SmartFCT: Improving power-efficiency for data center networks with deep reinforcement learning
Reducing the power consumption of Data Center Networks (DCNs) and guaranteeing the
Flow Completion Time (FCT) of applications in DCNs are two major concerns for data center …
Flow Completion Time (FCT) of applications in DCNs are two major concerns for data center …
Towards an energy-efficient Data Center Network based on deep reinforcement learning
Abstract Data Center Network (DCN) plays a crucial role in orchestrating the physical or
virtual resources in data centers to meet the requirements of Internet of Things and Cloud …
virtual resources in data centers to meet the requirements of Internet of Things and Cloud …
AggreFlow: Achieving power efficiency, load balancing, and quality of service in data center networks
Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs
using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and …
using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and …
DiffTREAT: Differentiated traffic scheduling based on RNN in data centers
Transmission schemes in data centers are supposed to accurately distinguish flow types for
different scheduling. However, prior efforts failed to meet the needs at all levels in a cost …
different scheduling. However, prior efforts failed to meet the needs at all levels in a cost …
Distributed flow scheduling in energy-aware data center networks
R Liu, H Gu, X Yu, X Nian - IEEE Communications Letters, 2013 - ieeexplore.ieee.org
Recently, the strategy of powering off unneeded network devices is proposed for energy-
aware data center networks (DCNs). However, efficient flow scheduling is required for …
aware data center networks (DCNs). However, efficient flow scheduling is required for …
Dynamic flow scheduling for power-efficient data center networks
Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs
using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on and off links …
using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on and off links …
Flash: Joint Flow Scheduling and Congestion Control in Data Center Networks
Flow scheduling and congestion control are two important techniques to reduce flow
completion time in data center networks. While existing works largely treat them …
completion time in data center networks. While existing works largely treat them …
Software defined networking-based traffic engineering for data center networks
Today's Data Center Networks (DCNs) contain tens of thousands of hosts with significant
bandwidth requirements as the needs for cloud computing, multimedia contents, and big …
bandwidth requirements as the needs for cloud computing, multimedia contents, and big …
Flow splitter: A deep reinforcement learning-based flow scheduler for hybrid optical-electrical data center network
Hybrid optical-electrical switching based data center network (HOE-DCN) has been
regarded as a promising architecture for the next generation data center network (DCN). To …
regarded as a promising architecture for the next generation data center network (DCN). To …
Rilnet: A reinforcement learning based load balancing approach for datacenter networks
Q Lin, Z Gong, Q Wang, J Li - … , MLN 2018, Paris, France, November 27–29 …, 2019 - Springer
Modern datacenter networks are facing various challenges, eg, highly dynamic workloads,
congestion, topology asymmetry. ECMP, as a traditional load balancing mechanism which is …
congestion, topology asymmetry. ECMP, as a traditional load balancing mechanism which is …