KiSS: A Novel Container Size-Aware Memory Management Policy for Serverless in Edge-Cloud Continuum

TL;DR

KiSS addresses cold-start and inter-function memory contention in edge FaaS by introducing a container size-aware memory management policy that partitions memory into small-function and large-function warm pools. Guided by a workload analysis, KiSS isolates high-frequency, low-memory containers from infrequent, memory-intensive ones, achieving substantial reductions in cold starts (up to 60%) and function drops (up to 56.5%) in edge-like scenarios. The approach is evaluated with a KiSS-enhanced FaaSCache simulator across edge-relevant memory ranges (1–24 GB), demonstrating policy independence across common eviction strategies and strong robustness under stress. The work highlights the practicality of workload-driven memory management for edge deployments and suggests adaptive extensions to further optimize performance under dynamic conditions.

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.

Figures (16)

• Figure 1: Memory contention in warm pools: (a) Large containers displace small containers, disrupting locality and increasing cold starts; (b) Small containers dominate due to high invocation ratio starving Large containers.
• Figure 2: Percentile distribution of memory footprints for Azure Functions workloads.
• Figure 3: Normalized invocation trends for small and large functions.
• Figure 4: Percentile distribution of inter-arrival times for small and large functions.
• Figure 5: Percentile distribution of cold start latency for small and large functions.