Enhance & Explore: an Adaptive Algorithm to Maximize the Utility of Wireless Networks

The goal of jointly providing efficiency and fairness in wireless networks can be seen as the problem of maximizing a given utility function. In contrast with wired networks, the capacity of wireless networks is typically time-varying and not known explicitly. Hence, as the capacity region is impossible to know or measure exactly, existing scheduling schemes either under-estimate it and are too conservative, or they over-estimate it and suffer from congestion collapse. We propose a new adaptive algorithm, called Enhance & Explore (E&E). It maximizes the utility of the network without requiring any explicit characterization of the capacity region. E&E works above the MAC layer and it does not demand any modification to the existing networking stack. We first evaluate our algorithm theoretically and we prove that it converges to a state of optimal utility. We then evaluate the performance of the algorithm in a WLAN setting, using both simulations and real measurements on a testbed composed of IEEE 802.11 wireless routers. Finally, we investigate a wireless mesh network setting and we find that, when coupled with an efficient mechanism for congestion control, the E&E algorithm greatly increases the utility achieved by multi-hop networks as well.

A Measurement-Based Algorithm to Maximize the Utility of Wireless Networks

Adel Aziz, Julien Pierre Sacha Herzen, Ruben Merz, Patrick Thiran

The goal of jointly providing fairness and efficiency in wireless networks can be seen as the problem of maximizing a given utility function. The main difficulty when solving this problem is that the capacity region of wireless networks is typically unknown and time-varying, which prevents the usage of traditional optimization tools. As a result, scheduling and congestion control algorithms are either too conservative because they under-estimate the capacity region, or suffer from congestion collapse because they over-estimate it. We propose a new adaptive congestion control algorithm, called Enhance & Explore (E&E). It maximizes the utility of the network without requiring any explicit characterization of the capacity region. E&E works above the MAC layer and is decoupled from the underlying scheduling mechanism. It provably converges to a state of optimal utility. We evaluate the performance of the algorithm in a WLAN setting, using both simulations and measurements on a real testbed composed of IEEE 802.11 wireless routers.

2011

Rate Performance Objectives of Multi-hop Wireless Networks

Jean-Yves Le Boudec

We consider the question of what performance metric to maximize when designing adhoc wireless network protocols such as routing or MAC. We focus on maximizing rates under battery lifetime and power constraints. Commonly used metrics are total capacity (in the case of cellular networks) and transport capacity (in the case of adhoc networks). However, it is known in traditional wired networking that maximizing total capacity conflicts with fairness, and this is why fairness oriented rate allocations, such as max-min fairness, are often used. We review this issue for wireless ad-hoc networks. Indeed, the mathematical model for wireless networks has a specificity that makes some of the findings different. It has been reported in the literature on Ultra Wide Band that gross unfairness occurs when maximizing total capacity or transport capacity, and we confirm by a theoretical analysis that this is a fundamental shortcoming of such metrics in wireless ad-hoc networks, as it is for wired networks. The story is different for max-min fairness. Although it is perfectly viable for a wired network, it is much less so in our setting. We show that, in the limit of long battery lifetime, the max-min allocation of rates always leads to strictly equal rates, regardless of the MAC layer, network topology, choice of routes and power constraints. This is due to the ``solidarity

2004

Rate Performance Objectives of Multi-hop Wireless Networks

Jean-Yves Le Boudec

We consider the question of what performance metric to maximize when designing ad-hoc wireless network protocols such as routing or MAC. We focus on maximizing rates under battery lifetime and power constraints. Commonly used metrics are total capacity (in case of cellular networks) and transport capacity (in case of ad-hoc networks). However, it is known in traditional wired networking that maximizing total capacity conflicts with fairness, and this is why fairness oriented rate allocations, such as max-min fairness, are often used. We revisit this issue for wireless ad-hoc networks. Indeed, the mathematical model for wireless networks has a specificity that makes some of the findings different. On one hand, it has been reported in the literature on Ultra Wide Band that gross unfairness occurs when maximizing total capacity or transport capacity, and we confirm by a theoretical analysis that this is a fundamental shortcoming of such metrics in wireless ad-hoc networks, as it is for wired networks. In contrast, the story is different for max-min fairness. While it is perfectly viable for wired network, it is much less so in our setting. We show that, in the limit of long battery lifetime, the max-min allocation of rates always leads to strictly equal rates, regardless of MAC layer, network topology, choice of routes and power constraints. This is due to the ``solidarity" property of the set of feasible rates. This results in all flows receiving the rate of the worst flow, and leads to severe inefficiency. We show numerically that the problem persists when battery lifetime constraints are finite. This generalizes the observation reported in the literature that, in heterogeneous settings, 802.11 allocates the worst rate to all stations, and shows that this is inherent to any protocol that implements max-min fairness. Proportional fairness is an alternative to max-min fairness that approximates rate allocation performed by TCP in the Internet. We show by numerical simulations that proportional fairness of rates or transport rates is robust and achieves a good trade-off between efficiency and fairness, unlike total rate or maximum fairness. We thus recommend that metrics for the rate performance of mobile ad-hoc networking protocols be based on proportional fairness.

2003

Rate Performance Objectives of Multi-hop Wireless Networks

Jean-Yves Le Boudec

2003