Overall Objectives
Research Program
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
XML PDF e-pub
PDF e-Pub

Section: New Results

Resource Allocation Algorithms in Large Distributed Systems

Participants : Christine Fricker, Philippe Robert, Guilherme Thompson.

This is a collaboration with Fabrice Guillemin from Orange Labs which started in February 2014.

Controlling impatience in cellular networks using QoE-aware radio resource allocation

Impatience of users when using a data service has a major impact on the quality of service offered by telecommunication networks, especially in cellular networks with scarce radio resources. Impatience is negative for users, it is due to many factors related to the performance of servers, customer devices, etc., but also to bandwidth sharing in the network.

While impatience can be seen as a negative phenomenon, it can also be used as a lever to discourage customers when the system becomes too much overloaded. This can be achieved in cellular networks by modulating the capacity available to customers being at a certain distance of the antenna. This general idea can be applied in several manners and can be viewed as a network optimization mechanism. In this paper, we reuse the general framework of α-fair scheduler in order to perform this control. This has the advantage of being easy to implement in realistic settings as α-fair schedulers (and especially the Proportional Fair (PF) one) are widely adopted in mobile networks. This also reduces the dimension of our problem as it narrows the optimization problem to the tuning of a single parameter α.

In order to achieve this goal, we first derive a model for reneging probabilities under a general α-fair scheduler. In particular, we consider a heavy load regime and develop a fluid flow analysis of impatience in cellular networks. We notably establish a fixed point formulation for the computation of the reneging probability and introduce a new metric, namely QoE perturbation, expressing how much a particular flow impacts the reneging probability in the system. We then use this QoE perturbation metric to design of a new radio resource management scheme that controls the parameter of the scheduler in order to reduce the global reneging in the system. For instance, recognizing that customers far from the base station degrade the global performance of the system, impatience and α-fair scheduling can be used to discourage those customers and in some sense to perform an implicit admission control in order to optimize the use of radio resources.

Resource Allocation in Large Data Centers

The goal of this study is to investigate the design of allocation algorithms of requests requiring different classes of quality of video streams as well as their performances. The class of algorithms considered may downgrade the quality of some of the transmission to maximize the utilization of the servers.