Serverless Computing Framework for High-Performance Distributed Applications

Abstract

Serverless computing has emerged as a transformative cloud computing paradigm for developing highly scalable, event-driven, and cost-efficient distributed applications. Unlike conventional cloud architectures that require manual server provisioning, infrastructure management, and capacity planning, serverless platforms automatically allocate computing resources, scale functions dynamically, and charge users only for actual resource consumption. Despite these advantages, executing high-performance distributed applications using serverless architectures remains challenging due to cold-start latency, function orchestration complexity, state management, communication overhead, resource scheduling, and performance optimization. Recent advances in cloud-native computing, Function-as-a-Service (FaaS), workflow orchestration, edge computing, container optimization, and intelligent resource management have created new opportunities for designing high-performance serverless execution frameworks capable of supporting compute-intensive distributed workloads. This study proposes a Serverless Computing Framework for High-Performance Distributed Applications (SCF-HPDA) that integrates event-driven function execution, intelligent resource allocation, adaptive scheduling, workflow orchestration, distributed state management, performance monitoring, and auto-scaling into a unified computational architecture. The proposed framework employs adaptive scheduling and serverless orchestration techniques to minimize execution latency while maximizing throughput, scalability, resource utilization, and system reliability.