Autoscaling is a technology that automatically scales resources for applications without human intervention to ensure runtime Quality of Service (QoS) while reducing costs. However, user-facing cloud applications serve dynamic workloads that often exhibit variability and contain bursts, posing challenges to autoscaling in maintaining QoS within Service-Level Objectives (SLOs). Conservative strategies risk over-provisioning, while aggressive ones may cause SLO violations, making it more challenging to design effective autoscaling. This paper introduces BASE, a burst-adaptive autoscaling framework that leverages a stacked ensemble of machine learning models to mitigate SLO violations and reduce costs for containerized services and applications operating under time-varying workloads. BASE incorporates a novel prediction-based burst detection mechanism that distinguishes between predictable workload spikes and actual uncertain bursts. When bursts are detected, BASE appropriately overestimates them and allocates resources accordingly to address the rapid growth in resource demand. On the other hand, BASE employs reinforcement learning to rectify potential inaccuracies in resource estimation, enabling more precise resource allocation during non-burst periods. Experiments across ten real-world workloads demonstrate BASE's effectiveness, achieving a significant reduction in SLO violations with lower resource costs compared to other prominent methods.

Wearable exoskeleton robots play a crucial role in promoting upper limb function recovery. To enhance human-robot interaction and achieve precise control, continuous prediction of limb joint angles is required. This paper proposes a decoupled network model (VGANet) based on Variable Graph Convolutional Networks (V-GCN) and Temporal External Attention (TEA) for motion prediction in upper limb rehabilitation training. By establishing a mapping relationship between surface electromyography (sEMG) signals and upper limb movements, the model can predict future joint angles based on real-time sEMG signals. Experimental results demonstrate that this method can achieve continuous motion prediction for the shoulder joint and has been successfully applied to the control system of exoskeleton robots, providing an effective solution for the intelligent development of rehabilitation exoskeletons.

Global population aging has led to a sharp increase in patients of upper limb motor dysfunction. Robot assisted virtual training, as a novel solution, can offer safe and precise assistance for upper limb rehabilitation. However, it remains a critical challenge to compensate virtual interaction force and realize joint synergy movement. In this paper, we design an upper limb rehabilitation robot for virtual training (ULRVT II) which is a cable driven exoskeleton with high compatibility controlled by a joint synergy method. Moreover, we establish a rehabilitation platform with a virtual training environment and evaluation system for experimental validation. Tests for the performance of joint synergy and virtual training are carried out to show the effectiveness of our robot.

Due to the lightweight and flexible characteristics, containers have gradually been used for the application deployment and the basic unit for resource allocation in a cloud platform recently. Reinforcement learning (RL), as a classic algorithm, is widely used in virtual machine scheduling scenarios due to its advantages of adaptability and robustness. However, most RL methods have problems in container scheduling, such as untimely scheduling, lack of accuracy in decision-making and poor dynamics that will lead to a higher SLA violation rate. In order to solve the above problems, a predictive RL algorithm A-SARSA is proposed, which combines the ARIMA model and the neural network model. This algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads. Through a large number of experiments, the timeliness and effectiveness of the A-SARSA algorithm for container scheduling are verified, which can reduce the SLA violation rate dramatically while keeping the resource utilization rate at a good level.