With the rapid development of the Internet of Things (IoT), emerging microservice architectures support scalable and flexible deployment to handle a significant increase in latency-sensitive requests in resource-limited edge. Due to complex data dependencies and instance sharing among microservices, service deployment and request routing tightly coupled, which motivates a complex joint optimization problem. In this case, the difficulty of service orchestration is extremely enlarged when considering multi-instance modeling and fine-grained latency analysis for the strict quality of service (QoS). Furthermore, since the objectives of end-to-end response latency and energy consumption present an inherent conflicting relationship, carefully reconciling the trade-offs between them through efficient microservice orchestration is necessary, but significantly challenging. However, most previous work failed to propose suitable approaches and models to address the above difficulties. Therefore, this paper investigates the energy-efficient microservice orchestration for latency-sensitive applications in edge computing, aiming to minimize both latency and energy consumption. We first develop a multi-instance queuing network model that accurately captures data dependencies and enables detailed analysis of queuing, computing, and communication delays. Then, we propose the Energy-efficient Determination of Microservice Instances (EDMI) algorithm to derive the minimal required number of instances. Furthermore, we design a deep reinforcement learning-based orchestration method, Global-Reward Soft Actor-Critic (GRSAC), which integrates local reward signals into global feedback to mitigate sparse rewards. Finally, we provide theoretical analysis on time complexity and convergence. Experimental results show that our proposed algorithm significantly outperforms existing approaches in reducing both response latency and energy consumption.

Compound faults are common in modern intelligent manufacturing systems and pose severe risks to critical machinery. Accurate diagnosis is therefore essential but remains hindered by two challenges: the limited interpretability of deep learning models and the neglect of intrinsic dependencies among co-occurring faults in existing multi-label frameworks. To tackle these issues, we propose an interpretable time-frequency network that integrates physics-guided signal processing with deep learning. Specifically, a multi complex wavelet kernel convolution layer generates explainable time-frequency representations using five distinct wavelets, while a kernel weight module dynamically integrates them through adaptive weighting. Subsequently, a hybrid multi-scale convolution further enhances cross scale feature interactions, while a dynamic time-frequency fusion module adaptively balances complementary information. Furthermore, an asymmetric joint loss is introduced to explicitly establish label correlations and capture the joint probability of compound faults. The experimental results of two case studies highlight that the proposed method achieves superior diagnostic performance and significantly outperforms other advanced diagnostic methods.

Service meshes built upon ubiquitous microservice architectures, as an emerging paradigm, promise to enhance the flexibility, scalability, and portability of energy-consuming and latency-sensitive applications in edge with limited resources. However, due to intricate microservice dependencies, service multiplexing, and parallel distributed instances, microservice deployment and request routing are highly interdependent. To reduce response latency and energy consumption, such collaborative optimization for efficient service mesh orchestration is necessary, but significantly challenging. Besides, strict service level objective (SLO) requirements and f ine-grained latency analysis with multi-nest routing further impose great difficulties to online orchestration. When considering multi instance modeling and multi-hop data communications for numerous microservices, the difficulty is extremely amplified. Nevertheless, most prevailing work failed to design sophisticate models and methods for addressing the above difficulties, and ignored the inherent transmission energy consumption for highly-concurrent multi-hop data interactions. Therefore, this paper investigates the energy aware service mesh deployment and online request routing in edge. First, we establish a multi-instance queuing network model to accurately analyze the end-to-end response latency with complicated dependencies and multi-hop communications, and optimize energy consumption in a fine-grained manner. Then, to boost the overall performance, we design an efficient multi-dimensional hierarchical deep reinforcement learning algorithm, which enables edges and service instances to cooperate with each other to handle massively concurrent requests. Besides, we propose an energy-aware proactive autoscaling algorithm to carefully adapt to exceedingly dynamic scenarios. Finally, extensive experiments are performed to show our superior performance compared to other baselines.

The rapid development of service computing has led to the emergence of scalable and flexible architectures such as microservices and Artificial Intelligence as a Service (AaaS), enabling the orchestration of AI-driven intelligent applications. However, existing work on intelligent applications orchestration overlooked essential microservice components that support AI services, resulting in coarse-grained and incomplete models. To ensure system integrity and enhance QoS, fine-grained collaborative orchestration of microservices and AI services is crucial. However, this poses significant challenges due to complex service dependencies, high request concurrency, and heterogeneous resource demands in edge environments. Moreover, the strong coupling between service deployment and request routing complicates their joint optimization, since effective decisions in one depend on the other. To address these challenges, we propose a collaborative orchestration framework that jointly optimizes the deployment of microservices and AI services along with probabilistic request routing in edge environments. We formulate the problem as a mixed-integer nonlinear program and leverage Jackson queuing networks for accurate delay modeling. To solve this, we develop a dual time scale hybrid greedy proximal policy optimization (DTS-HGPPO) algorithm that performs instance-level deployment and adaptive routing, enhanced with iterative instance planning, action masking and intrinsic motivation mechanisms. Extensive trace-driven experiments demonstrate that our method significantly reduces both response delay and service cost compared to state-of-the-art baselines.

In recent years, there has been a surge focusing on advanced heart sound (HS) signal analysis and automated diagnosis based on deep learning (DL) for cardiovascular diseases (CVDs). However, the untrust of users in decision-making caused by the complex nonlinear transformation within the model and unclear feature extraction mechanism remains a huge challenge. For the diagnosis issue of CVDs involving human life and health, if the reasons why the model obtains the final conclusion cannot be known in advance, taking actions rashly will conceal significant risks. In this paper, an interpretable wavelet convolution transformer, named WCFormer, is proposed for HS signal analysis and automated diagnosis of CVDs. This method aims to enhance the interpretability of the traditional transformer and realize the high-accuracy diagnosis of CVDs by embedding wavelet knowledge information and improving its structure. Specifically, a wavelet convolution kernel is first designed to capture disease-related information with a clear physical meaning. Then, a global–local feature extractor is designed by removing the position encoding of the transformer and combining it with the convolution module. Two case studies involving HS signals are implemented to validate the efficacy of the proposed WCFormer and the results are compared with several widely used approaches, revealing that the WCFormer can achieve more excellent performance than other comparison methods.

Lightweight microservices as a software architecture have been widely adopted in online application development. However, in highly-concurrent microservice scenarios, frequent data communications, complex call dependencies, and dynamic delay requirements bring great challenges to efficient microservice orchestration. In this case, service deployment and request routing are interactively-coupled in multi-instance modeling, and cannot be locally optimized effectively, thereby enlarging the difficulty for collaborative orchestration. To accommodate time-varying request properties and dynamic microservice multiplexing, orchestration schemes are frequently adapted to real-time parallel request queues, further complicating the difficulty. Nevertheless, most previous work failed to propose appropriate models and methods for the above issues. Therefore, this paper investigates the online microservice orchestration with probabilistic routing for dynamic call graphs in clouds. First, we formulate the time-slot-based joint optimization problem as a Markov Decision Process. The open Jackson queuing networks are used to accurately establish multi-instance models and analyze the request queuing, processing, and communicating delays. Then, we propose an efficient curiosity-driven deep reinforcement learning algorithm, which meticulously implements instance-level orchestration through multi-dimensional collaborative decisions and multi-time-scale trigger events. Finally, through comprehensive trace-driven experiments, our proposed approach significantly outperforms other baselines in terms of orchestration cost and resource utilization.