TY - GEN
T1 - Minimum-delay load-balancing through non-parametric regression
AU - Larroca, Federico
AU - Rougier, Jean Louis
PY - 2009/1/1
Y1 - 2009/1/1
N2 - Network convergence and new applications running on endhosts result in increasingly variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, loadbalancing has proved itself an excellent tool to face this uncertainty. Formally, load-balancing is defined in terms of a convex link cost function of its load, where the objective is to minimize the total cost. Typically, the link queueing delay is used as this cost since it measures its congestion. Over-simplistic models are used to calculate it, which have been observed to result in suboptimal resource usage and total delay. In this paper we investigate the possibility of learning the delay function from measurements, thus converging to the actual minimum. A novel regression method is used to make the estimation, restricting the assumptions to the minimum (e.g. delay should increase with load). The framework is relatively simple to implement, and we discuss some possible variants.
AB - Network convergence and new applications running on endhosts result in increasingly variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, loadbalancing has proved itself an excellent tool to face this uncertainty. Formally, load-balancing is defined in terms of a convex link cost function of its load, where the objective is to minimize the total cost. Typically, the link queueing delay is used as this cost since it measures its congestion. Over-simplistic models are used to calculate it, which have been observed to result in suboptimal resource usage and total delay. In this paper we investigate the possibility of learning the delay function from measurements, thus converging to the actual minimum. A novel regression method is used to make the estimation, restricting the assumptions to the minimum (e.g. delay should increase with load). The framework is relatively simple to implement, and we discuss some possible variants.
KW - Convex nonparametric least squares
KW - Next generation internet
KW - Traffic engineering
KW - Wardrop equilibrium
UR - https://www.scopus.com/pages/publications/67650302285
U2 - 10.1007/978-3-642-01399-7_61
DO - 10.1007/978-3-642-01399-7_61
M3 - Conference contribution
AN - SCOPUS:67650302285
SN - 9783642013980
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 782
EP - 794
BT - NETWORKING 2009 - 8th International IFIP-TC 6 Networking Conference, Proceedings
A2 - Fratta, Luigi
A2 - Schulzrinne, Henning
A2 - Takahashi, Yutaka
A2 - Spaniol, Otto
PB - Springer Verlag
T2 - 8th International IFIP-TC 6 Networking Conference, NETWORKING 2009
Y2 - 11 May 2009 through 15 May 2009
ER -