Multi-cloud deployed multi-agent systems powered by Large Language Models (LLMs) introduce critical security challenges. While Intel Trust Domain Extensions (TDX)-based Confidential Virtual Machines (CVMs) provide strong isolation and boot-time attestation, they lack dynamic runtime integrity verification, a capability essential for trusted agent systems that frequently load new models and coordinate across distributed services. We present TrustWeave, a runtime integrity measurement and attestation framework that extends the Linux Integrity Measurement Architecture (IMA) with support for Intel TDX's Runtime Measurement Registers (RTMRs). TrustWeave enables userspace attestation of dynamically loaded agent components throughout their life-cycle, providing runtime trust guarantees for secure and scalable LLM agent deployments.

Serverless computing offers resource efficiency, cost efficiency, and a "pay-as-you-go" pricing model, which makes it highly attractive to both users and cloud providers. However, serverless computing faces serious cold start problem, especially for deep neural network (DNN) inference, which requires low latency. Existing cold start optimization focuses only on quick container start and fast runtime and library loading. However, DNN application bootstrap (DNN framework load and start, model initialization, model download, deserialization and copy) is the leading factor during the overall cold start time. As the model size grows, the application-level bootstrap becomes more severe.

Training a deep learning model typically involves two interdependent stages: preprocessing input data on the CPU and training the model on the GPU. This process begins with the CPU performing resource-intensive I/O operations to load raw data, which it then processes and supplies to the GPU. This creates a typical provider-consumer relationship between the CPU and GPU. However, the inefficiency of I/O and CPU operations, in contrast to the high capabilities of GPUs, often turns preprocessing into a significant bottleneck. The imbalance between CPU and GPU resources leads to inefficiency, as the underutilized GPU cannot fully exploit its computational potential. To address this issue, we propose FastMatch, a system designed to selectively and precisely cache key data, thereby reducing the reliance on extensive I/O operations, and adjusting the CPU's performance to better align with the GPU's capabilities swiftly. FastMatch dynamically identifies important data and prompts the caching system to store it in fast DRAM. This cached data significantly enhances I/O performance while maintaining comparable accuracy levels. Additionally, FastMatch monitors and rectifies mismatches in CPU and GPU resources by tuning the number of preprocessing processes. This helps distribute preprocessing workloads more evenly, ensuring optimal utilization of both CPU and GPU resources. We implemented a prototype of FastMatch and successfully integrated it with TorchData. Our evaluations showed up to 2.2 × reduction in training time compared to vanilla PyTorch Dataloader.