Predictable and Resilient Real-Time Edge Computing

5G multi-access edge computing (MEC) promises to enable latency-critical applications by bringing computational power closer to mobile devices. Our measurements on commercial MEC deployments reveal frequent SLO violations due to high tail latencies. We identify resource contention at the RAN and the edge server as the root cause, compounded by SLO-unaware schedulers. Existing SLO-aware MEC schedulers require RAN–edge coordination, making them impractical for deployment and prone to poor performance due to coordination delays, limited heterogeneous application support, and ignoring edge resource contention. This paper introduces SMEC, a practical, SLO-aware resource management framework that facilitates deadline-aware scheduling through fully decoupled operations at the RAN and edge servers. Our key insight is that standard 5G protocols and application behaviors naturally provide information exploitable for SLO-aware management without extensive infrastructure or application changes. Evaluation on our 5G testbed shows that SMEC achieves 90–96% SLO satisfaction versus under 6% for existing approaches, while reducing tail latency by up to 122×.

We present Pendulum, a live video analytics system with a novel network-compute joint scheduling in mobile edge computing (MEC) architecture. In practical scenarios, the resource bottleneck frequently alternates between network (video streaming) and compute (DNN inference) stages due to independent fluctuations in wireless channel and scene content. Prior single-stage scheduling systems suffer from throughput or accuracy fluctuation and resource wastage resulting from over-provisioning. To overcome these limitations, we newly leverage the interplay between video bitrate and DNN complexity to design an end-to-end system. Pendulum is composed of (i) a resource-efficient network-compute demand curve profiler and (ii) a joint resource scheduler. Evaluation with various videos and state-of-the-art DNNs shows that Pendulum achieves up to 0.64 mIoU gain and 1.29× higher throughput than state-of-the-art baselines. Pendulum also achieves near-optimal multi-user resource scheduling performance with minimal search overhead, achieving a 25% cost reduction compared to network-compute decoupled scheduling.

Mobile live video analytics apps require end-to-end latency guarantee for responsiveness and immersiveness. Achieving consistent low latency is challenging due to complex fluctuations of wireless channel and scene complexity; for example, latency SLO satisfaction rate drops to as low as 26% in commercial 5G MEC platforms. Prior works mostly focus on either app-only (bitrate, DNN adaptation, or GPU allocation) or RAN-only (radio resource allocation) scheduling, with mutual ignorance of the other side resulting in mismatched scheduling decisions and frequent SLO violations. Coordinating the two schedulers is also challenging, as they are run separately by network and cloud operators with disjoint control. We present ARMA, an end-to-end live video analytics system with app-RAN mutual-awareness for high end-to-end latency SLO satisfaction in MEC. We design a mutually-aware decoupled scheduling mechanism on top of RAN Intelligent Controller (RIC) in Open-RAN architecture that fosters cooperative interaction between the two operators’ schedulers while preserving operational proprietaries. We prototype an Open RAN-enabled 5G MEC testbed and evaluate ARMA, showing that it achieves 97% SLO satisfaction rate.

The modern 5G fronthaul, connecting base stations to radio units in cellular networks, is designed for microsecond-level performance using Ethernet-based protocols. Due to perceived performance overheads and misconceptions about the risk and impact of attacks, integrity protection is not mandatory in the 5G fronthaul standards. We show how vulnerabilities from the lack of protection can be exploited, making attacks easier and more powerful. We present a novel class of powerful attacks and traditional attacks, both fully launched from software over open packet-based interfaces, to cause degradation or denial-of-service to users over large regions. Our attacks do not require a physical radio presence or signal-based mechanisms, do not crash radios, and are highly severe, impacting multiple cells. We demonstrate the impact in an end-to-end manner on a commercial-grade, multi-cell 5G testbed, showing adversaries can degrade user performance by more than 80%, block select users from the cell permanently, or generate signaling storms of more than 2500 messages per minute with just two compromised cells and four users. We present analysis of countermeasures that meet strict fronthaul performance requirements.

Virtualized radio access networks (vRANs) run RAN processing on commodity servers, replacing proprietary hardware. The DU component has real-time deadlines and a black-box nature, making resilience features like upgrades and failover challenging. These properties prevent use of techniques like VM migration or state replication. Atlas is the first system providing resilience for the DU by repurposing existing wireless resilience mechanisms—handovers and cell reselection—to provide software resilience. For upgrades, we serve cells from both old and new DUs via the same radio, using handovers to migrate user devices. For failures, we identify deficiencies in RAN protocols that disrupt reselection and eliminate them with a middlebox between the DU and higher layers. Evaluation on a 5G vRAN testbed shows Atlas has minimal connectivity disruption during resilience events, with low overhead.

Virtualized radio access networks (vRANs) are replacing specialized RAN hardware with commodity servers. Today’s vRANs lack resilience, as there is no support for failover or upgrades without long interruption. Enabling these features is challenging due to real-time latency and black-box nature. Slingshot is a new system providing transparent resilience for the vRAN physical layer (PHY) using real-time workload migration, fast protocol middleboxes, and real-time failure detection. A key insight is to treat disruptions like regular wireless impairments and use the cellular network’s resilience. Experiments with a 5G vRAN testbed show Slingshot handles PHY failover with no disruption to video conferencing, under 110 ms to TCP, and enables zero-downtime upgrades.