Scalability and Performance Optimization Strategies in Large-Scale System Architectures

Abstract

This paper aims to break away from traditional thinking paradigms by introducing theories and methods from complexity science to provide innovative and effective solutions for scalability and performance optimization of large-scale system architectures. The paper first constructs a complex network model for large-scale system architecture, which includes nodes (services), edges (interactions), and global attributes, along with dynamic models reflecting key metrics such as system performance and scalability, providing a theoretical foundation for subsequent research. Subsequently, from a complexity perspective, it innovatively proposes scalability strategies, including adaptive scalability strategies for complex network optimization, data-driven complex system regulation, and elastic computing and service orchestration mechanisms. In terms of performance optimization, it applies tools such as complex systems observation theory and network flow theory to explore insights and breakthroughs in complexity related to full-link performance monitoring and diagnostics, asynchronous communication, task scheduling, and software-hardware collaborative optimization. Finally, the paper summarizes the research findings, distilling the core concepts and methodological innovations of scalability and performance optimization strategies from a complexity perspective. It also looks ahead to the prospects of further integrating complexity science and information technology in the future, along with potential research topics in new scenarios and challenges.