Sheriff: A Regional Pre-alert Management Scheme in Data Center Networks

Effective TCP Flow Management Based on Hierarchical Feedback Learning in Complex Data Center Network

Sensors ◽

10.3390/s22020611 ◽

2022 ◽

Vol 22 (2) ◽

pp. 611

Author(s):

Kimihiro Mizutani

Keyword(s):

Reinforcement Learning ◽

Data Center ◽

Transmission Control Protocol ◽

Complex Structure ◽

Effective Bandwidth ◽

Complex Data ◽

Data Center Network ◽

Data Center Networks ◽

Flow Management ◽

Management Scheme

Many studies focusing on improving Transmission Control Protocol (TCP) flow control realize a more effective use of bandwidth in data center networks. They are excellent ways to more effectively use the bandwidth between clients and back-end servers. However, these schemes cannot achieve the total optimization of bandwidth use for data center networks as they do not take into account the path design of TCP flows against a hierarchical complex structure of data center networks. To address this issue, this paper proposes a TCP flow management scheme specified a hierarchical complex data center network for effective bandwidth use. The proposed scheme dynamically controls the paths of TCP flows by reinforcement learning based on a hierarchical feedback model, which obtains an optimal TCP flow establishment policy even if both the network topology and link states are more complicated. In evaluation, the proposed scheme achieved more effective bandwidth use and reduced the probability of TCP incast up to 30% than the conventional TCP flow management schemes: Variant Load Balancing (VLB), Equal Cost Multi Path (ECMP), and Intelligent Forwarding Strategy Based on Reinforcement Learning (IFS-RL) in the complex data center network.

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Pache: A Packet Management Scheme of Cache in Data Center Networks

IEEE Transactions on Parallel and Distributed Systems ◽

10.1109/tpds.2019.2931905 ◽

2020 ◽

Vol 31 (2) ◽

pp. 253-265

Author(s):

Tao Chen ◽

Xiaofeng Gao ◽

Tao Liao ◽

Guihai Chen

Keyword(s):

Data Center ◽

Data Center Networks ◽

Management Scheme

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Analysis on Buffer Occupancy of Quantized Congestion Notification in Data Center Networks

IEICE Transactions on Communications ◽

10.1587/transcom.2016ebp3052 ◽

2016 ◽

Vol E99.B (11) ◽

pp. 2361-2372 ◽

Cited By ~ 1

Author(s):

Chang RUAN ◽

Jianxin WANG ◽

Jiawei HUANG ◽

Wanchun JIANG

Keyword(s):

Data Center ◽

Data Center Networks ◽

Buffer Occupancy

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HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00248-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Jiawei Huang ◽

Shiqi Wang ◽

Shuping Li ◽

Shaojun Zou ◽

Jinbin Hu ◽

...

Keyword(s):

Data Center ◽

Large Scale ◽

Network Performance ◽

Rooted Tree ◽

Heterogeneous Traffic ◽

Data Center Networks ◽

Packet Reordering ◽

Traffic Characteristics ◽

Network Utilization ◽

The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

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