Spandana: Reconciling Strict SLOs with Low Cost under Fine-Grained Load Fluctuations

2026-06-29

AI Summary

This work addresses the challenge of simultaneously achieving strict service-level objectives (SLOs), high resource utilization, and low cost in cloud online services under fine-grained load fluctuations. The authors propose a hybrid architecture that decouples SLO enforcement from cost optimization by employing a lightweight, request-level scheduler to dynamically route incoming requests to either virtual machines (VMs) or Function-as-a-Service (FaaS) backends. This approach is complemented by a traffic-aware resource allocation algorithm that optimizes VM provisioning. By separating SLO guaranteeing mechanisms from cost-control strategies for the first time, the proposed method achieves CPU utilization of 76%–86% while strictly meeting SLOs, reducing operational costs by 5%–44% compared to three state-of-the-art baseline approaches.

Abstract

Cloud-based online services face significant sub-second load fluctuations while needing to meet strict Service Level Objectives (SLOs). Cluster operators often over-provision resources to protect SLOs, sacrificing utilization and cost efficiency. Existing reactive and proactive autoscalers, serverless (FaaS) deployments, and VM/FaaS hybrid systems fail to reconcile strict SLO compliance with low cost and high utilization under fine-grained load fluctuation. We introduce Spandana, an architecture that addresses this trade off by decoupling SLO enforcement from cost optimization. A lightweight controller colocated with each application VM enforces SLOs by steering each arriving request between the VM and FaaS. Requests that can meet the SLO stay on the VM; the remaining requests are forwarded to a stock FaaS layer such as AWS Lambda. For cost optimization, Spandana's resource allocator determines the most-efficient VM provisioning by accounting for VM cost, FaaS cost, and traffic volatility, allowing the VM pool to run at high utilization. Our evaluation shows that Spandana maintains strict SLO adherence, achieves 76-86% CPU utilization, and reduces cost by 5-44% over three SOTA baselines.

Problem

• Service Level Objectives
• load fluctuations
• cost efficiency
• resource utilization
• autoscaling

Innovation

• SLO-aware autoscaling
• VM/FaaS hybrid architecture
• fine-grained load fluctuation
• cost-efficient resource provisioning
• request steering