Cross-database knob tuning has long been recognized as a critical but complex task. Modern database systems expose hundreds of configuration knobs that play key roles in memory management, concurrency control, and query optimization. Proper tuning can significantly improve performance, while improper settings can cause severe degradation. Despite progress with black-box methods like reinforcement learning and bayesian optimization, as well as LLM-based tuning guides, challenges remain, such as modeling knob dependencies, high cold-start trial costs, and weak handling of dynamic workloads. We propose KnobTuneX, a structure-aware, LLM-enhanced framework for automatic database tuning that integrates domain knowledge, historical behaviors, and reasoning capabilities to adapt to diverse workloads. The approach features an offline learning stage to capture knob-performance relationships and build a historical RAG store, and an online inference stage that dynamically recommends knobs based on structured reasoning and historical insights. By explicitly modeling dependencies among knobs and leveraging LLMs for informed decision-making, the framework achieves both interpretability and adaptability. Finally, we evaluate KnobTuneX on PostgreSQL and show that the method outperforms mainstream approaches in efficiency, scalability, and overall tuning quality across OLTP and OLAP workloads. The implementation of our work can be found at https://github.com/vjwww/KnobTuneX.

Generating high-quality pseudo-labels on the cloud side is crucial for cloud-edge collaborative object detection, especially in dynamic traffic monitoring scenarios where the target data distribution continuously evolves. Existing methods often assume a perfectly reliable cloud model, neglecting the potential for errors in the cloud’s predictions, or employ simple adaptation techniques that struggle to handle complex distribution shifts. This paper proposes a novel Cloud-Adaptive High-Quality Pseudo-label generation algorithm (CA-HQP) that addresses these limitations by incorporating a learnable Visual Prompt Generator (VPG) and a dual feature alignment strategy into the cloud model updating process. The VPG enables parameter-efficient adaptation of the large pre-trained cloud model by injecting task-specific visual prompts into the model’s input, enhancing its flexibility without extensive fine-tuning. To mitigate domain discrepancies, CA-HQP introduces two complementary feature alignment techniques: a global Domain Query Feature Alignment (DQFA) that captures scene-level distribution shifts and a fine-grained Temporal Instance-Aware Feature Embedding Alignment (TIAFA) that addresses instance-level variations. Extensive experiments on the Bellevue traffic dataset, a challenging real-world traffic monitoring dataset, demonstrate that CA-HQP significantly improves the quality of pseudo-labels compared to existing state-of-the-art cloud-edge collaborative object detection methods. This translates to notable performance gains for the edge model, showcasing the effectiveness of CA-HQP in adapting to dynamic environments. Further ablation studies validate the contribution of each individual component (DQFA, TIAFA, VPG) and confirm the synergistic effect of combining global and instance-level feature alignment strategies. The results highlight the importance of adaptive cloud model updates and sophisticated domain adaptation techniques for achieving robust and accurate object detection in continuously evolving scenarios. The proposed CA-HQP algorithm provides a promising solution for enhancing the performance and reliability of cloud-edge collaborative object detection systems in real-world applications.

In recommender systems, the cold-start problem poses a significant challenge, especially as users transition from being new to more engaged. Existing solutions often lack the granularity to accommodate this evolving user engagement, resulting in suboptimal performance for intermediate and older users. We introduce "Smooth Start", a unified model that addresses this oversight through a gating mechanism. This mechanism dynamically adjusts the information flow based on each user’s level of engagement, providing a tailored focus for more personalized recommendations. Experimental results validate the effectiveness of "Smooth Start" across varying levels of engagement, offering a more nuanced and efficient solution to the cold-start problem.

Collaborative cloud–edge Power Internet of Things technology is required to support the development of smart grids, which have become intelligent, green, and regionally autonomous systems. The diversity of electricity customer behaviors and different computational intensities of energy management applications present challenges for task allocation among computing resources that belong to different agents. In this article, we propose a novel trilevel collaborative optimization model to comprehensively consider the relation among various agents, including users, edge nodes (ENs), a cloud center (CC), and a multiedge league (MEL). We first formulate a Stackelberg game between users and ENs modeled as the lower level and middle level. In addition, with the assistance of the CC, we propose a MEL cooperation scheme to analyze the collaborative task allocation problem among multiple edges, which is modeled as the upper level to maximize the social welfare of the multiedge system (MES) without damaging the interests of the various ENs. The proposed trilevel model is equivalent to a bilevel program, solved by the proposed collaborative dynamic task allocation (CDTA) algorithm. Numerical simulations are presented to verify the proposed scheme and the results show that this scheme is effective for task allocation among users, ENs, the cloud, and the MEL in a cloud-assisted MES.

The ever increasing line speed and the continuous growing demands of various functions support(for example QoS, multicast and security) have interact- tively made it harder for forwarding engines to process packets at line speed, and this will increasingly make the forwarding engines call for additional buffers to accommodate the burst transmission and decrease the packet loss rate. In this paper, a high-speed pipeline designed for TCAM-based forwarding engines is presented, and its buffer analysis model is also given, then, the buffer requirement of the forwarding engine is analyzed under two conditions: the forwarding rate is not less than and less than the input rate. Our analysis results and experiments both show that, the proposed forwarding pipeline is of high performance, and just one pipeline can easily deal with the data transfer rate of 30 Gb/s or even higher; the pipelined forwarding engine only need to buffer a several packets, then the loss rate will be an acceptable value or even zero, further increasing the buffer size will have little effect on reducing the loss rate.

Centralized parallel packet switch algorithm and distributed parallel packet switch algorithm are two typical scheduling algorithms for parallel packet switch. This paper analyzes the two algorithms in detail, addresses several key problems in their implementation and finally presents several available methods and suggestions to make the parallel packet switch more practical