Network Service Embedding with Multiple Resource Dimensions
Angelos Pentelas, George Papathanail, Ioakeim Fotoglou, Panagiotis Papadimitriou · 2020
The wide adoption of cloud computing, along with the advent of Network Function Virtualization (NFV) and its auspicious applications, have generated a new class of combinatorial optimization problems, with network service embedding (NSE) being one of the most prominent. NSE methods aim at improved resource efficiency and increased revenues for cloud resource providers. However, these methods commonly handle resources types with a single dimension (e.g., virtual nodes with only computing demands), thus limiting the scope of the generated solutions.In an attempt to address NSE under a pragmatic scope, we investigate the potential gains of a heuristic algorithm, which takes into account both the CPU and the memory dimension of virtual nodes. To the best of our knowledge, the novelty of our work lies on the fact that the proposed heuristic exploits insights from research on multi-dimensional virtual machine allocation, i.e., the computation and accounting of a suitability metric across multiple resource dimensions. Our simulation results demonstrate that the proposed NSE method outperforms both a mixed integer linear program (MILP) and a similar heuristic, which do not account for multiple resource dimensions.