{AWARE}: Automate workload autoscaling with reinforcement learning in production cloud systems
Workload autoscaling is widely used in public and private cloud systems to maintain stable
service performance and save resources. However, it remains challenging to set the optimal …
service performance and save resources. However, it remains challenging to set the optimal …
Computers Can Learn from the Heuristic Designs and Master Internet Congestion Control
In this work, for the first time, we demonstrate that computers can automatically learn from
observing the heuristic efforts of the last four decades, stand on the shoulders of the existing …
observing the heuristic efforts of the last four decades, stand on the shoulders of the existing …
FLASH: Fast model adaptation in ML-centric cloud platforms
The emergence of ML in various cloud system management tasks (eg, workload autoscaling
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …
Spine: An efficient DRL-based congestion control with ultra-low overhead
Previous congestion control (CC) algorithms based on deep reinforcement learning (DRL)
directly adjust flow sending rate to respond to dynamic bandwidth change, resulting in high …
directly adjust flow sending rate to respond to dynamic bandwidth change, resulting in high …
Prioritization-driven congestion control in networks for the internet of medical things: A cross-layer proposal
Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient
quality of service (QoS) to transmit the collected data successfully. However, unsolved …
quality of service (QoS) to transmit the collected data successfully. However, unsolved …
Liteflow: towards high-performance adaptive neural networks for kernel datapath
Adaptive neural networks (NN) have been used to optimize OS kernel datapath functions
because they can achieve superior performance under changing environments. However …
because they can achieve superior performance under changing environments. However …
Astraea: Towards Fair and Efficient Learning-based Congestion Control
Recent years have witnessed a plethora of learning-based solutions for congestion control
(CC) that demonstrate better performance over traditional TCP schemes. However, they fail …
(CC) that demonstrate better performance over traditional TCP schemes. However, they fail …
HINT: Supporting congestion control decisions with P4-driven in-band network telemetry
Years of research on congestion controls have highlighted how end-to-end and in-network
protocols might perform poorly in some contexts. Recent advances in data plane network …
protocols might perform poorly in some contexts. Recent advances in data plane network …
A machine learning-based framework for dynamic selection of congestion control algorithms
Most congestion control algorithms (CCAs) are designed for specific network environments.
As such, there is no known algorithm that achieves uniformly good performance in all …
As such, there is no known algorithm that achieves uniformly good performance in all …
Habitus: Boosting Mobile Immersive Content Delivery through Full-body Pose Tracking and Multipath Networking
Delivering immersive content such as volumetric videos and virtual/mixed reality requires
tremendous network bandwidth. Millimeter Wave (mmWave) radios such as 802.11 ad/ay …
tremendous network bandwidth. Millimeter Wave (mmWave) radios such as 802.11 ad/ay …