design edge architectures

Service deployment for mobile users in edge-cloud multi-tier networks faces significant challenges under low-latency and highly dynamic conditions, particularly regarding service placement and migration. Method: This paper proposes the first decentralized asynchronous service deployment framework that operates without global visibility or a centralized orchestrator. It integrates a distributed consensus protocol, an asynchronous event-driven scheduler, and a resource-aware lightweight placement algorithm to enable elastic scaling and fault tolerance while guaranteeing latency constraints. Contribution/Results: Evaluated on large-scale simulations driven by real-world mobility traces, the framework achieves performance close to that of optimal centralized solutions. It incurs negligible communication overhead (effectively zero bandwidth consumption) and reduces deployment costs by 37%. The design significantly enhances system scalability and robustness, demonstrating practical viability for dynamic edge-cloud environments.

This study addresses the practical disparities and co-evolution between high-performance computing (HPC) and edge computing architectures within the cloud continuum. It presents the first large-scale empirical analysis based on 396 real-world, production-grade AWS architectures. Methodologically, we propose a multidimensional, data-driven framework encompassing service topology identification, storage type classification, architectural complexity quantification, and ML service integration statistics. Results reveal systematic differences—and complementary patterns—between HPC and edge architectures across four dimensions: core service composition (e.g., EC2 versus Greengrass/Lambda), storage design paradigms (parallel file systems versus distributed lightweight caches), complexity distributions, and ML embedding strategies. This work delivers the first industry-scale architectural benchmark for the cloud continuum, providing empirically grounded insights and methodological foundations for cross-domain architecture design, resource optimization, and cloud-native convergence of HPC and edge computing.

Serverless computing has matured into an effective execution model for edge cloud environments, enabling function level decomposition, demand driven scaling, and workflow execution across stable, well provisioned infrastructure. This success motivates extending it to the edge cloud space continuum, where Low Earth Orbit (LEO) constellations are increasingly explored as distributed compute substrates. However, existing serverless orchestration is not directly applicable in this setting, where LEO systems impose time varying contact graphs, intermittent link availability, and strict feasibility constraints on energy, memory, communication, and operational cost. This article identifies ten broken assumptions in existing serverless orchestration and organizes them into three core challenges: spatiotemporal execution over dynamic graphs, constraint aware function placement and scaling, and correctness and progress under decentralized and delayed state. It then proposes an architecture that enables robust and efficient serverless execution across the continuum, grounded in these challenges and demonstrated through a representative flood response use case.

This study addresses the infrastructure complexity of cloud-edge-end协同 architectures, which has emerged as a major bottleneck hindering developer productivity and innovation. Through 101 semi-structured interviews across 86 organizations, this work empirically identifies deployment complexity and onboarding difficulty as core challenges. It proposes four architectural directions to mitigate these issues: Object-as-a-Service (unified object abstraction), internal developer platforms, declarative AI/ML pipelines, and lightweight edge runtimes. Findings indicate that high-level abstractions and automation significantly enhance developer experience—outweighing the impact of execution performance optimizations—and thereby establish a new paradigm for platform engineering and distributed system design.

This study addresses the challenges faced by edge-cloud-native applications in cross-industry adoption, including fragmented toolchains, steep learning curves, and inconsistent performance across hybrid environments. Through in-depth interviews with practitioners from multiple sectors, the work offers the first systematic insight into the real-world pain points experienced by non-technical teams during digital transformation. It reveals that such teams prioritize productivity, service quality, and usability over cost alone. Grounded in qualitative analysis, the research identifies key platform design requirements centered on developer-friendliness, end-to-end lifecycle simplification, and SLA-aware orchestration, with a focus on distributed network computing, hybrid cloud management, and service-level agreement (SLA) assurance. These findings provide a practice-oriented roadmap for the evolution of converged cloud-network infrastructures.

This study addresses the joint optimization of end-to-end latency and system cost in hierarchical edge–cloud IoT networks by co-designing service placement, task offloading, and bandwidth allocation strategies. The original non-convex mixed-integer nonlinear programming problem is relaxed and solved via an efficient iterative algorithm that integrates successive convex approximation (SCA) with a multi-timescale decomposition approach, guaranteeing convergence to a local optimum. Experimental results demonstrate that the proposed scheme significantly outperforms existing benchmarks, achieving substantial reductions in both response latency and operational overhead while enhancing overall system efficiency, thereby validating the critical value of the integrated optimization framework.

This work addresses the challenge of resource allocation in geographically distributed and heterogeneous continuum computing infrastructures, where combinatorial explosion and limited generalization hinder effective deployment. To tackle this, the study introduces, for the first time, the pricing structures commonly found in Software-as-a-Service (SaaS) ecosystems into the resource allocation problem, formulating a unified, price-based representation of the configuration space. The authors propose PRIME, a pricing-aware analysis engine that efficiently searches for cost-optimal deployment configurations satisfying both functional and non-functional constraints. Leveraging synthetic infrastructure topologies and workload generation techniques, the project constructs a comprehensive dataset comprising 9,600 diverse scenarios, demonstrating that the proposed approach achieves both scalability and computational efficiency in complex, heterogeneous environments.

This work addresses the challenges of decentralized, low-latency service orchestration in smart grids under the integration of IoT and distributed energy management. To this end, the authors propose a unified task orchestration framework tailored for edge–fog–cloud collaborative architectures. The framework combines graph-driven modeling with swarm intelligence–based optimization to enable resource-aware, low-latency task offloading. System interoperability is ensured through adherence to Energy Data Space standards, while blockchain technology guarantees traceability of workloads. Real-world deployment experiments based on KubeEdge demonstrate that the proposed approach achieves zero-downtime service migration and sustained service availability under dynamic workloads, significantly enhancing both system responsiveness and reliability.