Intermediate nodes are typically network hardware devices such as routers, gateways, firewalls, or switches.
General-purpose computers also forward packets and perform routing, although they have no specially optimized hardware for the task.
Larger networks have complex topologies that can change rapidly, making the manual construction of routing tables unfeasible.
The neighboring nodes examine this information and compare it to what they already know; anything that represents an improvement on what they already have, they insert in their own table.
When applying link-state algorithms, a graphical map of the network is the fundamental data used for each node.
[2] OLSR is proactive; it uses Hello and Topology Control (TC) messages to discover and disseminate link-state information through the mobile ad hoc network.
Using Hello messages, each node discovers 2-hop neighbor information and elects a set of multipoint relays (MPRs).
In computer networking, the metric is computed by a routing algorithm, and can cover information such as bandwidth, network delay, hop count, path cost, load, maximum transmission unit, reliability, and communication cost.
A local administrator can set up host-specific routes that provide more control over network usage, permits testing, and better overall security.
In some small systems, a single central device decides ahead of time the complete path of every packet.
For example, for web requests one can use minimum latency paths to minimize web page load time, or for bulk data transfers one can choose the least utilized path to balance load across the network and increase throughput.
A popular path selection objective is to reduce the average completion times of traffic flows and the total network bandwidth consumption.
A classic example involves traffic in a road system, in which each driver picks a path that minimizes their travel time.
In particular, Braess's paradox shows that adding a new road can lengthen travel times for all drivers.
First, AS-level paths are selected via the BGP protocol that produces a sequence of ASs through which packets flow.
These routing decisions often correlate with business relationships with these neighboring ASs,[10] which may be unrelated to path quality or latency.
Additionally, a similar routing challenge can be observed in cellular networks, where different packets are destined for various endpoints, and each link exhibits varying spectral efficiency.
In this context, the selection of the optimal path involves considering latency and packet error rate.
To address this, multiple independent entities, one for each base station, play a crucial role in path selection while striving to optimize overall network performance.
It was also suggested that, were an appropriate mechanism in place, ISPs would be willing to cooperate to reduce latency rather than use hot-potato routing.
This has been used by large internet companies that operate many data centers in different geographical locations attached using private optical links, examples of which include Microsoft's Global WAN,[16] Facebook's Express Backbone,[17] and Google's B4.
[18] Global performance metrics to optimize include maximizing network utilization, minimizing traffic flow completion times, maximizing the traffic delivered prior to specific deadlines and reducing the completion times of flows.
[19] Work on the later over private WAN discusses modeling routing as a graph optimization problem by pushing all the queuing to the end-points.
The authors also propose a heuristic to solve the problem efficiently while sacrificing negligible performance.