Deployment of Service Chains by Exploiting Correlations between Network Functions

What is it about?

In this paper, we study the service chain deployment by exploiting two types of correlations between network functions: the Coordination Effect due to information exchanges among multiple VMs running the same network function, and the Traffic-Change Effect where the volume of outgoing traffic is not necessarily equal to the volume of its incoming traffic at each network function because of packet manipulations such as compression and encryption.

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Photo by Ildefonso Polo on Unsplash

Photo by Ildefonso Polo on Unsplash

Why is it important?

These two effects have not been studied simultaneously in the context of service chaining. With the objective to maximize the profit measured by the admitted traffic minus the implementation cost, we first formulate a joint service-function deployment and traffic scheduling (SUPER) problem that is proved to be NP-hard. We then devise an approximation algorithm based on the Markov approximation technique and analyze its theoretical bound on the convergence time. Simulation results show that the proposed algorithm outperforms two existing benchmark algorithms significantly.

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