Adaptive Serverless Resource Management via Slot-Survival Prediction and Event-Driven Lifecycle Control

2026-04-07

AI Summary

This work addresses the high cold-start latency and low resource utilization inherent in static resource allocation within serverless computing environments. To overcome these limitations, the authors propose a dynamic resource management mechanism that integrates event-driven orchestration with probabilistic modeling. By leveraging sliding-window aggregation and asynchronous processing, the approach constructs a slot survival prediction model, which intelligently adapts instance idle timeouts and request queuing strategies to proactively govern function instance lifecycles. Experimental evaluation across multi-cloud platforms demonstrates that the proposed scheme reduces cold-start latency by up to 51.2% and nearly doubles resource cost efficiency, significantly outperforming conventional static provisioning strategies.

Abstract

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.

Problem

• serverless computing
• cold start latency
• resource utilization
• workload variability
• cost-efficiency

Innovation

• slot-survival prediction
• event-driven lifecycle control
• adaptive serverless resource management
• cold start reduction
• probabilistic modeling