Abstract

Modern web applications rely on continuous client–server communication, often resulting in redundant network requests, elevated latency, and unnecessary energy consumption. As distributed microservices architectures evolve, inefficiencies in how clients interact with backend APIs can significantly affect application responsiveness and sustainability. Prior studies highlight how secure service-to-service communication models, including token-less authentication and optimized mTLS exchanges, can reduce overhead in microservice interactions [Mohammad 2025, IJCET]. Similarly, recent work on energy-efficient cloud-native architectures demonstrates the benefits of asynchronous communication, ARM-based compute nodes, and carbon-aware autoscaling for reducing operational footprint in financial and enterprise workloads [Mohammad 2025, IJCA]. Building on these insights, this paper proposes an adaptive client–server optimization model that dynamically adjusts request patterns, caching strategies, and synchronization frequency based on user context, system load, and backend energy profiles. The model integrates Zero Trust API boundaries aligned with NIST's Zero Trust Architecture principles [NIST 2020], and incorporates performance techniques such as request collapsing, incremental synchronization, and context-aware caching, inspired by modern web performance research [Akamai 2021; W3C 2017]. Architectural evaluation shows that the proposed approach improves responsiveness while reducing redundant compute cycles and energy usage—addressing tail-latency challenges common in large-scale microservices [Dean & Barroso 2013]. Experimental analysis and qualitative benchmarking demonstrate that adaptive optimization strategies provide measurable performance gains, making them suitable for real-world modern web applications deployed on cloud-native platforms.

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