arXiv subjects

Shyam Jesalpura

Publications and source records attributed to Shyam Jesalpura.

4 recordsLinked to original sources

arXiv2026

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

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.

cs.DC↗

arXiv2026

Flare: Leveraging Serverless Elasticity to Absorb Microservice Load Spikes

Online services strive to maintain application responsiveness even when the traffic is unpredictable and fluctuating. Today's online services are commonly deployed as chains of microservices, each microservice packaged as one or more containers inside virtual machines (VMs). While performant and affordable when the load is steady, VM-based deployments are known to be slow to scale when the load spikes, resulting in degraded performance for end-users of the service. To avoid such performance degradations, service providers can over-provision their deployments; however, such a strategy is costly and inefficient, leaving resources under-utilized for extended periods. To address the challenge of unpredictable load spikes, we propose Flare, a hybrid microservice architecture that combines VMs with serverless computing. Flare utilizes VMs to cost-effectively handle steady workloads and leverages serverless elasticity to absorb traffic spikes. When a spike occurs, Flare detects which specific service(s) are overloaded and shifts the excess load of only those services to serverless, thus minimizing the cost overhead. Flare seamlessly integrates into existing auto-scaling and serverless infrastructure, requiring minimal changes to the control plane and no modifications to the application.

cs.DC↗

arXiv2025

Odyssey: An End-to-End System for Pareto-Optimal Serverless Query Processing

Running data analytics queries on serverless (FaaS) workers has been shown to be cost- and performance-efficient for a variety of real-world scenarios, including intermittent query arrival patterns, sudden load spikes and management challenges that afflict managed VM clusters. Alas, existing serverless data analytics works focus primarily on the serverless execution engine and assume the existence of a "good" query execution plan or rely on user guidance to construct such a plan. Meanwhile, even simple analytics queries on serverless have a huge space of possible plans, with vast differences in both performance and cost among plans. This paper introduces Odyssey, an end-to-end serverless-native data analytics pipeline that integrates a query planner, cost model and execution engine. Odyssey automatically generates and evaluates serverless query plans, utilizing state space pruning heuristics and a novel search algorithm to identify Pareto-optimal plans that balance cost and performance with low latency even for complex queries. Our evaluations demonstrate that Odyssey accurately predicts both monetary cost and latency, and consistently outperforms AWS Athena on cost and/or latency.

cs.DB↗

arXiv2025

Shattering the Ephemeral Storage Cost Barrier for Data-Intensive Serverless Workflows

Serverless computing is a popular cloud deployment paradigm where developers implement applications as workflows of functions that invoke each other. Cloud providers automatically scale function instances on demand and forward workflow requests to appropriate instances. However, current serverless clouds lack efficient cross-function data transfer, limiting the execution of data-intensive applications. Functions often rely on third-party services like AWS S3, AWS ElastiCache, or multi-tier solutions for intermediate data transfers, which introduces inefficiencies. We demonstrate that such through-storage transfers make data-intensive deployments economically impractical, with storage costs comprising more than 24-99% of the total serverless bill. To address this, we introduce Zipline, a fast, API-preserving data communication method for serverless platforms. Zipline enables direct function-to-function transfers, where the sender function buffers payloads in memory and sends a reference to the receiver. The receiver retrieves the data directly from the sender's memory, guided by the load balancer and autoscaler. Zipline integrates seamlessly with existing autoscaling, maintains invocation semantics, and eliminates the costs and overheads of intermediate services. We prototype Zipline in vHive/Knative on AWS EC2 nodes, demonstrating significant improvements. Zipline reduces costs and enhances latency and bandwidth compared to AWS S3 (the lowest-cost solution) and ElastiCache (the highest-performance solution). On real-world applications, Zipline lowers costs by 2-5x and reduces execution times by 1.3-3.4x versus S3. Compared to ElastiCache, Zipline achieves 17-772x cost reductions while improving performance by 2-5%.

cs.DC↗