Semantic image segmentation typically relies on static pixel- wise classifiers that operate over a fixed category space, making them insensitive to the actual semantic composition of a real-world image. In this work, we propose CaSS, a category-aware semantic segmentation framework that introduces image-level semantic priors to dynamically adapt pixel-level classification. Specifically, a pre-trained vision–language model is employed to infer the set of semantic categories present in an image, which is encoded as a structured category prior. This prior is then used to drive a lightweight dynamic parsing network that generates image-conditioned classifier parameters for pixel- wise segmentation. By explicitly constraining the classifier with category-aware priors, CaSS reduces interference from absent classes and enhances both intra-class consistency and inter-class discriminability. The proposed approach follows a large–small model collaboration paradigm, leveraging the strong semantic understanding of vision–language models while preserving efficient pixel-level inference. Extensive experiments on standard semantic segmentation benchmarks demonstrate that CaSS consistently improves segmentation accuracy over state-of-the-art methods with minimal parameter overhead.

In resource-constrained edge computing, the execution efficiency of workflow applications is significantly affected by bandwidth contention, especially during data transmissions between dependent tasks. However, existing workflow scheduling studies often struggle to optimize transmission delay effectively, whereas bandwidth slicing offers promising potential by leveraging the dynamic nature of bandwidth resources. To address this issue, we propose Workflow Scheduling with Dynamic Bandwidth Slicing (WSDBS), a novel scheduling algorithm that integrates bandwidth slicing into the workflow execution process. By introducing a dual-prediction strategy, WSDBS estimates the availability of both computational and bandwidth resources on servers, facilitating efficient task scheduling decisions under transmission uncertainty. Moreover, a novel transmission urgency metric is developed, which is derived from both link load and transmission criticality. This metric guides bandwidth slicing for the dynamic allocation of server-side bandwidth resources, ultimately alleviating contention among concurrent transmissions. Extensive experiments based on real-world Alibaba cluster traces show that WSDBS consistently improves scheduling efficiency, reducing the average makespan by 10.87%-14.44% over state-of-the-art baselines. These results validate its effectiveness in alleviating bandwidth contention and improving scheduling performance in edge computing environments.

In edge computing, workflow applications are optimally scheduled onto edge servers that are pre-equipped with the necessary services to satisfy stringent low-latency demands. However, prior research has not fully addressed the joint optimization of service placement and workflow scheduling, particularly the exploitation of task parallelism to reduce overall makespan. To address this shortcoming, we explore the combined workflow scheduling and service placement (WSP-SP) problem with the goal of minimizing the average makespan of applications. Recognizing that WSP-SP is NP-hard, we propose a two-stage, Parallelism Aware Workflow Scheduling and Service Placement strategy (PAWSSP) that minimizes the makespan with low complexity. In the first stage, a Parallelism Aware Service Placement module (PASP) is designed to adjust the service layout by allocating services with high parallelism onto distinct servers to fully leverage task-level concurrency. In the subsequent workflow scheduling stage, PAWSSP determines task priority by resolving inter-task competition and further reduces waiting times by assigning tasks to servers experiencing lower resource contention. We further extend PAWSSP to make it applicable to both offline and online scenarios. Extensive evaluations demonstrate that PAWSSP performs robustly across diverse scenarios, reducing the average makespan by 3.9%-14.7% compared to existing baselines, while maintaining modest runtime overhead.

Cloud-Fog computing has emerged as an essential paradigm to support the growing demand for real-time data processing driven by the Internet of Things. By integrating the extensive computing capabilities of cloud data centres with the low-latency benefits of fog nodes, this architecture increases resource utilisation and improves quality of service. However, the dynamic and heterogeneous nature of cloud fog environments poses significant workflow scheduling challenges, especially when optimising multiple trade-offs such as latency, cost, energy consumption, and resource utilisation. This paper investigates the many-objective dynamic workflow scheduling problem under deadline constraints in container-based cloud-fog computing environments (MDWS-CoCF). Unlike existing studies that primarily focus on horizontal scaling, this work considers both vertical and horizontal scaling of containers, allowing for real-time adjustments of container configurations based on task-specific requirements. To address this complex problem, we first develop a dynamic workflow scheduling simulator that models real-world scenarios, including a variety of task categories and container scalability. Based on this simulator, we propose a Cooperative Coevolution Genetic Programming (CCGP) approach that evolves specialised heuristics for task selection, resource allocation, and container deployment to facilitate adaptive and efficient scheduling in MDWS-CoCF. Extensive simulations using real-world data traces show that the proposed CCGP approach significantly outperforms existing baseline algorithms, achieving superior performance as measured by the HyperVolume and Inverted Generational Distance metrics. The results show that the evolved heuristics are robust and effective under different dynamic scenarios, ensuring balanced optimisation of many objectives.

The multicloud environments (MCE) represent a novel paradigm encompassing multiple infrastructure as a service (IaaS) providers, enabling users to tailor and optimize cloud services according to their specific requirements. This approach effectively addresses the limitations of a single cloud environment (SCE) regarding technical constraints, geographical coverage deficiencies, and cost-effectiveness concerns while catering to the increasingly diverse and expanding user demands. In MCE, users must employ appropriate strategies to efficiently allocate diverse tasks across multiple cloud service providers (CSPs) by leveraging the best available resources. Traditional scheduling algorithms are inadequate for addressing the complexities of such MCE. This study introduces a framework for the task scheduling procedure in MCE, treating independent task scheduling as a Markov decision process (MDP). We propose a novel agent environment framework that is designed based on the distinctive characteristics of MCE and enables independent task scheduling. Furthermore, we propose a task scheduling algorithm for MCE based on deep reinforcement learning (DRL) to optimize cost and makespan according to diverse user requirements. The simulation experiments are conducted using both simulated datasets and real-world datasets, demonstrating that our proposed algorithm surpasses the other five algorithms in terms of cost minimization and makespan optimization.

Data stream classification is widely used in Internet of Things (IoT) scenarios such as health monitoring, anomaly detection and online diagnosis. Due to the continuous data stream changing dynamically over time, it is impossible to classify all the data simultaneously. Moreover, labeling each sample in practical data stream applications is time-and resource-consuming. The realistic situation is that only a few instances in a data stream are labeled. Therefore, classifying data streams with limited labels has become challenging in IoT scenarios. In this paper, we propose an incremental dynamic weighted semi-supervised method for classifying IoT data streams. Considering the dynamics and continuity in data streams, we use a chunk-based approach to learn the features in the data stream and assign weights to the classifier dynamically. Moreover, we deploy incremental learning methods to continuously learn from the sampled labeled data stream to update the classifier model, which can take advantage of newly incoming labeled data to improve learning performance. Experimental evaluations on seven IoT datasets show that the proposed method outperforms semi-supervised methods in accuracy, precision, and geometric mean (Gmean) by 10% and 5% over supervised methods, respectively.

Recently, the multi-cloud environment (MCE) has increasingly become the preferred choice of users. As with the cloud environment, efficient workflow scheduling in a MCE remains crucial for identifying the cost efficiency and overall performance of the MCE. In MCE, the resources exhibit heterogeneity, complexity, and dynamism. Simultaneously, the intricate inter-task dependencies among workflow tasks, diverse Quality of Service (QoS) metrics for users, and multiple cloud service providers’ (CSPs) billing mechanisms significantly amplify the workflow scheduling challenge. Motivated by the application of reinforcement learning (RL) in workflow scheduling in a cloud environment, this paper proposes a scheduling algorithm that takes advantage of the asynchronous advantage actor–critic algorithm (A3C) to balance cost, makespan and resource utilization in workflow scheduling in a MCE. By analyzing the elements in the MCE, we design and define multiple agents in the MCE, and each cloud service provider will have an agent to record the state and update the local parameters. For the workflow task submitted by the user, the action is selected according to the initialization policy and submitted to the scheduling action to allocate the task to a designated virtual machine in the MCE so that each agent can more clearly perceive the environment change and adapt to the MCE. In contrast to the traditional A3C algorithm, we design a new critic network according to the data characteristics of real-world scientific workflows so that each agent is more suitable for real-world scientific workflow data. Through multiple sets of simulation experiments, the workflow scheduling algorithm based on the A3C algorithm in the MCE (MCWS-A3C) was compared with three benchmark methods. The experimental results show that the proposed method has better advantages than other methods in terms of cost, makespan, and resource utilization . Specifically, on the Montage_100 dataset, the average cost was reduced by 55.12% compared to other methods. The pioneering introduction of the A3C algorithm that adapts to the dynamic environment into the MCE brings more possibilities to address the issue of workflow scheduling in the MCE.

Anomaly detection is indispensable for achieving higher availability and reliability in the cloud computing. The traditional autoencoder-based method only models the historical normal samples and then identifies the current online anomaly samples by the fixed threshold of anomaly score. Although more advances have been made in recent years, two main challenges remain: (i) ignoring the historical anomaly samples, (ii) poor self-adaptive ability for online detection. To address the above challenges, we propose a unified detector, namely BroadCAE, which integrates autoencoder with contrastive learning and broad network. Specifically, the reconstruction loss is first replaced by contrastive loss, which equally formulates both normal and anomaly samples. These samples belonging to the same class become closer in a lower-dimensional space. Conversely, different classes of samples are far away from each other. Next, we apply the anomaly-score-based pseudo thresholds to train the dynamic threshold selection, which generates the threshold according to the coming sample. The broad network in dynamic threshold selection takes the place of the deep network, which overcomes catastrophic forgetting and adapts to new online samples. Finally, validation experiments are conducted on four benchmark datasets. Our BroadCAE outperforms the comparative baseline methods by averaging over 4% of the f1-score.