms.
The second aspect is the management of the popular contents users want to get access to. In a Content Delivery Network (CDN), popular content is disseminated and stored in cache servers as close as possible to the demand to avoid delay in access. How to place servers in the network and replicated contents in the servers are traditional issues in CDNs. In mobile CDNs, things are exacerbated because of the changing and unpredictable environment characterized by spatial and temporal changes in the traffic demand, user mobility and variable channel conditions.
The way the project intends to tackle these problems is based on a “learn to learn approach”. If we think about BSs and cache servers as autonomous entities seeking to optimize a global objective function and able to take decisions based on incomplete information, the notion of distributed learning arises naturally. There are numerous approaches along these lines and each mechanism has its own characteristics in terms of needed information, type of achieved equilibrium, convergence speed, and stability. Each mechanism can also be tuned thanks via an array of parameters.
The problem is exacerbated in non-stationary situations due to mobility, traffic demand or radio channel variations. The originality of the project thus lies in its objective of building a portfolio of distributed learning algorithms that are then to be orchestrated. To account for learning in the presence of non-stationary processes, we intend to use the theory of stochastic approximation in order to develop robust versions of existing learning schemes. Orchestrating a portfolio of learning algorithms is, in many regards, similar to the literature on “learning with expert advice”, so our goal will be to devise adaptive learning schemes that select dynamically between different learning schemes so that their long-term learning power exceeds the regret of any individual “expert”. Bringing together experts form both network and learning, NETLEARN ultimately intends to propose architecture and protocol adaptations for implementing our resource management algorithms.