kubernetes

This work proposes CODECO, a framework designed to address the challenges of traditional centralized Kubernetes in federated edge environments, where heterogeneous infrastructure, device mobility, and multi-provider collaboration are prevalent. CODECO enables edge autonomy while preserving global consistency through co-orchestration of data, computation, and networking. It integrates a semantic application model, a partitioned federation mechanism, AI-driven scheduling decisions, and a hybrid governance model. Built upon an extended Kubernetes architecture, CODECO supports context-aware microservice deployment and adaptive management. The framework’s efficacy in orchestrating applications across complex federated edge-cloud scenarios is validated through a reproducible experimental platform, demonstrating its capability to efficiently manage dynamic and heterogeneous edge environments.

Existing Kubernetes schedulers struggle to simultaneously optimize user-defined QoS objectives—such as energy efficiency, cost, and global performance—while lacking automated, declarative orchestration capabilities across heterogeneous cloud-fog-edge clusters. To address this, we propose the first QoS-aware federated orchestration system. Our approach employs a lightweight, Raft-replicated resource agent architecture tightly coupled with a centralized knowledge repository, enabling, for the first time, automatic translation of user-specified YAML-declared multi-dimensional QoS constraints (e.g., latency, energy consumption, cost) into microservice placement and dynamic migration policies. The system integrates Istio service mesh and federated cluster management to support policy-driven scheduling, QoS-compliant rescheduling, and zero-touch failover. Evaluated on a nine-cluster testbed, our system demonstrates both effectiveness and scalability in meeting diverse, cross-layer QoS requirements.

This study systematically reveals the significant impact of network misconfigurations on lateral movement attack risks in Kubernetes clusters. Addressing the limited coverage of existing detection tools, we propose a security assessment framework that integrates static configuration analysis with lateral movement path modeling. We conduct a large-scale, cross-organizational empirical study across 287 open-source applications, identifying— for the first time—634 real-world network misconfiguration vulnerabilities, far exceeding the detection capacity of mainstream tools. Our findings have driven remediation efforts in over 30 critical open-source projects; the proposed mitigation strategies have been adopted by multiple enterprises, substantially enhancing network isolation and overall security posture in production Kubernetes deployments.

To address low application deployment/reconfiguration efficiency and suboptimal resource utilization in Cooperative Intelligent Transportation Systems (C-ITS) under dynamic environments and heterogeneous, multi-source requirements, this paper proposes a requirement-driven cloud-native application management approach. The method innovatively integrates Kubernetes container orchestration with the ROS 2 real-time communication framework, establishing an automated management architecture that supports on-demand microservice deployment, dynamic reconfiguration, and elastic scaling—enabling plug-and-play integration and closed-loop response of external auxiliary services. The framework is deeply optimized for edge–cloud collaboration in C-ITS. Evaluated in a collective environmental perception use case, it reduces computational resource consumption by 32% and network traffic by 41%. The prototype system is open-sourced.

Research on containerization in multi-cloud environments remains fragmented, lacking a systematic, up-to-date synthesis. Method: We conduct a Systematic Mapping Study (SMS) spanning 2013–2024, analyzing 121 high-quality publications through bibliometric analysis, thematic coding, and ISO/IEC 25010 quality attribute modeling. Contribution/Results: We propose the first four-level classification framework—“Theme–Strategy–Quality Attribute–Tactic”—identifying four core research themes, 98 implementation strategies, 10 critical quality attributes, and 47 corresponding architectural tactics. Innovatively, we introduce a two-dimensional challenge-solution taxonomy organized along Security, Automation, Deployment, and Monitoring dimensions. This yields the first structured, reusable landscape of multi-cloud containerization, bridging theoretical research and industrial practice by supporting architecture design and technology selection—thereby addressing a longstanding gap in systematic knowledge integration for this domain.

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.

This study addresses the challenge of automating the deployment of multi-service containerized applications in heterogeneous edge-cloud environments, where minimizing manual intervention while ensuring performance guarantees remains difficult. The authors evaluate and validate the open-source CODECO toolkit’s orchestration capabilities across diverse hardware platforms—including ARM, AMD, and Raspberry Pi—and lightweight Kubernetes distributions such as k3s. Experimental results demonstrate that, compared to standard Kubernetes workflows, CODECO significantly reduces human intervention during deployment while maintaining competitive deployment efficiency, runtime performance, and manageable resource overhead. These findings highlight CODECO’s strong compatibility and its potential to lower operational complexity in edge-cloud collaborative deployments.

This study presents the first systematic evaluation of the deployment feasibility of spiking neural networks (SNNs) in containerized edge environments. Focusing on resource- and energy-constrained virtual edge scenarios, we construct a testbed leveraging Docker Desktop, WSL2, and Windows 11 atop a single-node Kubernetes cluster orchestrated via K3d. We investigate end-to-end latency, throughput, classification accuracy, and concurrent behavior of SNN workloads under resource constraints and autoscaling conditions. Our findings reveal that SNNs are highly sensitive to CPU and memory availability: resource limitations substantially increase latency and reduce throughput, while classification accuracy remains stable. Furthermore, the default round-robin load-balancing strategy proves mismatched with SNNs’ long-duration inference tasks, leading to elevated tail latency. This work highlights the limitations of current stateless orchestration mechanisms in supporting neuromorphic computing paradigms.

This work addresses the challenges posed by the highly dynamic topology of low Earth orbit (LEO) satellite networks, which cause high latency and management disruptions in conventional container orchestration systems. To overcome these limitations, the authors propose a low-latency, highly stable container orchestration control plane tailored for LEO satellites. The design innovatively integrates a distributed ground-based control node architecture, an orbit prediction model, and an orbit-aware scheduling strategy to enable proximity-based controller binding and dynamic task assignment. Experimental evaluations based on real satellite trajectories demonstrate that the proposed approach reduces average management latency by 59% compared to existing methods and completely eliminates management interruptions, thereby significantly enhancing the continuity and efficiency of satellite node management.

Kubernetes’ default scheduler employs lightweight heuristic policies, often causing resource fragmentation and failure to schedule high-priority Pods. This paper proposes a constraint programming (CP)-based Pod packing optimization method, implemented using OR-Tools as a pluggable scheduler. It verifies the optimality of the default scheduler’s decisions—and provides fallback optimizations—within strict time budgets (1 s or 10 s). Our key contribution is the first application of CP modeling to Kubernetes scheduling verification and repair, uniquely balancing formal correctness guarantees with real-time feasibility. Experimental evaluation on small-to-medium clusters shows that our approach improves scheduling success rates by over 44% within 1 second and over 73% within 10 seconds, while strictly certifying the optimality of the default scheduler’s solution in more than 19% of cases.