AAMcon: an adaptively distributed SDN controller in data center networks

Today, most user services are based on cloud computing, which leverages data center networks (DCNs) to efficiently route its communications. These networks process high volumes of traffic and require exhaustive failure management. Furthermore, expanding these networks is usually costly due to their constraint designs. In this article, we present enhanced Torii (eTorii), an automatic, scalable, reliable and flexible multipath routing protocol that aims to accomplish the demanding requirements of DCNs. We prove that eTorii is, by definition, applicable to a wide range of DCNs or any other type of hierarchical network and able to route with minimum forwarding table size and capable of rerouting around failed links on-the-fly with almost zero cost. A proof of concept of the eTorii protocol has been implemented using the Ryu SDN controller and the Mininet framework. Its evaluation shows that eTorii balances the load and preserves high-bandwidth utilization. Thus, it optimizes the use of DCN resources in comparison to other approaches, such as Equal-Cost Multi-Path (ECMP).

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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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