Chanh Nguyen

Eunil Seo, Chanh Nguyen, Erik Elmroth

Hybrid cloud-edge infrastructures now support latency-critical workloads ranging from autonomous vehicles and surgical robotics to immersive AR/VR. However, they continue to experience crippling long-tail latency spikes whenever bursty request streams exceed the capacity of heterogeneous edge and cloud tiers. To address these long-tail latency issues, we present Latency-Aware, Predictive In-Memory Routing and Proactive Autoscaling (LA-IMR). This control layer integrates a closed-form, utilization-driven latency model with event-driven scheduling, replica autoscaling, and edge-to-cloud offloading to mitigate 99th-percentile (P99) delays. Our analytic model decomposes end-to-end latency into processing, network, and queuing components, expressing inference latency as an affine power-law function of instance utilization. Once calibrated, it produces two complementary functions that drive: (i) millisecond-scale routing decisions for traffic offloading, and (ii) capacity planning that jointly determines replica pool sizes. LA-IMR enacts these decisions through a quality-differentiated, multi-queue scheduler and a custom-metric Kubernetes autoscaler that scales replicas proactively -- before queues build up -- rather than reactively based on lagging CPU metrics. Across representative vision workloads (YOLOv5m and EfficientDet) and bursty arrival traces, LA-IMR reduces P99 latency by up to 20.7 percent compared to traditional latency-only autoscaling, laying a principled foundation for next-generation, tail-tolerant cloud-edge inference services.

Obaidullah Zaland, Chanh Nguyen, Florian T. Pokorny et al.

Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical machine learning (ML) typically requires data to be located on-premises for training, whereas FL leverages numerous user devices to train a shared global model without the need to share private data. Current robotic manipulation tasks are constrained by the individual capabilities and speed of robots due to limited low-latency computing resources. Consequently, the concept of cloud robotics has emerged, allowing robotic applications to harness the flexibility and reliability of computing resources, effectively alleviating their computational demands across the cloud-edge continuum. Undoubtedly, within this distributed computing context, as exemplified in cloud robotic manipulation scenarios, FL offers manifold advantages while also presenting several challenges and opportunities. In this paper, we present fundamental concepts of FL and their connection to cloud robotic manipulation. Additionally, we envision the opportunities and challenges associated with realizing efficient and reliable cloud robotic manipulation at scale through FL, where researchers adopt to design and verify FL models in either centralized or decentralized settings.