Microservices are widely adopted in large IT enterprises, leveraging the scalability, resiliency, and elasticity of the cloud-native architecture. Effective root cause analysis is crucial for ensuring the reliability of such cloud-native systems. Many efforts have focused on using the three modalities of observability data–traces, metrics, and logs. However, existing approaches are limited by inconsistent problem definitions and cloud-native heterogeneity. To address these challenges, we proposeHolisticRCA, a root cause analysis framework in cloud-native systems from a holistic perspective.HolisticRCAformally defines root cause analysis through three dimensions. ThenHolisticRCAuses an “assembling building blocks” strategy to address the cloud-native heterogeneity. It maps each observability feature into a shared vector space and concatenates the vector embeddings associated with each resource entity for standardized resource entity vector embeddings. Then it applies Graph Attention Network to capture intertwined resource entity relations and incorporates mask embeddings to enable holistic analysis. The evaluation results on three public datasets show thatHolisticRCAoutperforms existing approaches in holistic root cause analysis of cloud-native systems.

Modern large-scale systems and networks necessitate automated anomaly detection to support the high availability and quality of services. Since logs are an essential data source that can accurately reflect the state of a system, log anomaly detection has attracted a lot of attention from researchers in both academia and industry. As the technology of artificial intelligence advances, plenty of work has adopted deep learning to detect log anomalies and achieved promising results. Nevertheless, it usually suffers from a lack of labels, excessive log sequence length, and low throughput problems when deploying to real-world systems. To address these challenges, we propose Log-Fold, an unsupervised Transformer-based log anomaly detection approach. In LogFold, we propose fold embedding, which can compress long log sequences to enhance the efficiency of anomaly detection. And we design a sequence reconstruction technique to enhance the effectiveness of anomaly detection. Our evaluation shows LogFold achieves 90.55% and 99.90% Fl-score on HDFS and BGL datasets, respectively, outperforming state-of-the-art methods. Besides, the fold embedding layer achieves compression rates of 36.55% and 64.86% on HDFS and BGL datasets, respectively, which helps to improve the throughput of LogFold.

Facing the integrated enhancement of network and computing requirements from emerging high-computing-demand service such as XR and metaverse, Mobile Computing Force Network (MCFN) is proposed to satisfy this kind of service. MCFN is the network which achieves computing and mobile network convergence based on the perception, control, and management over computing resources. MCFN could have the global knowledge of network topology and service endpoint, which could provide the best network and service path to satisfy end-to-end requirements. The main objectives of this paper are introducing use cases, requirements of MCFN, and potential influence on 5G-Advanced and 6G network architecture as well as the key technologies to realize MCFN.

With the rapid revolution of digitalization in manufacturing, the advantage of having smart manufacturing has been quickly recognized by industry. Manufacturers are facing challenges of organizing the unprecedented integration of systems across manufacturing hierarchy, domain boundaries and life cycle phases in their smart manufacturing transformation. The far-reaching, maturity models providing the levels of maturity and the corresponding evaluation scheme will facilitate such smart manufacturing transformation. Numerous maturity models have been designed to assess the maturity with a set of criteria. However in business practice, maturity models are still not concrete for using in the smart manufacturing system implementation. This is because many of the maturity models are less care of interconnecting process steps in system implementation, which is an important aspect to reach out the sustainability of business.To make over the above problems in business practice, maturity models that identify the gaps and address appropriate solutions with process orientation is needed. In this paper, we propose a maturity model to measure the competency of system implementation and keep up the pace of processes that can employ in the actual factory automation environment. Kaizen(continuous improvement) subsumed under the term of process is highlighted in the proposed maturity model to match the sustainability strategic requirement in business practice.