Serverless computing has emerged as a compelling cloud paradigm due to its simplified development model, automatic scalability, and fine-grained billing. While its stateless execution model enables high elasticity and resource efficiency, it poses noteworthy challenges for building complex stateful applications. To bridge this gap, modern serverless platforms rely on external cloud storage systems to manage persistent state, giving rise to stateful serverless computing. However, this decoupled architecture introduces two fundamental limitations: performance bottlenecks caused by frequent remote state accesses, and state reliability issues stemming from function crash retries. We propose Arcus, a high-performance function state I/O system co-designed for both idempotent execution and low-latency access. Arcus integrates two key techniques: CFLog, a coordination-free logging mechanism that eliminates internal ordering overhead while ensuring idempotent function execution, and FCCache, a cache system optimized for the dispersed invocation pattern of serverless workloads, which reduces read/write latency by improving local hit rates for reads and shortening the write path. Compared to the state-of-the-art system Halfmoon, Arcus reduces read/write latency by 80.4% and 87.9%, respectively. In evaluations using three real-world applications, Arcus achieves an average 60.3% reduction in end-to-end request latency, demonstrating its effectiveness for fast and reliable function state I/O.
Yijie Liu, Zhuo Huang, Hanxiang Huang et al.· IEEE Transactions on Paralle...· 0 citations
Container images are fundamental to cloud deployment, with their build instructions (e.g., Dockerfiles) critically impacting the efficiency and stability of cloud service. Manually authoring these instructions is error-prone, while Large Language Models (LLMs) lack the domain knowledge to generate both correct and optimized Dockerfiles reliably. This problem may cause runtime failures, prolonged deployment times and increased storage overhead. This paper introduces a novel knowledgeenhanced approach to automate Dockerfile generation. First, we construct a Dockerfile Instructions Knowledge Graph (DIKG) by analyzing a large corpus, capturing complex dependencies among images, packages, and commands. Leveraging DIKG, we design DKRAG, a retrieval-augmented generation system that guides an LLM to interpret user requirements and produce semantically accurate instructions. The output is further optimized via log-based repair and static dependency-aware refactoring for correctness, layer sharing, and minimal image size. Comprehensive experiments show our approach significantly improves the generation accuracy while also reducing build time and storage overhead compared to state-of-the-art methods.
Kun Wang, Yao Wu, Hao Fan et al.· Fall Joint Computer Conferen...· 0 citations
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