edge computing

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.

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.

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 problem of edge service deployment failures in digital healthcare caused by network outages affecting centralized registries. To mitigate this, we propose a three-tier distributed registry architecture integrating remote public, MEC-private, and LAN-local tiers. By combining edge computing with Docker containerization, the proposed approach enables proximity-aware service orchestration and high-availability distribution. Experimental evaluations demonstrate that under network disconnection scenarios, private and local registries significantly outperform their public counterparts in terms of deployment latency, system load, and energy consumption. These findings indicate that the proposed architecture effectively enhances the fault tolerance and overall resilience of edge services in healthcare environments.

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 cost and latency for large-scale workflow deployment in serverless edge computing by proposing an optimal placement model based on nonlinear integer programming. To overcome computational bottlenecks, we introduce a novel decomposition strategy that effectively balances scalability with problem-specific attributes. Experimental results demonstrate that the proposed method exhibits strong scalability and achieves an average performance improvement of 10% over conventional heuristic algorithms. By significantly reducing both user costs and execution latency, this work establishes a new paradigm for the efficient deployment of complex workflows in edge environments.

This study addresses the complex trade-offs among cost, performance, and latency in mobile edge computing scenarios, where existing research lacks a systematic modeling framework for edge–cloud architectures. The authors present the first closed-form queueing network model to quantitatively analyze how workload mobility and system dynamics jointly impact end-to-end latency and operational cost. Through both simulation and real-world validation, the model’s accuracy and practical relevance are demonstrated. The work highlights the critical roles of mobility patterns and system utilization, offering theoretical foundations and actionable insights for resource provisioning and optimization in 5G and future mobile edge computing systems.

Deploying distributed lightweight workloads on resource-constrained ultra-micro edge devices (e.g., Raspberry Pi Zero) without centralized data centers poses significant challenges in achieving low latency, low power consumption, and localized execution. Method: This paper proposes Pico-Cloud, a micro-edge cloud architecture integrating lightweight container virtualization, decentralized service discovery, and minimalist orchestration to jointly optimize computation, networking, storage, and energy efficiency on edge hardware. Contribution/Results: Evaluated on single-board computer clusters, Pico-Cloud sustains stable distributed cloud services with end-to-end latency under 50 ms and >60% power reduction. Unlike conventional edge cloud approaches, it is the first fully decentralized micro-cloud solution featuring hardware cost <$15 per node and autonomous offline operation. It establishes a scalable, low-cost, and green infrastructure paradigm for applications including rural connectivity, educational computing clusters, and edge AI inference.

To address high energy consumption of containerized applications and the lack of fine-grained energy awareness in resource scheduling within heterogeneous edge-cloud environments, this paper proposes an embedded energy-aware scheduling framework. The framework integrates real-time power consumption metrics across both computation and networking dimensions into the Kubernetes scheduler and implements dynamic energy-efficiency optimization on an ARM-based physical edge testbed. Its key innovations include a lightweight hardware-coordinated monitoring mechanism and a redesigned scheduling decision logic that jointly optimizes workload distribution and energy consumption. Experimental results demonstrate that, under high-load conditions, the proposed approach reduces total system energy consumption by 23.7% compared to vanilla Kubernetes, while maintaining QoS guarantees and high resource utilization—thereby significantly enhancing the energy efficiency of edge-cloud collaborative systems.