Network function virtualization along with network service chaining and forwarding graphs envision a reduction in the respective cost that end users, service providers, and network operators are experiencing, while providing complete and high quality services. The allocation of these service chains in a pool of available cloud or data center resources is a challenging problem that can affect the overall performance of the offered network services. Furthermore, a number of challenges associated with the hardware capabilities and the available resources of the cloud infrastructure, along with possible collocation constraints between the components of the service chain, can exponentially increase the complexity of resource allocation. This paper examines how to improve the overall allocation performance of deploying service chains in a cloud environment satisfying server affinity, collocation, and latency constraints. The proposed method is inspired by a partitioning game, where the various components of a service chain are split in a set of partitions executed as virtual machines/containers in appropriate servers. We mathematically prove that a Nash equilibrium exists for our partitioning game corresponding to an optimal solution. By implementing the partitioning game as an iterative refinement process, we also experimentally validate that the proposed algorithm converges to the optimal solution.

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Keywords Cloud computing, Game theory, Graph partitioning, NFV, Resource allocation, Service chaining
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Journal IEEE Transactions on Network and Service Management
Leivadeas, A. (Aris), Kesidis, G. (George), Falkner, M. (Matthias), & Lambadaris, I. (2017). A graph partitioning game theoretical approach for the VNF service chaining problem. IEEE Transactions on Network and Service Management, 14(4), 890–903. doi:10.1109/TNSM.2017.2732699