A Computation Workload Characteristic Study of C-RAN

Driven by the surging demand of mobile applications and IoT devices, the amount of global mobile data traffic is estimated to increase sevenfold in the next few years and reach 69 exabytes per month by 2022. This rapid growing rate force telecom operator to adapt new wireless network technologies in order to deliver desired network performance and quality while reduce network deployment and operating costs. One of the approaches that has gained more traction recently is C-RAN, which aims to renovate the infrastructure of radio access network based on cloud technology. In this work, we built a cloudified LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI), an open-source software radio solution, with the OpenStack, and open-source cloud infrastructure solution. Using the testbed, we conducted workload study to understand the computation resource demand of C-RAN software, and proposed a function splitting technique to improve the resource utilization of C-RAN cloud platform.