The rapid expansion of cloud applications has led to unprecedented increases in network traffic volume, diversity, and complexity. As Cloud Service Providers (CSPs) adopt decentralized, geographically distributed data centers, effective traffic management across these environments has become critical. Distributed Rate Limiting (DRL) has emerged as an essential tool to manage the complex traffic dynamics of decentralized networks, yet traditional centralized rate limiting methods fall short, facing limitations in scalability, adaptability to bursty traffic, and efficiency. This paper presents C3PDAR (Cloud Control with Constant Probabilities and Dynamic Adjustment Range), a novel DRL algorithm tailored for decentralized cloud infrastructures. C3PDAR introduces three key innovations: (1) CPS-BPS DualPoint Rate Limiting and Parent-Child Token Bucket mechanisms, which effectively mitigate burst traffic and short-lived connections while improving bandwidth fairness and inter-tenant isolation; (2) A vSwitch-CGW Cascade Rate Limiting architecture, which reduces CPU overhead in CGW clusters and accelerates convergence by 42%–78%; (3) Virtual Extensible Local Area Network (VXLAN) Padding scheme, which embeds rate-limiting information in existing traffic instead of transmitting new data packets, reducing the communication overhead of the C3PDAR algorithm by over 40%. By integrating these advancements, C3PDAR delivers a scalable, robust solution that outperforms traditional DRL approaches in performance, fault tolerance, and resource efficiency. C3PDAR uniquely empowers CSPs to manage complex, high-volume traffic dynamics in decentralized cloud environments, offering both theoretical insights and practical optimizations for next-generation network control.

As cloud networks continue to evolve, network traffic has experienced an exponential increase. The network architecture is progressively adopting a distributed structure to address this challenge. This architecture extensively utilizes technologies like gateway clusters and Equal-Cost Multi-Path (ECMP) routing, enabling traffic from individual tenants to be routed through multiple pathways. As a result, distributed rate limiting (DRL) has emerged as an essential aspect. Nonetheless, the shift from centralized to DRL has encountered obstacles, with the associated algorithms grappling with simplicity, precision, and applicability issues. Consequently, our research seeks to reconceptualize the issue of DRL from a theoretical standpoint to discover a more holistic and efficacious solution.

Edge devices, including, in particular, mobile devices, have been emerging as an increasingly more important platform for deep neural network (DNN) inference. Typically, multiple lightweight DNN models generated using different architectures and/or compression schemes can fit into a device, thus selecting an optimal one is crucial in order to maximize the users’ Quality of Experience (QoE) for edge inference. The existing approaches to device-aware DNN optimization are usually time consuming and not scalable in view of extremely diverse edge devices. More importantly, they focus on optimizing standard performance metrics (e.g., accuracy and latency), which may not translate into improvement of the users’ actual subjective QoE. In this article, we propose a novel automated and user-centric DNN selection engine, called$\mathsf {Aquaman}$, which keeps users into a closed loop and leverages their QoE feedback to guide DNN selection decisions. The core of$\mathsf {Aquaman}$is a neural network-based QoE predictor, which is continuously updated online. Additionally, we use neural bandit learning to balance exploitation and exploration, with a provably efficient QoE performance. Finally, we evaluate$\mathsf {Aquaman}$on a 15-user experimental study as well as synthetic simulations, demonstrating the effectiveness of$\mathsf {Aquaman}$.

Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. Compared to cloud-based inference, running DNN inference directly on edge devices (a.k. a. edge inference) has major advantages, including being free from the network connection requirement, saving bandwidths, and better protecting user privacy [1].

Data centers have become a popular computing platform for various applications, and account for nearly 2% of total US energy consumption. Therefore, it has become important to optimize data center power, and reduce their energy footprint. With newer power- efficient design in data center infrastructure and cooling equipment, active components such as servers and the network consume most of the power with emerging sets of workloads. Most existing work optimizes power in servers and networks independently, and do not address them together in a holistic fashion that can achieve greater power savings. In this paper, we present PopCorns, a cooperative server-network framework for power optimization. We propose power models for switches and servers with low-power modes. We also design job scheduling algorithms that place tasks onto servers in a power-aware manner, such that servers and network switches can take effective advantage of low-power states. Our experimental results show that we are able to achieve more than 20% higher power savings compared to a baseline strategy that performs balanced job allocation across the servers.