CNASIM: A Customizable Simulation Library for Cloud-Native Application Performance Modeling

Cloud-Native Architectures for Scalable Enterprise Applications

The paper addresses the transformation of enterprise application infrastructure out of on-premise legacy resource setting into service-based cloud environments properly configured to scale horizontally, and presents experimental evaluations of the response time, throughput, service resiliency, and infrastructure utilization in both traditional and cloud-native deployments.

Mobile GUI Testing Fragility: A Study on Open-Source Android Applications

Practical Engineering Approaches to Scalable Cloud Architecture and Computational Resource Optimization

The engineering of scalable cloud systems has matured from empirical practice into a discipline grounded in formal scalability theory, distributed systems research, and production-derived architectural principles. Existing treatments address load scalability, microservices consistency, autoscaling, and production ML reliability as separate problems, each evaluated through single-mechanism studies conducted under stationary or weakly non-stationary conditions that diverge from production environments where these problems interact simultaneously. This review closes that gap by tracing a structural pattern common to all four domains: each first-order engineering solution introduces a second-order problem of comparable difficulty, a regularity not previously consolidated across the scalability, decomposition, resource optimization, and ML deployment literatures. Its added value over prior reviews lies in connecting formal theoretical constraints, including Amdahl’s Law, the Universal Scalability Law, and Conway’s Law, with production-derived quantitative benchmarks, rather than treating theory and practice as separate registers.The source pool combines foundational theoretical works, empirical studies published in IEEE and ACM venues between 2018 and 2024, and systematic reviews and large-sample case study collections, most notably Velepucha and Flores (2023), covering 71 primary studies of microservices migration, and Paleyes et al. (2022), synthesizing 209 machine learning deployment case studies. Inclusion required that a source report a formal theoretical result, an empirical measurement obtained under stated experimental conditions, or a synthesis of multiple primary studies; sources offering only prescriptive guidance without supporting measurement were excluded. Synthesis proceeded by extracting, for each domain, the mechanism, experimental conditions, and quantitative outcome, then identifying structural patterns recurring across domains.The review’s practical engineering contribution is a set of decision criteria for consistency protocol selection, autoscaling architecture design, and ML deployment monitoring that translate these findings into guidance actionable by practitioners operating production cloud systems.

Enhancing the Kubernetes Scheduler: A State-of-the-Art Review from Cloud to Edge

A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.

WASP: A Configurable Framework for Portable Stateful Serverless Applications

WebAssembly (WASM) is emerging as a lightweight alternative to containers for Function-as-a-Service (FaaS) across the edge-cloud continuum. However, existing WASM-based serverless platforms are tightly coupled to specific execution engines and predominantly designed for stateless workloads. This clashes with the heterogeneity of edge deployments, which demand support for stateful applications under diverse hardware and workload constraints. We introduce WASP, a configurable framework that brings stateful serverless execution to the edge-cloud continuum. By abandoning monolithic architectures in favor of strictly decoupled, pluggable components, WASP lets system administrators swap the WASM runtime and the datastore to fit available resources and application requirements, without altering application code. Configurable lifecycle and caching policies further enable fine-tuning for diverse non-functional requirements. Our experimental evaluation demonstrates that WASP introduces negligible runtime overhead and, by swapping runtimes, datastores, and policies, exposes radically different memory and latency profiles, confirming its adaptability to the heterogeneous constraints of the edge-cloud continuum.

OMBench: Taming Data Management Requirements in Cloud-Native Applications