Serverless Architecture Patterns: Deployment, Cost, and Latency Analysis
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I4P107Keywords:
Serverless Computing, Function-as-a-Service (FaaS), Cloud Architecture, Latency, Cost Optimization, Deployment Patterns, Microservices, AWS Lambda, Google Cloud Functions, Azure FunctionsAbstract
Serverless as one of the promising cloud computing technologies, Serverless allows resources to be provisioned automatically and assigned by cloud providers. Serverless architecture provides the ability to forget about infrastructure management and deliver auto-scaling capability, implement event-driven deployment, and charge at a very fine-grain level through the advent of Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS). The present paper gives a comprehensive overview of serverless architecture including its deployment models, cost considerations and changes in latency. Some of the following deployment patterns to be discussed in this paper will include microservices orchestration, fan-out/fan-in and event stream processing. An integrated evaluation model is conducted, that means the competitiveness of AWS Lambda, Azure Functions and Google Cloud Functions are compared. Cold start latency, execution time, and scalability are measured by evaluating real-world workloads. In the cost analysis, the time, memory, and calls of execution are considered. We also evaluate design trade-offs, and how to minimize latency and optimize costs. In our findings, albeit the strong benefits of serverless in terms of speed and cost-effectiveness, architectural and operational constraints will have to be taken into consideration in high-performance and real-time systems
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