We consider a system consisting of interacting objects. As we let the number of objects increase, we can characterize the limiting behaviour of the system.

# Category: Stochastic Networks

## A Network Decomposition

We consider a decomposition of the following network utility optimization problem

SYS:

## Congestion Control

We argue, in a slightly informal manner, that queueing networks implicitly optimize a utility function subject to constraints on network capacity. We start with the simple example of a closed queueing network and, as we shall discuss, a motivating example is the Transmission Control Protocol which controls the number of packets in transfer on an Internet connection.

## Distributed Random Access Scheduling

We consider a network of wireless routers. The routers that are close together can interfere if they transmit simultaneously. So schedules need to avoid such collisions. We want each each wireless node to achieve a transmission rates that equals its arrival rate. One might want to implement a policy like MaxWeight or simply estimate the vector of arrival rates and accordingly choose the correct transmission rate. However, this is complicated by the fact that the routers do not know the arrival and transmission rates of their neighbors; all they can do is sense if their neighbors are transmitting or not.

## Max-Weight Scheduling

We define a discrete-time queueing network where there are restrictions on which queues can be served simultaneously. We give a policy for serving queues which is stable whenever it is possible to stabilize the queueing network.

## Power-of-k Choices

We consider a model of a large number of single server queues. When a job arrives, we assume it chooses between queues. The job then chooses to join the shortest of these queues. We show that such choice can dramatically reduce queue sizes.