Sketch has gained wide deployment and application for approximate flow estimation, because of its ability to maintain good accuracy and high throughput with limited memory resources. However, most existing sketch approaches ignore the distinctions between flow priorities, though the high-priority flows are relatively scarce but hold significant information. Therefore, a class of priority-aware sketches has appeared recently to provide differentiated measurement accuracy for flows with different priorities. Unfortunately, it is challenging for these priority-aware sketches to strike a good balance between accuracy and throughput. To address this issue, we propose a priority-adaptive architecture PA-Sketch, which utilizes priority-aware hash to dynamically allocate appropriate numbers of hash functions for different flows according to their priorities. Moreover, to further achieve good accuracy in extremely small memory space, we introduce the priority-aware sampling into PA-Sketch. The test results show that PA-Sketch significantly improves accuracy while minimizing the hash overhead. Compared to the state-of-the-art priority-aware sketches, PA-Sketch reduces the ARE of high-priority flows by 86% and improves the F1 score by 1.83 times, meanwhile maintaining slight accuracy loss for low-priority flows.

In recent years, end-to-end congestion control algorithms or flow pausing mechanisms are proposed to achieve high throughput and low latency in datacenter networks. However, prior end-to-end congestion control works without complex signals fail to achieve fast convergence to a stable equilibrium state and effectively handle the transient congestion, while existing flow pausing mechanisms are decoupled from congestion control, which leads to long convergence time after transient states and incomplete queue elimination in equilibrium states. To address these issues, we present FAR, a rate control protocol that combines the advantages of flow pausing and congestion control. At its heart, FAR couples the bandwidth-estimation-based congestion control and the end-to-end flow pausing mechanisms. After flow pausing, FAR quickly explore the available bandwidth with a binary-search probe to achieve high throughput and low latency. Meanwhile, FAR employs a probe staggering mechanism to address the queue oscillation issue in high-concurrency scenarios. We implement the prototype of FAR using DPDK. Extensive evaluation results demonstrate that our protocol achieves accurate bandwidth estimation and reduces the tail flow completion time (FCT) by up to 67% compared with the state-of-the-art designs.

Quantiles over data stream have been recognized to be an essential feature in network traffic. To provide accurate estimation results, current quantile estimation approaches are preconfigured according to traffic distributions such as log-normal and Pareto distributions. In most practical applications, however, the traffic distributions are not known a priori or are highly dynamic, disturbing estimation results. In this paper, we propose SplitSketch, a sketch-based mechanism that aims to accurately estimate quantiles over data stream without any prior knowledge of traffic distributions. SplitSketch adjusts its estimation granularity according to the changing process of the traffic distribution. The granularities with denser distribution will be recorded with the finer granularity to provide more accurate estimation results. Experimental results demonstrate that, compared to existing approaches, SplitSketch reduces the absolute error in quantile estimation by 59.1% for heavy-tailed distributions and 79.2% for general distributions.

Sketch-based measurement approaches have recently become a promising solution for detecting periodic flows. However, current sketch approaches struggle to achieve accurate detection of periodic flows due to their short-sighted record of the flow arrival information. Recording the long-term information of flow arrivals could mitigate this issue, while the large memory consumption will hurt the detection accuracy. Consequently, achieving a satisfactory trade-off between memory efficiency and detection accuracy remains a tough challenge. To address this issue, we propose Aion for periodic flow detection. Specifically, Aion uses the Sidon sequence to compress historical flow arrival information in multiple time windows into very small size. Based on the periodicity information from successive time windows, Aion updates the estimated frequencies of periodic flows and promptly evicts non-periodic ones to enable accurate detection of frequent periodic flows. We implement Aion on a P4-based testbed and demonstrate that it achieves superior resource efficiency compared to state-of-the-art approaches. Trace-driven evaluations show that Aion improves F1-Score by up to 9.88×, particularly under small memory conditions.

Nowadays the data center switches employ the on-chip shared buffer to absorb bursts and avoid packet loss during transient congestion. However, as the buffer-per-port-per-Gbps in production data centers decreases, it becomes more challenging to provide efficient buffer management to meet the requirements of heterogeneous traffic. We observe that typical shared buffer management policies have two steps: first, they identify short flows arriving at ports and then allocate more buffer room for these ports. Unfortunately, the lack of isolation between long and short flows leads to increased queue buildup and even packet loss of short flows. To address this limitation, we propose D2T, which uses different queue length thresholds for long and short flows. Specifically, we first design a compact data structure to distinguish between long and short flows. Then when two kinds of flows coexist at the same port, the threshold of long flows will decrease to absorb the bursty short flows. We implement D2T at a P4- programmable switch and large-scale simulations. The results demonstrate that D2T reduces both average and tail flow completion times (FCT) of short flows by up to 29% and 62% compared with the state-of-the-art policies, respectively.

Multicast serves as an important approach for one-to-many communication in data center networks. To reduce overhead and improve scalability, bloom filters are employed in current multicast approaches to store forwarding ports of switches. However, the well-known false positive issue of bloom filter incurs wrong forwarding behaviors and redundant traffic in multicast tree, degrading transmission efficiency and increasing the risk of data leakage. Inspired by the fact that, given the same false positive ratio, the switch in the upper layers of multicast tree generates more redundant traffic, we propose RSBF, a fine-grained and resource-aware multicast approach using skewed bloom filters. Specifically, RSBF maintains multiple bloom filters corresponding to different layers of multicast tree, and allocates more ample space to the bloom filter of the upper layer switches, thereby reducing the overall redundant traffic. The test results of large-scale simulation demonstrate that RSBF reduces both redundant traffic and header overhead by up to 64% and 49% compared with the state-of-the-art approaches, respectively.

In modern data centers, many flow scheduling schemes are proposed to accelerate data transfer and improve user experience. However, these schemes assume ideally the prior knowledge of the flow size information, which, unfortunately, is hard to obtain without modifying data center applications. The sketch-based approaches measure the flow size at switch with a compact memory structure, high throughput, and acceptable accuracy loss. However, existing sketches commonly focus on large or specific flows, while most flows in data center networks are small, resulting in missing or overestimated size information about small flows. We propose Strainer Sketch, which enables accurate and fast measurement of small flows with small memory and flexible deployment in a variety of scheduling algorithms. Specifically, Strainer Sketch uses the hierarchical structure to mitigate hash collisions between large and small flows, and the probabilistic counting algorithm to mitigate overestimation due to hash collisions between small flows. Furthermore, we propose a packet scheduling algorithm SW-PIFO, which provides the flow discrimination for a huge number of small flows by using a limited number of queues. Through the testbed experiments and simulations of typical data center applications, we show that our scheme reduces the small flow completion time (FCT) by up to 56.7 <?TeX $\%$?> Math 1 compared with flow scheduling using classic sketches.

To achieve high throughput and low latency for data center applications, there are two broad lines of work: end-to-end congestion control algorithms and flow pausing mechanisms. It is challenging for end-to-end congestion control algorithms without complex signals to achieve fast convergence to a stable equilibrium state while effectively handling the transient congestion. Additionally, flow pausing mechanisms are decoupled from congestion control, which leads to long convergence time after transient state and incomplete queue elimination in equilibrium state. We propose a transport protocol that combines the advantages of flow pausing and congestion control, called FAR. The key idea is coupling the bandwidth-estimation based congestion control and the end-to-end flow pausing mechanisms. FAR quickly explores the available bandwidth with binary-search based packet train probe to achieve high throughput and low latency. Extensive evaluation results demonstrate that our protocol achieves accurate bandwidth estimation and reduces the tail flow completion time (FCT) by up to 67 <?TeX $\%$?> Math 1 compared with the state-of-the-art designs.

Finding persistent items provides indispensable information for data stream tasks. However, accurately identifying persistent items becomes very challenging with the increasing volume of data streams in memory-constrained environments. Existing solutions for finding persistent items often rely solely on estimating the persistence of items to make replacement decisions, requiring sufficiently large memory for acceptable performance. However, persistent items are frequently erroneously replaced in scenarios with numerous non-persistent items, leading to suboptimal accuracy. To address this issue, we reveal that periodically arriving items provide another useful feature for finding persistent items. We further propose P2Sketch that selectively replaces non-persistent items and preserves persistent items based on multi-dimensional statistics of estimated persistence and periodicity of items. Specifically, P2Sketch leverages the characteristics of persistence and periodic arrival to replace stored items selectively. When multiple candidates map to the same bucket, we replace items with longer periodic intervals and smaller estimated persistence to ensure more persistent items are protected. Experimental results show that P2Sketch significantly improves the F1 score by 1.15x and reduces the ARE by 2.66x under the condition of 50KB of memory compared with the state-of-the-art solutions.

Finding flows that are persistent but do not occur frequently is very important to intrusion detection and network management. Current solutions detect persistent and infrequent (PI) flows according to the accumulative statistics from all time windows. However, PI flows may generate a huge amount of traffic in very short time period. The bursty traffic significantly disrupts the accumulative statistics, leading to misjudgments of PI flows. To address this issue, we propose a novel design called PIB sketch, which finds PI flows based on the global information from all time windows and local information within each independent time window. By filtering out the bursty traffic, PIB sketch avoids the negative effect on measurement accuracy. We conduct large-scale trace-driven test to evaluate PIB sketch. The test results show that PIB effectively improves the F1 score and ARE compared with the state-of-the-art solutions.

To reduce the communication overhead in distributed sketch system, it is desirable to compress sketches before uploading. However, current sketch compression approaches hardly achieve high speed of compression procedure and low error of compressed sketches at the same time. In this paper, we take a clean slate approach to design a sketch compression scheme called as Fast-Mapping that achieves both fast compression speed and high accuracy. Based on the prior statistics knowledge of bucket data distribution, Fast-Mapping compresses the similar buckets to obtain high accuracy. We also theoretically derive the compression error bound of Fast-Mapping. The experimental results show that, Fast-Mapping achieves higher speed and lower error than the-state-of-art works.

Due to the ability to maintain good accuracy and high throughput with limited memory resources, sketch has gained wide deployment and application for approximate flow estimation. However, most existing sketch approaches ignore the distinctions between flow priorities, though the high-priority flows are relatively scarce but hold significant information. Therefore, a class of priority-aware sketches has appeared recently to provide differentiated measurement accuracy for flows with different priorities. Unfortunately, it is challenging for these priority-aware sketches to strike a good balance between accuracy and throughput. To address this issue, we propose a priority-adaptive architecture PA-Sketch, which utilizes priority-aware hash to dynamically allocate appropriate numbers of hash functions for different flows according to their priorities. For the scenarios we experimented, we observed that PA-Sketch significantly improves accuracy while minimizing the hash overhead. Compared to the state-of-the-art priority-aware sketches, PA-Sketch achieves around 4.83x higher accuracy and 1.83x higher F1 score for high-priority flows on average, meanwhile maintaining slight accuracy loss for low-priority flows.

Theoretical analysis of transfer learning of the deep neural networks (DNN) is crucial in ensuring stability or convergence and gaining a better understanding of the networks for further development. However, most current transfer learning methods are black-box approaches that are more focused on empirical studies. This paper develops a transfer learning algorithm for deep convolutional neural networks (CNN) with batch normalization layers. A convergence-guaranteed transfer learning algorithm is proposed to train the classifier of a deep CNN with pretrained convolutional layers. Two classification case studies based on VGG11 with the MNIST dataset and CIFAR10 dataset are presented to demonstrate the performance of the proposed approach and explore the effect of batch normalization layers on transfer learning,