Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
With the pervasive adoption of cloud computing, modern software systems have evolved toward highly distributed, elastic, and microservice-based architectures, which significantly increase the difficulty of effective fault localization. Spectrum- Based Fault Localization (SBFL) techniques are widely used to assist automated program repair and manual debugging; however, their practical effectiveness remains limited. Specifically, existing SBFL methods largely overlook the static semantic characteristics of program statements and fail to fully exploit the abundant execution data and scalable computational resources available in cloud computing environments. To address these limitations, this paper proposes a lightweight fault localization approach based on learning to rank, explicitly designed for cloud computing scenarios. The proposed method employs a linear ranking Support Vector Machine (SVM) that jointly integrates traditional SBFL suspiciousness scores with static statement-level features, including variables, operators, and statement categories, to construct a more discriminative fault localization model. Furthermore, to better leverage resource coordination and large-scale data processing capabilities in cloud environments, a cross-project training strategy is adopted, and distributed cloud resources are utilized to enable efficient model training and validation. The proposed approach is evaluated on large-scale datasets comprising 19 Java, 19 C, and 2 C++ projects. Experimental results demonstrate that, under the EXAM metric with the worst-case evaluation strategy, the proposed method reduces the number of statements requiring inspection by 26.1% compared to the best-performing SBFL technique. These findings indicate that integrating static program features with cloud-enabled learning and resource coordination can substantially improve fault localization effectiveness in complex cloud-based software systems....
Although the development of cloud computing has led to a paradigm shift in the data storage, it has also subjected the sensitive information to massive security and performance threats; that is, the data integrity and processing overhead. There is a basic trade-off in classical cryptography: the symmetrickey protocols are really fast, whereas they do not provide secure exchange of keys, but the asymmetrickey protocols are very secure and computationally infeasible when dealing with large amounts of data. Conventional cryptographic techniques commonly have a fundamental dilemma: symmetric-key protocols can offer fast execution but do not include any secure key-exchange protocols, whereas asymmetric-key systems can offer high protection but cannot be used with large volumes of data due to their slow computation speed. To alleviate these problems, this study presents a streamlined dualcipher architecture that is aimed at boosting the efficiency of the cloud through the integration of the AES-256 and RSA-2048. The intelligence of the system lies in a dynamic workload distribution mechanism such that the high-resource encapsulation of the RSA operations is limited to the encryption of keys, while the bulk encryption is left to the high-speed AES engine. The empirical assessment of different file sizes (1 MB to 1 GB) shows that a sustainable throughput of 56-72 MB/s is achieved at 83-86 percent of the functional speed of standalone AES. As a result, the suggested model provides a viable and scalable design for modern cloud infrastructures with a very low overhead of just 20-21 percent or so relative to single deployments of RSA....
This paper presents the design and performance evaluation of a scalable microservice-based cloud computing framework for distributed stochastic fluid flow simulations. The system integrates Docker containerization, NGINX load balancing, and Apache Kafka for asynchronous task coordination and efficient workload distribution across computational nodes. The backend combines PHP and Julia for orchestration and computation, with PostgreSQL managing task and result data. Benchmark experiments demonstrate near-linear scalability and stable performance under varying loads, confirming the system’s suitability for high-performance scientific computing. The proposed framework advances parallel and distributed computing by introducing a modular, reproducible architecture for executing complex stochastic models in cloud environments....
Efficient workflow scheduling in cloud–edge environments is severely bottlenecked by long-horizon dependencies and myopic resource fragmentation. This paper proposes the Decoupled Offline Sequence-based (DOS) scheduling framework to address these challenges. By decoupling expert policy learning from runtime deployment, DOS utilizes a multi-dimensional priority-aware linearization strategy to deterministically transform DAG-structured workflows into dependency-consistent sequences. Leveraging offline expert trajectories, we train UDC, a Gated CNN achieving unified sequence modeling via innovative triplet-to-unary encoding, equipped with explicit action masking to distill longhorizon spatio-temporal packing patterns. This mechanism enables rapid feed-forward inference without costly online environment interactions or policy updates. Extensive evaluations on real-world Alibaba cluster workloads demonstrate that DOS not only consistently minimizes average makespan compared to classical heuristics, but also drastically reduces resource-blocked steps under extreme concurrency versus online Actor–Critic experts. Crucially, compared to the Decision Transformer (DT) baseline, the UDC model achieves strictly scale-invariant and significantly lower inference latency, highlighting its robust scalability and practicality for large-scale continuum systems....
We researched, analyzed and predicted building energy consumption data using cloud computing and constructed an intelligent model. A local outlier factor outlier discovery algorithm was created to monitor abnormal energy consumption. A random forest algorithm was used for high-dimensional data to predict building energy consumption and analyze data in the Commercial Building Energy Consumption Survey database. The degree of importance of independent variables was evaluated to analyze how the architectural attributes of office buildings affect energy consumption....
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