The problem of optimal dynamic routing of messages in a store-and-forward packet switching network is addressed by a receding-horizon approach. The nodes of the network must make routing decisions on the basis of local information and possibly of some data, received from other nodes and compute their routing strategies by measuring local variables and exchanging a small amount of data with other nodes. These tasks lead to regard the nodes as the cooperating decision makers of a team organization, and call for a computationally distributed algorithm. The well known impossibility of solving team optimal control problems under general conditions suggest two main approximating assumptions: 1) the team optimal control problem is stated in a receding-horizon framework; and 2) each decision maker acting at a node is assigned a given structure in which a finite number of parameters have to be determined in order to minimize the cost function. This makes it possible to approximate the original functional optimization problem by a nonlinear programming one and to compute off line the routing control strategies.
[1]
Y. Ho,et al.
Team decision theory and information structures in optimal control problems--Part II
,
1972
.
[2]
Adrian Segall,et al.
The Modeling of Adaptive Routing in Data-Communication Networks
,
1977,
IEEE Trans. Commun..
[3]
Thomas Parisini,et al.
Team theory and neural approximators for dynamic routing in communication networks
,
1999,
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).
[4]
E. Davison,et al.
A decentralized discrete-time controller for dynamic routing
,
1998
.
[5]
Thomas Parisini,et al.
A receding-horizon regulator for nonlinear systems and a neural approximation
,
1995,
Autom..
[6]
John N. Tsitsiklis,et al.
Parallel and distributed computation
,
1989
.
[7]
H. Witsenhausen.
A Counterexample in Stochastic Optimum Control
,
1968
.
[8]
R. Radner,et al.
Economic theory of teams
,
1972
.