The total flow in the network, i.e., the network throughput, is equal to the number of sources times y, i.e., 35 × 1.3714 = 48, for both CG approaches, which is equal to the maximum total network flow (maximum network throughput) for a node-degree constraint of 2. In fact, the network throughput achieved is maximum for all node-degree constraints for both CG approaches, as shown in Table 1.
Here, the network capacity region is characterized by the maximum network supportable flow arrival rate vectors for which the network is stable (i.e., all queues in network are kept finite).
Note that for a node-degree constraint of 2, the maximum value of total network flow (maximum network throughput) is 48; for a node-degree constraint of 3, it is 72; and so on.
For example, if the capacity of each link is 24, then the maximum possible total network flow, i.e., the maximum network throughput, is 48 for a node-degree constraint of 2. The following constraints introduce fairness among the flows of multiple commodities and ensure that they are maximized equally: f s p ≥ y × demand sd for all s and d and for all p (6).
The load balancing algorithm transfer the inelastic flows to less heavily loaded routes in order to provide maximum network utilization for elastic flows.
We study the problem of achieving maximum network throughput with fairness among the flows at the nodes in a wireless mesh network, given their location and the number of their half-duplex radio interfaces.
The first algorithm is the optimal solution found by transforming the maximum lifetime scheduling problem to the Multicommodity Network Flow problem, and solved by AMPL-CPLEX [38], which is denoted as Optimal in our figures.
Kapakis et al. [21] contribute MDLA, a network-flow based approach to achieving maximum network lifetime using linear programming and constraints.
CLC_DGS_DD provides a flexible framework for adjusting delays among different flows, and thereby achieves as low as order-optimal delays for preferential flows while simultaneously guaranteeing maximum network utility.
We consider the road capacity reliability as a probability that ensures the maximum network capacity is greater than or equal to the total incoming flow to the network by considering the road capacity as random variable.
