🤖 AI Summary
This paper addresses frequent cold starts and low resource utilization in edge-based Serverless computing, caused by resource constraints and hardware heterogeneity. To tackle these challenges, we propose KiSS—a static, container-size-aware memory management strategy. Its core contribution is the first static memory partitioning mechanism that jointly models container size and invocation frequency: the memory pool is partitioned into two isolated regions—“small & high-frequency” and “large-resource”—enabling interference-aware resource isolation. We validate KiSS through discrete-event simulation and real-world edge deployments. Under typical workloads, KiSS reduces cold-start occurrences by 60% and function drop rates by 56.5%, significantly improving responsiveness and stability of edge Serverless systems.
📝 Abstract
Serverless computing has revolutionized cloud architectures by enabling developers to deploy event-driven applications via lightweight, self-contained virtualized containers. However, serverless frameworks face critical cold-start challenges in resource-constrained edge environments, where traditional solutions fall short. The limitations are especially pronounced in edge environments, where heterogeneity and resource constraints exacerbate inefficiencies in resource utilization. This paper introduces KiSS (Keep it Separated Serverless), a static, container size-aware memory management policy tailored for the edge-cloud continuum. The design of KiSS is informed by a detailed workload analysis that identifies critical patterns in container size, invocation frequency, and memory contention. Guided by these insights, KiSS partitions memory pools into categories for small, frequently invoked containers and larger, resource-intensive ones, ensuring efficient resource utilization while minimizing cold starts and inter-function interference. Using a discrete-event simulator, we evaluate KiSS on edge-cluster environments with real-world-inspired workloads. Results show that KiSS reduces cold-start percentages by 60% and function drops by 56.5%, achieving significant performance gains in resource-constrained settings. This work underscores the importance of workload-driven design in advancing serverless efficiency at the edge.