<![CDATA[Web-based information systems (WBIS) are increasingly vital across sectors like healthcare, smart cities, and industrial automation. However, traditional cloud computing struggles to meet the low-latency and high-performance demands of these applications due to network delays and bandwidth limitations. Edge computing offers a solution by processing data closer to the source, improving responsiveness and efficiency. This study aims to explore how edge computing can optimize the performance of WBIS by evaluating different edge architectures, task offloading strategies, and performance optimization frameworks. The goal is to identify effective methods for reducing latency, improving response times, and enhancing resource management in distributed edge environments. A systematic literature review was conducted, focusing on peer-reviewed articles published between 2017 and 2025. Four major academic databases were searched using targeted keywords related to edge computing and WBIS. Articles were screened and selected based on inclusion criteria emphasizing relevance, recency, and quality. Data from the final selection of studies were synthesized thematically to identify trends, techniques, and gaps. Findings indicate that hierarchical, distributed, and hybrid edge architectures significantly reduce latency and bandwidth usage. Task offloading strategies like dynamic offloading and reinforcement learning enhance scalability and computational efficiency. Performance frameworks that integrate modular edge-cloud coordination and predictive resource management improve real-time data processing and system responsiveness. Edge computing effectively addresses the limitations of traditional cloud models by enabling localized processing and intelligent resource management in WBIS. Continued research is needed to tackle challenges related to heterogeneity, security, and real-world deployment. The study provides a foundation for developing scalable, high-performance edge-enabled web applications.]]>
