SDN data plane generally utilizes TCAM to accommodate flow tables for fast packet classification, which also leads to serious problem of high energy consumption. Existing techniques are difficult to stably achieve satisfactory energy-saving effect especially under network traffic jitters. To address this problem, this paper first designs an elastic energy-saving cache to always keep sufficient number of active exact flows for stably high cache hit rates even under network traffic jitters. Particularly, we adaptively adjust the number of cache segments, in terms of the relationship between current cache hit rate and its preset expected range, to maintain high cache hit rate. Meanwhile, we regulate the threshold of packet inter-arrival time for identifying active exact flows, in accordance with current cache occupancy rate, to match the number of active exact flows with cache capacity. Furthermore, we theoretically derive cache occupancy rate based on randomly mapping assumption of each active exact flow and cache hit rate based on the assumption of flow activity degree model. Subsequently, we build an elastically energy-saving flow table storage architecture, by applying the elastic energy-saving cache to always enable a majority of incoming packets to bypass energy-hungry TCAM flow table lookups. Finally, we set up an experimental SDN platform to evaluate its performance on real network traffic traces. Experimental results indicate that our built flow table storage architecture steadily achieves high energy saving rates around 82.43% even under network traffic jitters, with the increase of 6.32˜7.55% compared to the state-of-the-art one.

In the paradigm of Software-Defined Networking (SDN), its data plane generally perform packet forwarding based on flow table lookup on TCAM with high energy consumption. Popular energy-saving methods employ caching techniques for most packets to bypass energy-intensive TCAM lookups. However, existing energy-saving caches cannot adapt to network traffic fluctuation with sufficient utilization of cache space due to non-negligible hash conflicts. To overcome this issue, we design a traffic-aware energy-saving cache with optimal utilization for TCAM flow tables in SDN data plane. In particular, we first devise a nearly conflict-free hashing algorithm for the cache called FelisCatus, which provides three candidate locations for each incoming flow by adjacent hopping, and searches for an empty or replaceable entry for each conflicting flow by co-directional kicking. Then, we propose an adaptive adjustment mechanism of flow activity criterion, i.e., packet inter-arrival time threshold, for enabling the cache to consistently accommodate the most active exact flows in network traffic. Furthermore, we build an energy-efficient SDN flow table storage architecture by applying the above cache and exploiting the accessing features of different memories. Finally, we verify the performance of our designed energy-saving cache and flow table storage architecture by experiments with backbone network traffic traces. Experimental results indicate that, our designed energy-saving cache obtains stable and high hit rates around 75% even under network traffic fluctuation, and our proposed flow table storage architecture achieve high energy saving rates around 71%, with the increase of 7.89% compared to state-of-the-art ones.

Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. However, these methods cannot well cope with scene text due to its extreme variance of scales and aspect ratios. In this paper, we present a simple yet effective transformer-based architecture for scene text detection. Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost. Specifically, we first select a few representative features at all scales that are highly relevant to foreground text. Then, we adopt a transformer for modeling the relationship of the sampled features, which effectively divides them into reasonable groups. As each feature group corresponds to a text instance, its bounding box can be easily obtained without any post-processing operation. Using the basic feature pyramid network for feature extraction, our method consistently achieves state-of-the-art results on several popular datasets for scene text detection.