Empirical Analysis of Cloud-Edge Infrastructure Complexity: Practitioner Pain Points and Architectural Directions

Abstract

The proliferation of cloud, edge, and Internet of Things (IoT) computing has created unprecedented opportunities for distributed applications. However, this architectural shift introduces profound infrastructural complexity, acting as a significant barrier to developer productivity and innovation. In this paper, we present an empirical analysis based on 101 semi-structured interviews across 86 organizations to investigate the state of cloud-native development practices, pain points, and expectations. Our findings quantitatively validate that deployment complexity (38.6%) and onboarding difficulty (35.6%) are the dominant operational bottlenecks, while developers heavily prioritize productivity (53.5%) and automation (44.6%) over raw performance optimization. Based on these empirical insights, we examine four architectural directions that address the validated pain points: unified object abstractions (Object-as-a-Service), platform engineering via Internal Developer Platforms, declarative AI/ML serving pipelines, and lightweight edge runtimes based on WebAssembly. Furthermore, we detail the multi-stakeholder ecosystem required for adopting novel infrastructure paradigms, emphasizing that security, operational integration, and strict multi-tenant isolation are prerequisites for production readiness. Our results demonstrate that the primary barrier to distributed computing adoption is not execution performance but infrastructural complexity, and that declaratively governed, higher-level abstractions across multiple paradigms offer viable architectural paths toward alleviating it.