Resource allocation with multi-factor node ranking in data center networks

In data center networks, resource allocation refers to mapping a large number of workloads to substrate networks. Existing heuristic mapping algorithms evaluate the resources of the nodes according to one resource factor or a product of resource factors, which will probably lead to an imbalance of the resource allocation. Furthermore, neglecting the hops of the substrate paths in the resource allocation may result in low resource utilization. In this paper, we adopt a top-k dominating model to rank the nodes, aiming at balancing these factors to improve resource allocation. Moreover, we propose a novel mapping algorithm TK-Match, which consists of a node mapping stage and link mapping stage. In the node mapping stage, TK-Match maps the virtual nodes to the substrate nodes in terms of the node ranking and the hops of the substrate paths. In the link mapping stage, TK-Match adopts the k-shortest path algorithm to map the virtual links. Extensive simulation experiments show that TK-Match can greatly increase the long-term average revenue and the acceptance ratio.