Network function (NF) offloading on SmartNICs has been widely used in modern data centers, offering benefits in host resource saving and programmability. Co-running NFs on the same SmartNICs can cause performance interference due to contention of onboard resources. To meet performance SLAs while ensuring efficient resource management, operators need mechanisms to predict NF performance under such contention. However, existing solutions lack SmartNIC-specific knowledge and exhibit limited traffic awareness, leading to poor accuracy for on-NIC NFs.

Runtime traffic analysis on programmable data-plane requires substantial human effort, and the high speed and complexity of dataplane often make human capacity the efficiency bottleneck. While existing work has proposed LLM-based approaches, they typically rely on offline network logs, failing to address the human capacity limitations in real-time environments. This paper explores the potential of leveraging evolving LLMs to mitigate these human-centric challenges in real physical dataplane. It outlines a novel framework called NetSophon, which features an LLM-based brain for efficient decision-making and an effective arm to manipulate and perceive the physical programmable dataplane. Through interactions among the brain, arm, and dataplane, NetSophon acts as a "super-copilot" for human operators, facilitating real-time dataplane traffic analysis at scale. A case study demonstrates NetSophon’s potential to assist human operators in interacting with dataplane.

BACKGROUND: Hepatic metastases (HM) and primary liver malignant tumor (PLMC) share partially similar pathological foundations, which can make it difficult to differentiate them based on visual imaging findings. This study will explore the value of multi-algorithm radiomics machine learning models that integrate ultrasound imaging and inflammation-immune features in the identification of HM. METHODS: Patients with hepatic malignancies who had undergone ultrasound-guided biopsy and image acquisition were retrospectively included. A total of 104 patients were randomly divided into training and internal validation cohorts at a 6:4 ratio, while other 45 patients were assigned as the external validation cohort. The PyRadiomics package was utilized to extract 107 radiomics original features. The Wilcoxon test was employed to identify high-value features significantly associated with HM. Univariate analysis was employed to identify inflammation-immune risk features related to HM for model development. 12 machine learning algorithms were integrated to combine radiomics features with inflammation-immune features. Model performance was comprehensively evaluated through receiver operating characteristic curves, and Shapley additive explanations (SHAP) method. RESULTS: The Wilcoxon test identified 15 radiomics features significantly associated with HM (P < 0.001), which were subsequently presented to 12 machine learning algorithms to develop 98 models using two or more features. Among these, 68% of the models achieved moderate identify performance [area under the curve (AUC), 0.73-0.82] in the internal validation cohort. The optimal algorithm radiomics model (random forest + gradient boosting machine) was developed using 4 selected features, yielding AUCs of 0.77 in the training cohort and 0.82 in the internal validation cohort, 0.70 in the external validation cohort, respectively. Univariate analysis further identified platelet-to-lymphocyte ratio (PLR) and platelet-to-albumin ratio (PAR) as high-risk inflammation-immune features for HM. While integrating radiomic features with PLR/PAR enhanced identification performance in the training cohort (AUC = 0.84), this improvement was not significantly replicated in the internal validation cohort (AUC = 0.82), but there was an improvement in the external validation cohort (AUC = 0.74). SHAP analysis revealed that PAR contributed most to the integrated model's predictions, followed by the aforementioned 4 radiomic features. CONCLUSION: This study revealed the potential clinical utility of radiomics features and inflammation-immune features in HM identification. CLINICAL TRIAL NUMBER: Not applicable.

The gap between the stagnation of CPU power and the increase in network bandwidth has promoted a shift towards placing more computation on network hardware [16, 17]. Therefore, SmartNICs have become prevalent in data centers to serve various cloud applications, from network functions [15, 17, 22] to high-level applications like distributed applications and storage [14, 16, 18--21, 23].