通过预测资源存活时间动态调整冷启动，提升无服务器性能与成本效率。
Adaptive Serverless Resource Management via Slot-Survival Prediction and Event-Driven Lifecycle Control
- 基于槽位存活预测动态调节空闲时长，实现智能资源管理。
- 在多云环境下冷启动减少51.2%，成本效率提升近2倍。
- 适合关注无服务器架构优化与资源调度的研究者和开发者。
无服务器计算消除了基础设施管理开销，但带来了冷启动延迟和资源利用率低下的挑战。传统静态资源配置在负载波动时易导致效率低下，引发性能下降或成本过高。本文提出一种自适应工程框架，通过事件驱动架构与概率建模优化无服务器性能。设计双策略机制：动态调整空闲时长，并基于槽位存活预测实施智能请求等待策略。结合滑动窗口聚合与异步处理，系统可主动管理资源生命周期。实验表明，在多云环境中，该方法将冷启动减少高达51.2%，成本效率接近基线方法的2倍。
原文摘要 · Abstract (English)
Serverless computing eliminates infrastructure management overhead but introduces significant challenges regarding cold start latency and resource utilization. Traditional static resource allocation often leads to inefficiencies under variable workloads, resulting in performance degradation or excessive costs. This paper presents an adaptive engineering framework that optimizes serverless performance through event-driven architecture and probabilistic modeling. We propose a dual-strategy mechanism that dynamically adjusts idle durations and employs an intelligent request waiting strategy based on slot survival predictions. By leveraging sliding window aggregation and asynchronous processing, our system proactively manages resource lifecycles. Experimental results show that our approach reduces cold starts by up to 51.2% and improves cost-efficiency by nearly 2x compared to baseline methods in multi-cloud environments.
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