Cross-layer design and performance analysis for maximizing the network utilization of wireless mesh networks in cloud computing

In recent years, building the cloud based on wireless mesh networks as well as wired networks is rapidly increased for processing the big data. However, existing scheduling and routing protocols cannot support processing the big data efficiently in the cloud, because the each flow path is determined before the data are transmitted based on some routing strategies in the wireless mesh networks. Currently, an important factor that should be considered is that the link capacity between mesh routers can be changed based on the current interference of the flow of other mesh routers. In general, network availability is also influenced by the interference related to the other layers in the protocol stack. In this paper, we study wireless mesh networks and propose JRS-S and JRS-M algorithms, which utilize both route discovery and resource allocation at the same time, in order to maximize capacity of the wireless mesh network in the cloud computing. Our algorithms for each flow use a cross-layer design method based on numerical modeling in order to adaptively control data scheduling at the link layer and find a high data rate path with minimum interference at the network layer. We analyze the optimal capacity of the wireless mesh networks for maximizing network utilization using a numerical solution tool. Through analysis, we also verify that our algorithms can improve system capacity by efficiently distributing a gateway load and that it can enhance the system availability.