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Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization
Traffic optimizations (TO, eg flow scheduling, load balancing) in datacenters are difficult
online decision-making problems. Previously, they are done with heuristics relying on …
online decision-making problems. Previously, they are done with heuristics relying on …
Location-aware and budget-constrained service deployment for composite applications in multi-cloud environment
Enterprise application providers are increasingly moving their workloads to the cloud for
technical and economic benefits. Multi-cloud environment makes it possible to orchestrate …
technical and economic benefits. Multi-cloud environment makes it possible to orchestrate …
Cost-effective web application replication and deployment in multi-cloud environment
Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to
share the workload across multiple cloud service providers to achieve high-quality services …
share the workload across multiple cloud service providers to achieve high-quality services …
Flow scheduling with imprecise knowledge
Most existing data center network (DCN) flow scheduling solutions aim to minimize flow
completion times (FCT). However, these solutions either require precise flow information …
completion times (FCT). However, these solutions either require precise flow information …
Uniform-Cost Multi-Path Routing for Reconfigurable Data Center Networks
Reconfigurable data center networks (RDCNs) are arising as a promising data center
network (DCN) design in the post-Moore's law era. However, the constantly reconfigured …
network (DCN) design in the post-Moore's law era. However, the constantly reconfigured …
A receiver-driven transport protocol with high link utilization using anti-ECN marking in data center networks
Existing reactive or proactive congestion control protocols are hard to simultaneously
achieve ultra-low latency and high link utilization across all workloads ranging from delay …
achieve ultra-low latency and high link utilization across all workloads ranging from delay …
MLTCP: A Distributed Technique to Approximate Centralized Flow Scheduling For Machine Learning
This paper argues that congestion control protocols in machine learning datacenters sit at a
sweet spot between centralized and distributed flow scheduling solutions. We present …
sweet spot between centralized and distributed flow scheduling solutions. We present …
Network monitoring on multi-pipe switches
Programmable switches have been widely used to design network monitoring solutions that
operate in the fast data-plane level, eg, detecting heavy hitters, super-spreaders, computing …
operate in the fast data-plane level, eg, detecting heavy hitters, super-spreaders, computing …
Traffic modeling and optimization in datacenters with graph neural network
J Li, P Sun, Y Hu - Computer Networks, 2020 - Elsevier
Traffic Optimization (TO) is a well-known and established topic in datacenters with the
fundamental goal of operating networks efficiently. Traditional TO heuristics may suffer from …
fundamental goal of operating networks efficiently. Traditional TO heuristics may suffer from …
Load balancing with traffic isolation in data center networks
T Zhang, Q Zhang, Y Lei, S Zou, J Huang… - Future Generation …, 2022 - Elsevier
The topologies of current data center networks are typically multi-rooted trees (eg leaf–
spine) with rich parallel paths between any pair of hosts. Recent progress has demonstrated …
spine) with rich parallel paths between any pair of hosts. Recent progress has demonstrated …