Multi‐resource workload mapping with minimum cost in cloud environment

Workload mapping in cloud environment refers to map multiple workloads provided by the cloud users/tenants to the substrate network provided by the cloud providers, which is a NP‐hard problem. The workload is a service demand made to the cloud, which is modeled as a logical network consists of virtual nodes and virtual links. Substrate network is a physical network consists of physical nodes that are inter‐connected via communication links. Devising heuristic methods has become the mainstream of workload mapping problem, which can obtain a feasible solution, but the quality of the solution is not guaranteed. Pointing to this issue, this paper takes the mapping cost of the workloads as the solving objective, and models the workload mapping as a constraint optimization problem. Based on the constraint optimization model, we devise two algorithms to solve the problem. These algorithms can not only find the feasible solution, but also ensure the solution is optimal. Lastly, we have demonstrated the optimality of the proposed algorithms through theoretical proof and evaluated the performance of them through simulation experiment.

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