Modern grid and cloud-native distributed infrastructures increasingly rely on Kubernetes control planes to orchestrate shared resources and services. Existing analyses of Kubernetes RBAC help identify over-permissioning and configuration risks, yet it remains difficult to determine whether a denied high-impact operation may become reachable through a short sequence of permitted RBAC changes. We present RBAC-ESCAPE, a formal verification approach that analyzes both direct authorization and bounded privilege-escalation reachability. When a security invariant is reachable, RBAC-ESCAPE synthesizes a minimal replayable witness that identifies the roles, bindings, and authorization changes enabling the violation. Direct decisions can be cross-validated with SubjectAccessReview, while representative multi-step witnesses are checked through replay in a test cluster. We evaluated RBAC-ESCAPE on open Helm and GitOps/cluster-configuration artifacts, covering 255 charts, 55 repositories, 25,030 manifests, and 2,576 RBAC objects. The results show that artifact-scale analysis is feasible and that bounded reachability can complement snapshot-level auditing with actionable escalation evidence.
UAV-assisted edge-cloud computing has emerged as a flexible paradigm for providing on-demand service provisioning in disaster rescue, temporary hotspot expansion, and remote-area coverage scenarios. However, stochastic task arrivals, time-varying air-to-ground links, and transmission queue backlogs make multi-UAV service deployment a challenging long-term energy-delay-coupled optimization problem. Existing UAV-assisted edge computing schemes mainly optimize coverage, trajectory, or transmission energy, while queue stability is often treated as a posterior performance metric rather than being explicitly incorporated into the deployment decision process. To address this limitation, this paper proposes UD-DDPG, a Lyapunov-guided queue-stable dynamic deployment framework for multi-UAV-assisted edge networks. The proposed framework jointly models task arrivals, air-to-ground transmission rates, UAV mobility constraints, and queue evolution, with the objective of minimizing long-term task transmission energy while maintaining queue stability. A Lyapunov drift-plus-penalty method is introduced to construct virtual queues and transform the long-term delay constraint into a per-slot stability-aware optimization term. This term is further embedded into the reward design of a deep deterministic policy gradient algorithm, enabling UAVs to learn energy-efficient and queue-aware deployment policies in continuous action spaces. Based on centralized training and distributed execution, multiple UAVs adaptively adjust their flight directions and distances according to user distribution, queue states, and channel conditions. Simulation results show that UD-DDPG reduces average task transmission energy by 15.8
While anomaly detection is essential for cloud computing and predictive maintenance, approaches that bridge continual learning with environmental sustainability remain largely unexplored. In production environments, evolving data distributions cause performance degradation of machine learning models, and naive adaptation may lead to forgetting of past patterns that are likely to reoccur. In this study, we formulate temporal concept-incremental anomaly detection for both univariate and multivariate time series. We introduce time-based and entity-based concept extraction strategies together with Wasserstein-distance diagnostics for inter- and intra-concept distribution drift, and define an evaluation protocol that measures detection quality, knowledge retention, and energy consumption. We further propose Selective Temporal Replay (STR), a replay strategy for autoencoder-based continual anomaly detection that combines diversity-based initialization, novelty-gated admission, retention-aware replacement, and age-weighted preservation of stored sequences. The empirical study comprises more than 5,400 experiments across four datasets, nine autoencoder architectures, and ten continual learning strategies. On Yahoo! A1, STR obtains the highest ROC-AUC in seven of the nine architectures and is the only strategy with positive backward transfer on all four datasets; under the smallest memory budgets, it obtains the highest ROC-AUC among replay methods on Yahoo! A1 and SMD. The sustainability analysis identifies when selective replay justifies its additional energy cost and when regularization-based continual learning strategies are preferable.
The increasing density and heterogeneity of Internet of Things (IoT) deployments impose stringent requirements on latency, energy efficiency, and reliability within edge computing environments. These systems typically operate under highly dynamic, bursty workloads, where rapid fluctuations in queue occupancy and resource utilization can lead to congestion, increased latency, and deadline violations. Conventional task offloading strategies, which rely on static heuristics or data-intensive machine learning models, often fail to achieve real-time adaptability, low computational overhead, and decentralized decision-making simultaneously. This study introduces an event-driven neuromorphic task offloading framework for edge-IoT systems utilizing Spiking Neural Networks (SNNs). The offloading problem is formulated as a multi-objective optimization process that jointly addresses latency, energy consumption, and deadline compliance. An SNN-based controller is integrated into edge nodes to approximate optimal decisions via spike-driven temporal dynamics, enabling adaptive, energy-aware orchestration without centralized optimization or continuous model inference. The proposed framework is evaluated using a hybrid simulation environment that integrates YAFS and Brian2. Theoretical analysis demonstrates bounded decision dynamics and sparse computational complexity. Experimental results indicate that the proposed approach reduces latency by up to 26
Cloud-assisted vehicular computing enables latency-sensitive applications by offloading tasks among vehicles, roadside units (RSUs), and remote clouds, yet it faces stringent challenges due to rapid topology changes, heterogeneous resources, and diverse task requirements. This paper presents a comprehensive study of task offloading and resource scheduling in a vehicle–RSU–cloud environment. We propose a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources. To capture realistic operational constraints, we develop joint models incorporating task attributes, security requirements, pricing factors, and heterogeneous computing/communication capabilities. Building on these models, we design an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions. Extensive simulation-based evaluation and comparative analysis demonstrate that the proposed approach enhances offloading efficiency and robustness, especially in scenarios with multiple scheduling targets, offering a practical basis for scalable vehicular edge–cloud collaboration.
