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The wide adoption of the emerging SmartNIC technology creates new opportunities to offload application-level computation into the networking layer, which frees the burden of host CPUs, leading to performance improvement. Shuffle, the all-to-all data exchange process, is a critical building block for network communication in distributed data-intensive applications and can potentially benefit from SmartNICs. In this paper, we develop SmartShuffle, which accelerates the data-intensive application's shuffle process by offloading various computation tasks into the SmartNIC devices. SmartShuffle supports offloading both low-level network functions, including data partitioning and network transport, and high-level computation tasks, including filtering, aggregation, and sorting. SmartShuffle adopts a coordinated offload architecture to make sender-side and receiver-side SmartNICs jointly contribute to the benefits of shuffle computation offload. SmartShuffle carefully manages the tight and time-varying computation and memory constraints on the device. We propose a liquid offloading approach, which dynamically migrates operators between the host CPU and the SmartNIC at runtime such that resources in both devices are fully utilized. We prototype SmartShuffle on the Stingray SoC SmartNICs and plug it into Spark. Our evaluation shows that SmartShuffle improves host CPU efficiency and I/O efficiency with lower job completion time. SmartShuffle outperforms Spark, and Spark RDMA by up to 40% on TPC-H.

Navigating the performance and efficiency trade-offs is critical for serverless platforms, where the providers ideally want to give the illusion of warm function startups while maintaining low resource costs. Limited controls, provided via toggling sandboxes between warm and cold states and keepalives, force operators to sacrifice significant resources to achieve good performance.

Aiming at the large amount of power user data and the fact that the collaborative filtering recommendation technology fails to consider the relationship between users and customer service staff, a k-means clustering and user portrait recommendation method is proposed. This method firstly uses clustering technology clustering the power users’ portrait label vectors to gather similar users together, and makes recommendations based on the cluster to which the users belong. Secondly, through calculating the user portrait label vector similarity between the attributes of the service feature vector to establish the connection between the user and the customer service, and improve the traditional grading forecast method, the user and the personnel of the service of similarity index is integrated into it. Finally, the personnel of the service will be recommended from the two aspects of service quality and service fits.