RT-OPEX: Flexible Scheduling for Cloud-RAN Processing

It is cost-effective to process wireless frames on general-purpose processors (GPPs) in place of dedicated hardware. Wireless operators are decoupling signal processing from basestations and implementing it in a cloud of compute resources, also known as a cloud-RAN (C-RAN). A C-RAN must meet the deadlines of processing wireless frames; for example, 3ms to transport, decode and respond to an LTE uplink frame. The design of baseband processing on these platforms is thus a major challenge for which various processing and real-time scheduling techniques have been proposed. In this paper, we implement a medium-scale C-RAN-type platform and conduct an in-depth analysis of its real-time performance. We find that the commonly used (e.g., partitioned) scheduling techniques for wireless frame processing are inefficient as they either over-provision resources or suffer from deadline misses. This inefficiency stems from the large variations in processing times due to fluctuations in wireless traffic. We present a new framework called RT-OPEX, that leverages these variations and proposes a flexible approach for scheduling. RT-OPEX dynamically migrates parallelizable tasks to idle compute resources at runtime, reducing processing times and hence deadline misses at no additional cost. We implement and evaluate RT-OPEX on a commodity GPP platform using realistic cellular workload traces. Our results show that RT-OPEX achieves an order-of-magnitude improvement over existing C-RAN schedulers in meeting frame processing deadlines.