In software defined network (SDN), packet processing is commonly implemented using match-action model, where packets are processed based on matched actions in match action table. Due to the limited FPGA on-board resources, it is an important challenge to achieve large-scale high throughput based on exact matching (EM), while solving hash conflicts and out-of-order problems. To address these issues, this study proposed an FPGA-based EM table that leverages shared rule tables across multiple pipelines to eliminate memory replication and enhance overall throughput. An out-of-order reordering function is used to ensure packet sequencing within the pipelines. Moreover, to handle collisions and increase load factor of hash table, multiple hash table blocks are combined and an auxiliary CAM-based EM table is integrated in each pipeline. To the best of our knowledge, this is the first time that the proposed design considers the recovery of out-of-order operations in multi-channel EM table for high-speed network packets processing application. Furthermore, it is implemented on Xilinx Alveo U250 field programmable gate arrays, which has a million rules and achieves a processing speed of 200 million operations per second, theoretically enabling throughput exceeding 100 Gbps for 64-Byte size packets.

Information-centric networking (ICN) provides low-latency content delivery with in-network caching, but delivery latency depends on cache distance from consumers. To reduce delivery latency, a scheme to cluster domains and retain the main popular content in each cluster with a cache distribution range has been proposed, which enables consumers to retrieve content from neighboring clusters/caches. However, when the distribution of content popularity changes, all content caches may not be distributed adequately in a cluster, so consumers cannot retrieve them from nearby caches. We therefore propose a dynamic clustering scheme to adjust the cache distribution range in accordance with the change in content popularity and evaluate the effectiveness of the proposed scheme through simulation.

Cloud gaming systems allow users to play games that require high-performance computational capability on their mobile devices at any location. However, playing games through cloud gaming systems increases the Round-Trip Time (RTT) due to increased network delay. To simulate a local gaming experience for cloud users, we must minimize RTTs, which include network delays. The speculative video transmission pre-generates and encodes video frames corresponding to all possible user inputs and sends them to the user before the user's input. The speculative video transmission mitigates the network, whereas a simple solution significantly increases the video traffic. This paper proposes tile-wise delta detection for traffic reduction of speculative video transmission. More specifically, the proposed method determines a reference video frame from the generated video frames and divides the reference video frame into multiple tiles. We calculate the similarity between each tile of the reference video frame and other video frames based on a hash function. Based on calculated similarity, we determine redundant tiles and do not transmit them to reduce traffic volume in minimal processing time without implementing a high compression ratio video compression technique. Evaluations using commercial games showed that the proposed method reduced 40-50% in traffic volume when the SSIM index was around 0.98 in certain genres, compared with the speculative video transmission method. Furthermore, to evaluate the feasibility of the proposed method, we investigated the effectiveness of network delay reduction with existing computational capability and the requirements in the future. As a result, we found that the proposed scheme may mitigate network delay by one to two frames, even with existing computational capability under limited conditions.

With the development of drone technology, concerns have arisen about the possibility of drones being equipped with threat payloads for terrorism and other crimes. A drone detection system that can detect drones carrying payloads is needed. A drone's propeller rotation frequency increases with payload weight. Therefore, a method for estimating propeller rotation frequency will effectively detect the presence or absence of a payload and its weight. In this paper, we propose a method for classifying the payload weight of a drone by estimating its propeller rotation frequency from radar images obtained using a millimeter-wave fast-chirp-modulation multiple-input and multiple-output (MIMO) radar. For each drone model, the proposed method requires a pre-prepared reference dataset that establishes the relationships between the payload weight and propeller rotation frequency. Two experimental measurement cases were conducted to investigate the effectiveness of our proposal. In case 1, we assessed four drones (DJI Matrice 600, DJI Phantom 3, DJI Mavic Pro, and DJI Mavic Mini) to determine whether the propeller rotation frequency of any drone could be correctly estimated. In case 2, experiments were conducted on a hovering Phantom 3 drone with several payloads in a stable position for calculating the accuracy of the payload weight classification. The experimental results indicated that the proposed method could estimate the propeller rotation frequency of any drone and classify payloads in a 250g step with high accuracy.