Time-Sensitive Networking (TSN) is the most promising network infrastructure for various time-critical applications in Industry 4.0. However, industry applications generate a mix of time-triggered (TT) and event-triggered (ET) flows. Scheduling such mix-flows is a key challenge for TSN. Though current TSN scheduling mechanisms commonly provide deterministic transmission for TT flows with stringent latency requirements, they cannot flexibly accommodate ET flows, which are usually generated by emergency events. In this paper, we propose Elastic Backoff (EBO), a systematic solution for scheduling mix-flows in an elastic way. Our key insight is that the network resources should be reasonably allocated for ET flows while minimally impacting TT flows. To this end, we incorporate the elasticity into the TSN scheduling to make resource reservations for ET flows without hurting TT flows. We conduct extensive experiments on both testbeds and simulations. The evaluation results show that, compared with the state-of-the-art designs, EBO improves the schedulability of ET flows by up to 7.5×, while still ensuring the deterministic transmission of TT flows.

For the widely deployed on-chip shared buffer, efficient buffer management is the key to absorbing bursts and avoiding 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. What’s more, we introduce D2T${}^{*}$which combines D2T with advanced DRL techniques to move toward mastering buffer management for further improving performance across various scenarios. 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.

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.

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.