Graph embedding is an effective yet efficient way to convert graph data into a low dimensional space. In recent years, deep learning has applied on graph embedding and shown outstanding performance. Adjacency matrix is often taken as the storage data structure of graph. However, there are the problems of insufficient spatial proximity information in adjacency matrix.. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial proximity matrix can obtain subspace of the graph which can help convolution neural network easily and quickly extract the spatial localization. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other deep learning methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

Today's trend of building ever larger language models (LLMs), while pushing the performance of natural language processing, adds significant latency to the inference stage. We observe that due to the diminishing returns of adding parameters to LLMs, a smaller model could make the same prediction as a costly LLM for a majority of queries. Based on this observation, we design Tabi, an inference system with a multi-level inference engine that serves queries using small models and optional LLMs for demanding applications. Tabi is optimized for discriminative models (i.e., not generative LLMs) in a serving framework. Tabi uses the calibrated confidence score to decide whether to return the accurate results of small models extremely fast or re-route them to LLMs. For re-routed queries, it uses attention-based word pruning and weighted ensemble techniques to offset the system overhead and accuracy loss. We implement and evaluate Tabi with multiple tasks and models. Our result shows that Tabi achieves 21%-40% average latency reduction (with comparable tail latency) over the state-of-the-art while meeting LLM-grade high accuracy targets.

Various datacenter network (DCN) load balancing schemes have been proposed in the past decade. Unfortunately, most of these solutions designed for lossy DCNs do not work well for Priority Flow Control (PFC) enabled lossless DCNs, primarily due to the reason that the individual congestion signals used in these solutions, e.g., link load, queue length, Round Trip Time (RTT) and Explicit Congestion Notification (ECN), may not be able to correctly or timely reflect the hop-by-hop PFC pausing. This paper first reveals the above problems via extensive experiments, and then based on the insights learned, we present Proteus, a PFC-aware load balancing scheme that is resilient to PFC pausing by exploring a combination of multi-level congestion signals. At its heart, Proteus leverages RTT-level signals (i.e., RTT and link utilization) to detect path status for initial routing decision, and exploits sub-RTT level signal (i.e., cumulative sojourn time) to reflect instantaneous PFC pausing and make timely rerouting choices based on the idea of better-late-than-never. We have implemented Proteus in the hardware programmable switch. Our testbed experiments as well as large-scale simulations show that Proteus can effectively handle PFC pausing under realistic workloads and achieve up to 35%, 31%, 28%, 22% and 46%, 42%, 34%, 29% better average FCT and $99^{th}$ percentile FCT than CONGA, DRILL, Hermes and MP-RDMA, respectively.