Taxi-cab cloud architecture to offload data traffic from cellular networks

Abstract

The next generation mobile networks (NGMN) are over-taxed by the increasing demand of on-the-go content access. Standards bodies and researchers are building solutions that offload the traffic demands from the NGMN infrastructure to small cells. We motivate a taxicab cloud as a mobile ISP that offloads traffic demands from NGMN to under-utilized licensed bands such as TV whitespaces. This cloud consists of mobile (taxicabs) and fixed cloudlets. Fixed cloudlets are placed around major transit hubs in New York City. Cloudlets communicate with each other using cognitive radio (CR) technology for opportunistic spectrum access in the licensed band. Mobile cloudlets feature a multi-radio design to allow various short-range connection options (Bluetooth, Wi-Fi, mmWave etc.) to user equipment (UE). Cloudlets maintain distributed caches of popular content while UEs use name based content retrieval to access content. We use this scenario as a backdrop to study the taxicab mobility pattern. This is a bottom-up approach to designing suitable network and link layer technologies as well as estimate the benefits i.e., volume of traffic offloaded from the NGMN.