In the complex hardware and software hybrid system, the rapid increase of the software scale and the system complexity bring the important challenge to the software security modeling. There are still many functional or semantic problems for traditional modeling language and modeling method, when they are used into the security modeling. Therefore, based on the Architecture Analysis and Design Language (AADL), this paper proposes the AADL error model and extracted the timing fault tree. After comprehensive analysis by using the common factor failure analysis and the hardware and software failure analysis, we can obtain the generation mechanism of danger. By establishing the AADL multi-view security model of the software system, and converting it into the Timed Abstract State Machine (TASM) and the Markov model, this paper forms a method of the security verification and evaluation based on extended AADL model. Finally, we confirm the effectiveness of the proposed method by performing security analysis for an instance.

We consider ML query processing in distributed systems where GPU-enabled workers coordinate to execute complex queries: a computing style often seen in applications that interact with users in support of image processing and natural language processing. In such systems, coscheduling of GPU memory management and task placement represents a promising opportunity. We propose Compass, a novel framework that unifies these functions to reduce job latency while using resources efficiently, placing tasks where data dependencies will be satisfied, collocating tasks from the same job (when this will not overload the host or its GPU), and efficiently managing GPU memory. Comparison with other state of the art schedulers shows a significant reduction in completion times while requiring the same amount or even fewer resources. In one case, just half the servers were needed for processing the same workload.

Interactive intelligent computing applications are increasingly prevalent, creating a need for AI/ML platforms optimized to reduce per-event latency while maintaining high throughput and efficient resource management. Yet many intelligent applications run on AI/ML platforms that optimize for high throughput even at the cost of high tail-latency. Cascade is a new AI/ML hosting platform intended to untangle this puzzle. Innovations include a legacy-friendly storage layer that moves data with minimal copying and a "fast path" that collocates data and computation to maximize responsiveness. Our evaluation shows that Cascade reduces latency by orders of magnitude with no loss of throughput.

Industrial Artificial Intelligence (IAI) services are increasingly integral to smart manufacturing, especially in quality assurance tasks like defect detection. This paper presents the design, implementation, and evaluation of a video-based visual anomaly detection (VAD) system that runs at inspection stations on a smart shop floor. Our system processes real-time video streams from multiple cameras mounted around a conveyor belt to detect surface-level defects in mechanical components. To meet stringent latency and accuracy requirements, an edge-cloud architecture powered by AI accelerators and InfiniBand networking is adopted. The IAI service features key frame extraction modules, fine-tuned lightweight VAD models, and optimization techniques such as batching and microservice-level parallelism. The design choices of AI modules are carefully evaluated to balance effectiveness and efficiency. As a result, the system latency is optimized by 57%. In addition to the high-performance solution, a cost-efficient alternative is also suggested that is able to complete the task within the time frame.