Abstract
The data center has become the infrastructure of most Internet services, and its network carries different types of business flow, such as query, data backup, control information, etc. At the same time, the throughput-sensitive large flows occupy a lot of bandwidth, resulting in the small flow’s longer completion time, finally affecting the performance of the applications. Recent proposals consider only dynamically adjusting the ECN threshold or reversing the ECN packet priority. This paper combines these two improvements and presents the HDCQ method for coordinating data center queuing, separating large and small flows, and scheduling in order to ensure flow completion time. It uses the ECN mechanism to design load-adaptive marking threshold update algorithms for small flows to prevent micro-bursts from occurring. At the same time, packets marked with ECN or ACK are raised in priority, prompting these packets to be fed back to the sender as soon as possible, effectively reducing the TCP control loop delay. Extensive experimental analysis on the network simulator (NS-2) shows that the HDCQ algorithm has better performance in the face of micro-burst traffic, reducing the average flow completion time by up to 24% compared with the PIAS.
Highlights
With the rapid development of network applications, high-performance data centers have been established worldwide to carry most of the internet traffic [1]
We evaluated PIAS, Data Center TCP(DCTCP) [12], Low Latency Data Center Transport (LLDCT) [13], and Harmony Data Center Queue (HDCQ) algorithms under various web searching and data mining workloads
In order to effectively deal with data center network congestion, the HDCQ algorithm switches on the switch Explicit Congestion Notification (ECN) marking threshold and notifies the sender to reduce the sending rate in time by adjusting the packet priority [9] (Packet Classifier model)
Summary
With the rapid development of network applications, high-performance data centers have been established worldwide to carry most of the internet traffic [1]. In order to prevent network microbursts, the HDCQ algorithm dynamically adjusts the threshold based on the number of packets marked by the ECN in the queue to avoid network congestion from occurring and to maintain network robustness. Compared with other flow scheduling and ECN-based congestion control schemes, the HDCQ algorithm can more effectively reduce the completion time of short flows and maintain the throughput of long flows. Dynamically adjusting the small-flow ECN marking threshold in the switch prevents network microbursts, avoids network congestion, and increases the robustness of the data center. The HDCQ algorithm speeds up the TCP control loops by setting the ACK and ECN packets to the highest priority, reducing short flows’ completion time, especially the tail flow completion time, and maintaining the throughput of large flows. It outperforms other algorithms under both medium and large flows
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