Microservice is a popular architecture to construct applications from a set of small independent services in cloud environment, leading to high cohesion, high availability, low coupling, and decent scalability. Due to large number of independent services in a microservice system, system faults generated from a single service would propagate to multiple services, eventually degraded the overall system performance and Quality of Service (QoS). Thus, it is crucial to efficiently and autonomously diagnose the runtime system fault. However, the complexity and dynamism of microservice systems and cloud environment pose unique challenges to precisely and robustly identify the faults and localize the root causes. In this paper, we propose an Autonomous Model Selection-Ensemble-Stacking (AMSES) framework for microservice system fault identification. The proposed framework can automatically select, ensemble, and stack optimal models from candidate unsupervised detection models for identifying different fault types robustly. In addition, AMSES can adaptively localize the fault services using autoselected root cause localization model. Moreover, by exploiting the fault degree and causal inferring score, we can diagnose the detected system fault precisely and interpretably. To evaluate the effectiveness, we empirically compare AMSES with state-of-the-art models on three kinds of faults on two microservice benchmarks: Sock-Shop and Train-Ticket. The experimental results show that AMSES can achieve$\mathbf{8 7. 1 \%}$and$\mathbf{9 1. 4 \%}$macroF1 average for fault type identification on Sock-Shop and TrainTicket, respectively. Meanwhile, AMSES could outperform its competitors for root cause localization with an average Avg@5 of 0.856 on Sock-Shop and 0.633 on Train-Ticket.

The rapid evolution of vehicular edge computing (VEC) poses critical challenges in distributed caching resource management, particularly in reducing content retrieval latency and improving cache utilization efficiency. We propose a community-aware caching framework tailored for VEC scenarios, comprising two main components: a Dynamic Thematic-Community Clustering (DTCC) algorithm based on Collapsed Gibbs sampling and a Potential Game-based Caching Optimization (GCO) strategy. The DTCC algorithm captures the temporal evolution of vehicular social networks, facilitating dynamic community partitioning and topic distribution extraction. Meanwhile, GCO formulates the caching decisions of vehicles and base stations as a non-cooperative game, whose community-aware utility function design guarantees both the existence and convergence of a Nash equilibrium. Extensive experiments on real-world datasets demonstrate that GCO consistently outperforms state-of-the-art baselines across diverse performance metrics, further validating its efficacy compared with existing caching solutions.

Distributed anomaly detection for industrial IoT is crucial. Traditional detection methods suffer from degraded detection performance due to the data heterogeneity and high dynamics in the industrial IoT environment. We propose an Unsupervised Federated Hypernetwork Anomaly Identification Method for Industrial IoT Environment (uFedHyAI) to address these issues. Specifically, we introduce the federated hypernetwork architecture, which effectively mitigates data heterogeneity and volatility in industrial IoT environments while protecting industrial device data privacy. Then, we employ the Sequence Transformation Normalization Transformer (SC Nor-Transformer), which addresses the timing bias due to model aggregation through sequence transformation. At the same time, sequence normalization improves the timing dependency of captured subsequences. Finally, the subsequences’ temporal and frequency domain errors are combined to form the anomaly score. This novel anomaly score enhances the autocorrelation between subsequences and performs localization of anomaly sensors while achieving anomaly detection. We performed an extensive evaluation on six datasets, in which uFedHyAI outperformed the existing state-of-the-art baseline average F1 score by 6.59% and the average AUROC by 3.56%. Moreover, the average localization fault accuracy of uFedHyAI is 9.23% higher than that of the optimal baseline method.

As massive data is generated by Internet of Things (IoT) devices, user-end devices are required to implement computation-intensive functionalities, including multi-sensory data processing and analysis, sophisticated system control schemes, and artificial intelligence. Mobile Edge Computing (MEC) is a significant technology that has the potential to extend the computation and storage capacities of user-end devices by the decentralization of required resources and contents near users and at the edge. A crucial challenge in this direction is the development of a smart mechanism to effectively cache contents upon Edge Servers (ESs) near users for high effectiveness and low latency of content delivery with the constraints on computational and storage capacities of ESs. This study employs a queuing model for analyzing total request delay and interprets the content caching problem as an adversarial semi-bandits problem. We propose an Online Self-feedback Adversarial Semi-bandits Learning (OSAL) algorithm that incorporates a dual-layer learning architecture for dynamically generating caching strategies and maximizes the long-term reward. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods across various performance metrics in a real-world multi mobile-user content caching case.

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore.

Mobile Edge Computing (MEC) is an emerging computing paradigm that offloads cloud center functions to the edge server. In a MEC environment, edge servers' limited storage and processing capacity require selective service caching, where only a part of required content can be placed directly upon the destination edge server and the remaining at remote cloud end. A primary challenge in this context is the creation of an effective and responsive service caching algorithm that improves the Quality of Service (QoS) perceived by users while reducing operational costs. This study applies an$M$/ G /1 queuing model as the foundational framework and transforms the service caching problem as an adversarial semi-bandit problem. We propose a delay-aware Genetic-Follow-the-Regularized-Leader (GFRL) algorithm, which is capable of guiding decentralized caching decisions. Experimental results indicate that GFRL outperforms traditional methods across various performance metrics.

Fuzzy deduplication frees up more storage space than exact deduplication. Traditional deduplication schemes do not only fail to extend to fuzzy deduplication directly but also be vulnerable to brute-force guessing attacks (e.g. Convergent Encryption (CE) and Message-locked Encryption (MLE)). Currently, fuzzy deduplication is mainly handled with the help of aided-server and a third-party validator for the security and feasibility. This work extracts a single-server fuzzy deduplication scheme for encrypted multimedia data under dual-entity (e.g., uploader & server, and a third-party is not necessary). In addition, we perform experimental evaluation of DEFD on real-world datasets. The results show that DEFD can save 95+% of storage space, and for the real outsourced data deduplication scenarios DEFD can improve the accuracy by 5.287%.

To maintain the required service quality of time-critical cloud applications, operators must continuously monitor their runtime status, detect potential performance anomalies, and diagnose the root causes of these anomalies effectively. However, existing performance diagnosis methods face challenges such as the need for high-quality labeled data, the low reusability and robustness of performance anomaly detection models, and the absence of real-time fine-grained root cause localization. These challenges make fixing performance issues quickly and developing effective adaptation decisions difficult. We provide a Fine-grained Robust Performance Diagnosis (FIRED) framework to tackle those challenges. The framework offers a metrics selection component to filter noise and improve detection efficiency, an anomaly detection component that assembles several well-selected base models with a deep neural network, and adopts weakly supervised learning considering fewer labels exist in reality. The framework also employs a real-time, fine-grained root cause localization component to locate dependent resource metrics of performance anomalies. Our experiments show that the framework can effectively reduce data noise and achieve the best accuracy and algorithm robustness for performance anomaly detection. In addition, the framework can accurately localize the first root causes, with an average accuracy higher than 0.7 for locating the first four root cause metrics.

Vehicle Edge Computing (VEC) is the deployment of applications close to edge servers to provide low latency and highly responsive services to users. However, due to the complexity and dynamics of the VEC environment, it is prone to errors and failures, and the reliability of edge service migration may be compromised if no measures are taken to cope with different levels of failures. To address this issue, this paper proposes an modified (m, n)-fault tolerance strategy (M-MNFT). Unlike the traditional one, which only considers ES failures, M-MNFT additionally selects redundant edge base stations to ensure task reliability during task migration, and takes into account the fact that the relative distance between the request and the base station is as small as possible when the request is sent, so as to avoid the impact of the edge base station failure on the Quality of Service (QoS) during task migration. In addition, we have performed extensive simulations to show that M-MNFT outperforms existing methods in terms of the number of delayed requests, on-time finish rate, and average waiting time.

Cloud computing attracts increasing attention in processing dynamic computing tasks and automating the software development and operation pipeline. In many cases, the computing tasks have strict deadlines. The cloud resource manager (e.g., orchestrator) effectively manages the resources and provides tasks Quality of Service (QoS). Cloud task scheduling is tricky due to the dynamic nature of task workload and resource availability. Reinforcement Learning (RL) has attracted lots of research attention in scheduling. However, those RL-based approaches suffer from low scheduling performance robustness when the task workload and resource availability change, particularly when handling time-critical tasks. This paper focuses on both challenges of robustness and deadline guarantee among such RL, specifically Deep RL (DRL)-based scheduling approaches. We quantify the robustness measurements as the retraining time and investigate how to improve both robustness and deadline guarantee of DRL-based scheduling. We propose MLR-TC-DRLS, a practical, robust Meta Deep Reinforcement Learning-based scheduling solution to provide time-critical tasks deadline guarantee and fast adaptation under highly dynamic situations. We comprehensively evaluate MLR-TC-DRLS performance against RL-based and RL advanced variants-based scheduling approaches using real-world and synthetic data. The evaluations validate that our proposed approach improves the scheduling performance robustness of typical DRL variants scheduling approaches with 97%–98.5% deadline guarantees and 200%–500% faster adaptation.

Effectively localizing root causes of performance anomalies is crucial to enabling the rapid recovery and loss mitigation of microservice applications in the cloud. Depending on the granularity of the causes that can be localized, a service operator may take different actions, e.g., restarting or migrating services if only faulty services can be localized (namely, coarse-grained) or scaling resources if specific indicative metrics on the faulty service can be localized (namely, fine-grained). Prior research mainly focuses on coarse-grained faulty service localization, and there is now a growing interest in fine-grained root cause localization to identify faulty services and metrics. Causal inference (CI) based methods have gained popularity recently for root cause localization, but currently used CI methods have limitations, such as the linear causal relations assumption and strict data distribution requirements. To tackle these challenges, we propose a framework named CausalRCA to implement fine-grained, automated, and real-time root cause localization. The CausalRCA uses a gradient-based causal structure learning method to generate weighted causal graphs and a root cause inference method to localize root cause metrics. We conduct coarse- and fine-grained root cause localization to evaluate the localization performance of CausalRCA. Experimental results show that CausalRCA has significantly outperformed baseline methods in localization accuracy, e.g., the average AC@3 of the fine-grained root cause metric localization in the faulty service is 0.719, and the average increase is 10% compared with baseline methods. In addition, the average Avg@5 has improved by 9.43%. Codes and data are open-sourced and can be found in our Github repository CausalRCA.

Workflow offloading in the edge-to-cloud continuum copes with an extended calculation network among edge devices and cloud platforms. With the growing significance of edge and cloud technologies, workflow offloading among these environments has been investigated in recent years. However, the dynamics of offloading optimization objectives, i.e., latency, resource utilization rate, and energy consumption among the edge and cloud sides, have hardly been researched. Consequently, the Quality of Service(QoS) and offloading performance also experience uncertain deviation. In this work, we propose a multi-objective robust offloading algorithm to address this issue, dealing with dynamics and multi-objective optimization. The workflow request model in this work is modeled as Directed Acyclic Graph(DAG). An LSTM-based sequence-to-sequence neural network learns the offloading policy. We then conduct comprehensive implementations to validate the robustness of our algorithm. As a result, our algorithm achieves better offloading performance regarding each objective and faster adaptation to newly changed environments than fine-tuned typical single-objective RL-based offloading methods.

Abstract Quality of data services is crucial for operational large-scale internet-of-things (IoT) research data infrastructure, in particular when serving large amounts of distributed users. Effectively detecting runtime anomalies and diagnosing their root cause helps to defend against adversarial attacks, thereby essentially boosting system security and robustness of the IoT infrastructure services. However, conventional anomaly detection methods are inadequate when facing the dynamic complexities of these systems. In contrast, supervised machine learning methods are unable to exploit large amounts of data due to the unavailability of labeled data. This paper leverages popular GAN-based generative models and end-to-end one-class classification to improve unsupervised anomaly detection. A novel heterogeneous BiGAN-based anomaly detection model Heterogeneous Temporal Anomaly-reconstruction GAN (HTA-GAN) is proposed to make better use of a one-class classifier and a novel anomaly scoring function. The Generator-Encoder-Discriminator BiGAN structure can lead to practical anomaly score computation and temporal feature capturing. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. The results show that HTA-GAN outperforms its competitors and demonstrates better robustness.

Financial time series prediction and trading decision-making are priorities of computational intelligence for researchers in academia and the finance industry due to their broad application areas and substantial impact. However, these methods remain challenging because they retain various complex statistical properties, and the mechanism behind the processes is unknown to a large extent. A significant number of machine learning-based methods are proposed and demonstrate impressive results, especially deep learning-based models. Nevertheless, due to the high complexity of massive, nonlinear, and nonindependent data and the difficulties and time consumption of complicated training models of deep learning, the performance of online trading decisions is still inadequate for practical application. This paper proposes the Integrated Framework of Forecasting Based Online Trading Strategy (IFF-BOTS) to satisfy better prediction performance and dynamic decisions for real-world online trading systems. Our method adopts a novel isomorphic convolutional neural network (CNN)-based forecaster-classifier-executor architecture to exploit CNN-based price and trend integrated prediction and direct-reinforcement-learning-based trading decision-making. IFF-BOTS can also achieve better real-time performance for online trading. We empirically compare the proposed approach with state-of-the-art prediction and trading methods on real-world S&P and DJI datasets. The results show that the IFF-BOTS outperforms its competitors in predicting metrics, trading profits, and real-time performance.

Container clusters play an increasingly important role in cloud computing for processing dynamic computing tasks. The resource manager (i.e., orchestrater) of the cluster automates the scheduling of the dynamic requests, effectively manages the resources' utilization across distributing infrastructure resources. For many applications, the requests to the cluster are often with restricted deadlines. The scheduling of container clusters is often tricky, especially when the cluster's size is large and the load of the requests is dynamically changing. Machine learning-based approaches such as reinforcement learning have attracted lots of research attention during the past years; However, those approaches suffer from low robustness when the requests in an operational environment are changing and different from the training data sets. This paper investigates this problem by quantifying the robustness and proposing meta-gradient reinforcement learning to improve the robustness of classical reinforcement learning-based approaches. The proposed approach can lead to better deadline guarantees and faster adaptation for time-critical task scheduling under dynamic environments. We then empirically test the benefits of our method using both real-world and synthetic data sets. The evaluation results show that the proposed method outperforms the compared RL methods in scheduling performance and robustness.

The mobile edge computing (MEC) paradigm places traditional digital infrastructure next to mobile networks and thus drives substantial improvements in performance and latency for mobile computing cases like gaming, video streaming, and IoT. However, it remains a great challenge to provide a effictive and performance guaranteed strategies for services offloading and migration in the MEC environment. Most existing solutions in this direction tend to consider task offloading as a offline decision making process by employing transient positions of users as model inputs. In this work instead, we consider a predictive-trajectory-aware task offloading strategy called PreMig. Simulations clearly demonstrate that our proposed strategy outperforms traditional ones in terms of effective service rate and migration overhead.

Recently, the vehicular edge computing (VEC) paradigm becoming an emerging solution for offloading computation-intensive tasks in the vehicular environment. However, pure edge resources can be limited and insufficient when vehicles and users are in great numbers. Thus, intelligent and efficient task deployment strategies for hybrid and layered edge infrastructures are in high need. In this paper, we propose a novel deployment approach for vehicular applications with multiple interdependent tasks in a hybrid three-layer edge computing infrastructure. We consider that each application can be divided into multiple interdependent tasks, and tasks can be deployed to different layers for execution. We propose an efficient multiple tasks deploying algorithm (MTDA) for yielding high-quality deployment solutions through prioritizing applications for meeting deadline constraints and tasks for meeting dependency constraints and simulative results clearly demonstrate that our proposed method outperforms traditional ones in terms of average application completion time and deadline meeting rate.

Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

The rapid development of mobile communication technologies prompts the emergence of mobile edge computing (MEC). As the key technology toward 5th generation (5G) wireless networks, it allows mobile users to offload their computational tasks to nearby servers deployed in base stations to alleviate the shortage of mobile resource. Nevertheless, various challenges, especially the edge-user-allocation problem, are yet to be properly addressed. Traditional studies consider this problem as a static global optimization problem where user positions are considered to be time-invariant and user-mobility-related information is not fully exploited. In reality, however, edge users are usually with high mobility and time-varying positions, which usually result in users reallocations among different base stations and impact on user-perceived quality-of-service (QoS). To overcome the above limitations, we consider the edge user allocation problem as an online decision-making and evolvable process and develop a mobility-aware and migration-enabled approach, named MobMig, for allocating users at real-time. Experiments based on real-world MEC dataset clearly demonstrate that our approach achieves higher user coverage rate and lower reallocations than traditional ones.

In an underdetermined mixture system with unknown sources, it is a challenging task to separate these sources from their observed mixture signals, where . By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed mixture signals. We show that these 1-D subspaces are associated with TF points where only single source possesses dominant energy. By grouping the vectors in these subspaces via hierarchical clustering algorithm, we obtain the estimation of the mixing matrix. Finally, the source signals could be recovered by solving a series of least squares problems. Since the sparse coding strategy considers the linear representation relations among all the TF representation vectors of mixing signals, the proposed algorithm can provide an accurate estimation of the mixing matrix and is robust to the noises compared with the existing underdetermined blind source separation approaches. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.

Summary Analyzing the quantitative performance plays an important role in understanding and improving the quality of cloud computing systems and cloud‐based applications. In cloud computing, service requests from users go through numerous provider‐specific steps from the instant it is submitted to when the requested service is fully delivered. Quantitative performance analysis is not an easy task because of the complexity of cloud provisioning control flows and the increasing scale and complexity of real‐world cloud infrastructures. This work proposes a probabilistic queuing network‐based model for the performance analysis of cloud infrastructures. It considers expected task completion time and rejection probability as the performance metrics. Experimental performance data suggest the correctness of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.