Hades: Hierarchical Adaptable Decoding for Efficient and Elastic vRAN

TL;DR

Hades introduces a hierarchical vRAN architecture that efficiently distributes FEC decoding tasks between edge and remote clouds, reducing edge compute load and adapting to workload fluctuations for 5G/6G networks.

Contribution

The paper presents a novel hierarchical decoding architecture that refactors vRAN processing to optimize resource utilization and latency by splitting decoding tasks across cloud tiers.

Findings

Reduces edge compute resource usage for FEC decoding.

Improves workload balancing between edge and remote clouds.

Enhances vRAN efficiency for 5G/6G deployments.

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.