Hades: Hierarchical Adaptable Decoding for Efficient and Elastic vRAN
Jincao Zhu, Kobus Van Der Merwe, Xenofon Foukas, Bozidar Radunovic
TL;DR
Hades addresses the high edge compute cost of uplink FEC decoding in vRAN by introducing a hierarchical, two-phase decoding paradigm that splits DU functionality between edge and remote clouds. The early, latency-critical decoding runs at the edge, while the completion decoding can be offloaded to the remote cloud over the middle-haul, guided by an EDF-based scheduling framework and an adaptive offloading policy. The authors implement Hades in srsRAN, demonstrate significant improvements in edge efficiency and elasticity, and show that throughput remains robust (up to about $75\%$ of maximum with MH offload) while keeping latency within target bounds under varied conditions. This work offers a practical path to elastic, cost-efficient 5G/6G vRAN deployments by balancing edge latency with MH bandwidth.
Abstract
In cellular networks, virtualized Radio Access Networks (vRANs) enable replacing traditional specialized hardware at cell sites with software running on commodity servers distributed across edge and remote clouds. However, some vRAN functions (e.g., forward error correction (FEC) decoding) require excessive edge compute resources due to their intensive computational demands and inefficiencies caused by workload fluctuations. This high demand for computational power significantly drives up the costs associated with edge computing, posing a major challenge for deploying 5G/6G vRAN solutions. To address this challenge, we propose Hades, a hierarchical architecture for vRAN that enables the distribution of uplink FEC decoding processing across edge and remote clouds. Hades refactors the vRAN stack and introduces mechanisms that allow controlling and managing the workload over these hierarchical cloud resources. More specifically, Hades splits the traditional non-stop run-to-completion iterative FEC decoding process into latency-critical early decoding iterations, i.e., related to MAC processing and early pre-parsing for content identification, and completion decoding iterations, i.e., decoding tasks with larger decoding delay budgets for final data bits extraction. This partitioning provides Hades the flexibility to utilize the available midhaul (MH) network for offloading the latency tolerant part of decoding to remote cloud instances, while performing time-sensitive decoding at the edge cloud locations for low-delay processing. Hades controls decoding load distribution between the edge and remote clouds, based on the edge decoding capacity and the offload network bandwidth, thus improving the utilization of edge compute.