Cloud computing technologies, including cloud-native and containerization, have gained prominence in recent years, attributed to their exceptional scalability, enhanced resource utilization, and expedited deployment capabilities. However, their inherent complexity and the intricate interplay of internal components heighten the risk of sporadic and unforeseen anomalies. To address these challenges, Root Cause Analysis (RCA) is employed to accurately identify problematic services (pods) and mine the precise faults behind observed anomalies. Tailored to the limitations of conventional RCA algorithms, we propose a novel approach that jointly models operation entities and their relationships as learnable embeddings. Additionally, this method integrates fault propagation information to further improve RCA accuracy. Our evaluation involves developing a prototype within the Kubernetes cloud-native system. Extensive experimental results validate the efficacy of our approach.

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.

Federated learning (FL) is a popular distributed machine learning paradigm which enables jointly training a global model without sharing clients' data. However, its repetitive server-client communication gives room for possible backdoor attacks which aims to mislead the global model into a targeted misprediction when a specific trigger pattern is presented. In response to such backdoor threats on federated learning, various defense measures have been proposed. In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning in a practical setting by proposing a new federated backdoor attack framework for possible countermeasures. Different from traditional training (on triggered data) and rescaling (the malicious client model) based backdoor injection, the proposed backdoor attack framework (1) directly modifies (a small proportion of) local model weights to inject the backdoor trigger via sign flips; (2) jointly optimize the trigger pattern with the client model, thus is more persistent and stealthy for circumventing existing defenses. In a case study, we examine the strength and weaknesses of several recent federated backdoor defenses from three major categories and provide suggestions to the practitioners when training federated models in practice.

High complexity of Dijkstra Algorithm limits its application in searching the shortest path in road network. Thus it is necessary to improve the efficiency of searching the shortest path in road network. Combining the existing conventional algorithms for restricted searching area, a shortest path algorithm based on cut-corner orientation rectangle is proposed. Based on orientation rectangle, the algorithm intends to cut corresponding corner areas so as to shrink the searching area. The proposed algorithm makes full use of the statistical parameter of urban road network to gain a reasonable value of the cut-angle. Theoretical calculation and experiment results are presented. All show that the proposed algorithm can apparently enhance the efficiency of searching the shortest path, which can reduce the time-complexity by 10-40% without influencing its reliability compared with existing conventional algorithms.

A framework for product named entity recognition in Chinese was presented using Conditional Random Fields with multiple features in this paper. It differentiates from most of the previous approaches mainly as follows. Firstly, introducing the domain ontology features to the CRFs model can use its semantic information. Secondly, combining internal and external features to compound features can use more rich overlapping features. so that it can improve the performance of product named entity Recognition. Experimental results show that this approach can achieve an overall F-measure around 87.16%, which seems to achieve the current state-of-the-art performance. However, due to the imperfect of Domain Ontology and the complication of reviews texts, the recognition for product named entity may not be better than the research of the traditional named entity recognition.