Spandana: Reconciling Strict SLOs with Low Cost under Fine-Grained Load Fluctuations
2026-06-29 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors present Spandana, a system designed to help cloud services handle sudden changes in workload without wasting resources or breaking performance promises (SLOs). Their approach uses a small controller with each virtual machine (VM) that decides whether to handle requests itself or send them to a serverless function (FaaS) like AWS Lambda based on whether it can meet the performance goals. This separation allows better resource use and cost savings. Their tests show Spandana keeps performance promises, uses the CPU efficiently, and cuts costs compared to other methods.
Service Level Objectives (SLOs)cloud computingvirtual machines (VM)serverless functions (FaaS)autoscalingresource allocationAWS Lambdacost optimizationCPU utilizationload balancing
Authors
Dilina Dehigama, Shyam Jesalpura, Zeyu Xu, Marton Nemeth, Shengda Zhu, Marios Kogias, Boris Grot
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.