As computation shifts from the cloud to the edge to reduce processing latency and network traffic, the resulting Computing Continuum (CC) creates a dynamic environment where meeting strict Quality of Service (QoS) requirements and avoiding service instance overload becomes challenging. Existing methods often prioritize global metrics and overlook per-client QoS, which is crucial for latency-sensitive and reliability-critical applications. We propose QEdgeProxy, a decentralized QoS-aware load balancer that acts as a proxy between IoT devices and service instances in the CC. We formulate the load balancing problem as a Multi-Player Multi-Armed Bandit (MP-MAB) with heterogeneous rewards: Each load balancer autonomously selects service instances to maximize the probability of meeting its clients' QoS requirements by using Kernel Density Estimation (KDE) to estimate QoS success probabilities. Our load-balancing algorithm also incorporates an adaptive exploration mechanism to recover rapidly from performance shifts and non-stationary conditions. We present a Kubernetes-native QEdgeProxy implementation and evaluate it on an emulated CC testbed deployed on a K3s cluster with realistic network conditions and a latency-sensitive edge-AI workload. Results show that QEdgeProxy significantly outperforms proximity-based and reinforcement-learning baselines in per-client QoS satisfaction, while adapting effectively to load surges and changes in instance availability.

翻译：随着计算从云端向边缘转移以降低处理延迟和网络流量，由此形成的计算连续体（CC）创造了一个动态环境，在该环境中满足严格的服务质量（QoS）要求并避免服务实例过载变得极具挑战。现有方法通常优先考虑全局指标而忽视单客户端QoS，这对于延迟敏感和可靠性要求严苛的应用至关重要。我们提出QEdgeProxy——一种去中心化的QoS感知负载均衡器，在物联网设备与CC中的服务实例之间充当代理。我们将负载均衡问题建模为具有异构奖励的多玩家多臂赌博机（MP-MAB）问题：每个负载均衡器通过使用核密度估计（KDE）来评估QoS成功概率，从而自主选择服务实例以最大化满足其客户端QoS要求的概率。我们的负载均衡算法还融合了自适应探索机制，以快速从性能波动和非稳态条件中恢复。我们提出了一个原生支持Kubernetes的QEdgeProxy实现方案，并在部署于K3s集群的仿真CC测试平台上进行评估，该平台具备真实网络条件和延迟敏感的边缘AI工作负载。结果表明，QEdgeProxy在单客户端QoS满意度方面显著优于基于邻近度和强化学习的基线方法，同时能有效适应负载激增和实例可用性变化。