A Comparative Analysis of Provisioned Concurrency Types for Cloud-Based Applications

Abstract

Abstract The increasing adoption of serverless computing, particularly AWS Lambda, has introduced both performance benefits and challenges. A major concern is the cold start latency, which affects response times and scalability, particularly for time-sensitive applications. To mitigate this issue, AWS introduced provisioned concurrency, which ensures pre-initialized function instances to reduce startup delays. However, selecting the appropriate concurrency model static or dynamic (scheduled or auto-scaling) remains a challenge due to the trade-offs between performance and cost. This dissertation presents a comparative analysis of provisioned concurrency types in AWS Lambda, evaluating their impact on various workload domains, including web applications, IoT backend services, and data processing systems. Real-world and simulated workloads were analyzed using statistical techniques, such as Mahalanobis distance calculations, to assess performance variations. A simple rule-based guide was developed to assist in selecting the most suitable concurrency type based on application needs, balancing cost efficiency and performance. For IoT workloads, scheduled provisioned concurrency (Mahalanobis distance: 10.80; cost: $0.0888/1000 invocations) achieved optimal balance, reducing latency by 8.7% over static provisioning. E-commerce applications benefited most from static provisioning (Mahalanobis distance: 9.00; cost: $0.0054/1000 invocations), ensuring low latency during traffic spikes. Data processing systems favored scheduled concurrency (Mahalanobis distance: 3.009; cost: $3.576/1000 invocations) for predictable batch jobs. Cost-Distance Ratio (CDR) analysis further validated these recommendations, prioritizing strategies with CDR < 1. The study provides empirical guidelines to optimize AWS Lambda deployments, recommending scheduled concurrency for IoT, static for e-commerce, and scheduled for data processing, ensuring cost-effective performance across domains.