Since the inception of Information Communication Technology, there has been a surge in networking services leading up to an increase in globalization across various spheres. Various state-of-the-art programs have been initiated in a bid to provide quality networking services. Unlike the late 1980’s when delivery of networking services was still monopolized, today there is stiff competition which has resulted in an upsurge in innovativeness. Advanced networking services include VMware, ISP traffic engineering, and Network security among others.

Internet Service Providers (ISP) play the critical role of dispensing internet connectivity. They are often described as the “pipe” through which content passes through. Often Internet Service Providers work in tandem with the actual Content Providers (CPs). Since ISPs are like “pipes” which provide connectivity, CPs provide the actual content which is to be used by the ISP customers (Ash, 2006). Therefore they may work independently but it is more profitable for both parties if they work inter-dependently. One important tool that ensures the inter-dependency is the Traffic Engineering (TE) tool. TE is applied by Internet Service Providers to ensure that their primary infrastructural role of managing connectivity and balancing traffic load is adequately delivered through ensuring systematic routing so as to minimize congestion while ensuring consumers experience high throughput, low packet loss and low latency (Wenjie, et al., 2009). Essentially Traffic Engineering is a technique that is useful in capacity planning within the transportation system in a bid to ensure topmost use of networking resources at a minimum cost.

One constant problem facing traffic engineering is the grasp of control of the traffic matrix. The collaboration between Internet Service Providers and Content Providers helps ensure that the aforementioned aspiration is met. To better understand the problem faced by TE, the Traffic engineering model of calculation is used. In a network represented by graph G= (V, E), where V stands for the set of nodes and E stands for the cluster of directed physical links, flows are transported on an end-to-end paths consisting of some links (Wenjie, et al., 2009). In this case a node, which is a router, host or server, may be referenced as i and j thus the rate of flow is demonstrated thus i, jV (Wenjie, et al., 2009). Routing may also be modeled as W = {wpl}, i.e., wpl = 1 wherein link l is assumed to be on a path p (Wenjie, et al., 2009). In this case the quantity of paths is not limited so W can stand for all the available paths especially those that carry traffic.

Essentially because of the inherent traffic demand experienced by both the Internet Service Providers and the Content Providers, traffic engineering works to change the routing so as to minimize the inherent network congestion. Network operators do this in practice by changing OSPF link weights or by establishing MPLS label-switched paths (Wenjie, et al., 2009). These alterations made to the routing have been seen to ensure that congestion is minimized and the proportion of traffic flow is adequately controlled.

References

Ash, G., R. (2006). Traffic Engineering and QoS Optimization of Integrated Voice and Data Networks. MORGAN KAUFMANN.

Wenjie, J., et al., 2009. Cooperative Content Distribution and Traffic Engineering in an ISP Network. Department of Computer Science and Department of Electrical Engineering; Princeton University. Retrieved from;

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