Recently, deep neural network (DNN) models work incredibly well, and edge computing has achieved great success in real-world scenarios, such as fault diagnosis for large-scale rotational machinery. However, DNN training takes a long time due to its complex calculation, which makes it difficult to optimize and retrain models. To address such an issue, this work proposes a novel fault diagnosis model by combining binarized DNNs (BDNNs) with improved random forests (RFs). First, a BDNN-based feature extraction method with binary weights and activations in a training process is designed to reduce the model runtime without losing the accuracy of feature extraction. Its generated features are used to train an RF-based fault classifier to relieve the information loss caused by binarization. Second, considering the possible classification accuracy reduction resulting from those very similar binarized features of two instances with different classes, we replace a Gini index with ReliefF as the attribute evaluation measure in training RFs to further enhance the separability of fault features extracted by BDNN and accordingly improve the fault identification accuracy. Third, an edge computing-based fault diagnosis mode is proposed to increase diagnostic efficiency, where our diagnosis model is deployed distributedly on a number of edge nodes close to the end rotational machines in distinct locations. Extensive experiments are conducted to validate the proposed method on the data sets from rolling element bearings, and the results demonstrate that, in almost all cases, its diagnostic accuracy is competitive to the state-of-the-art DNNs and even higher due to a form of regularization in some cases. Benefited from the relatively lower computing and storage requirements of BDNNs, it is easy to be deployed on edge nodes to realize real-time fault diagnosis concurrently.Note to Practitioners—Rotating machines, such as engines and motors, are the cornerstones of the modern industry. Edge computing is an emerging computing paradigm where computation is performed on the edges of networks rather than on the central cloud, thereby reducing system response time, transmission overhead, storage space, and computation resources of the cloud. Motivated by the high demand on computation for deploying DNN models and lower computation complexity for running BDNN models and easiness for large-scale deployment of BDNNs, an edge computing-based method for real-time fault diagnosis of rotating machines is proposed. First, we design a BDNN-based feature extractor to decrease the amount of computation and speed up a diagnosis processes. Then, the resulting binary features are fed to train an RF-based classifier, where we use ReliefF instead of Gini index when training a random forest model to further improve the proposed method’s diagnostic accuracy. Finally, a novel cloud-edge collaborative computing-based fault diagnostic mode is presented, where the model trained from the central cloud is deployed on the edge computing devices distributed in large-scale scenarios to realize real-time fault diagnosis. Experiment results show that the proposed method can maintain the desired accuracy but greatly enhance the diagnosis speed when deployed on the edge nodes near end physical machines. It is easily extended and used for fault detection in many industrial sectors.

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN’s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time.Note to Practitioners—Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method’s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows.

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. Multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Nondominated Sorting Genetic Algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.

Cloud computing is emerging as a deployment promising environment for hosting exponentially increasing scientific and social media applications, but how to manage and execute these applications efficiently depends mainly on workflow scheduling. However, scheduling workflows in the cloud is an NP-hard problem and its existing solutions have certain limitations when applied to real-world scenarios. In this paper, a Temporal Fusion Pointer network-based Reinforcement Learning algorithm for multi-objective workflow scheduling (TFP-RL) is proposed. Through adopting reinforcement learning, our algorithm can discover its heuristics over time by continuous learning according to the rewards resulting from good scheduling solutions. To make more comprehensive scheduling decisions as the influence of historical actions, a novel temporal fusion pointer network (TFP) is designed for the reinforcement learning agent, which can improve the quality of our resulting solutions and the ability of our algorithm in dealing with versatile workflow applications. To decrease convergence time, we train the proposed TFP-RL model independently by the Asynchronous Advantage Actor-Critic method and use its resulting model for scheduling workflows. Finally, under a multi-agent reinforcement learning framework, a Pareto dominance-oriented criterion for reasonable action selection is established for a multi-objective optimization scenario. We first train our TFP-RL model by taking randomly generated workflows as inputs to validate its effectiveness in scheduling, then compare our trained model with other existing scheduling approaches through practical compute- and data-intensive workflows. Experimental results demonstrate that our proposed algorithm outperforms the benchmarking ones in terms of different metrics.

In the actual industrial production processes, various faults occur at different frequencies and the resulting fault data may be class imbalanced. This means machine learning-driven fault diagnosis methods have to learn from imbalanced data, and accordingly lead to lower diagnostic accuracy or even directly errors in identifying minority class. To solve this problem, we present a novel Minority-class Sensitive Fault Diagnosis approach (MSFD), which can reduce the imbalance of data and enhance the sensitivity of our diagnostic model to minority-class samples. Specifically, we first design a new generative oversampling method by combining Wasserstein Generative Adversarial Network (WGAN) with Synthetic Minority Oversampling Technique (SMOTE) to balance the whole dataset and improve the distribution of the minority-class samples. WGAN is adopted to learn the distribution of minority-class samples and generate some minority-class samples as a supplement to the original dataset, while SMOTE is applied to the resulting dataset to further enhance the diversity of synthetic samples for weakening the influence from WGAN's mode collapse. In addition, a deep forest or multi-Grained Cascade Forest (GcForest) based minority-class aware fault classification model is developed. First, during multi-grained scanning processes, we score the forests and select the corresponding forests with higher scores to generate feature representations for accelerating model convergence. Second, weights are introduced for different forests in cascade levels to further improve the overall performance of our fault diagnostic model. A series of experiments are conducted to testify the effectiveness of our proposed method, and the experimental results show that our approach can synthesize new minority-class samples with higher qualities and improve the diagnosis performance for minority-class samples as well as its overall classification accuracy. Meanwhile, in case of extremely imbalanced datasets, the proposed approach still maintains a relatively high recognition rate for minority-class samples.

Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.

In order to reduce the social panic triggered by plane accidents, an effective approach is urgently needed to look for the missing passenger planes and then comfort the family of person who lost their lives in the accident. By analyzing the inherent features and motion law of the accident plane, this paper puts forward a differential dynamic model based method for locating the plane, which can effectively compensate the inherent deficiencies of radio position finding method, such as invalid locating. Firstly, this paper analyzes the instant conditions of the plane at its crashing moment and how these conditions are affected by the gravity and airflow resistance during its unpowered falling process, and as well as the plane falling trajectory. Then we establishes a dynamic model to determine the plane falling position and its corresponding search & rescue region. Finally simulation study and sensitivity analysis demonstrate that our proposed approach is effective and has good robustness. This research is very significant to reduce the rescue cost of air crash as well as improve the success rate of rescue.

Cloud manufacturing transforms scattered resources and capabilities supplied by entity enterprise into integrated manufacturing cloud services, which can be realized through composing different services. A complete candidate service set is the foundation of developing a composite service with higher quality. In order to improve QoS (Quality of Service) of the composite service, the correlations between services are considered and then classified in the whole composition process. However, considering the correlation in composition process but ignoring it during matching process, such as the existing methods about service correlation, will affect QoS of the composite service. In this paper, an extended service modelling is proposed to describe service correlations, and then a reservation algorithm is designed to reserve services with correlation in service matching stage so as to make the composite service much better than before. Simulation results demonstrate the effectiveness of our model and algorithm.

Recently, cloud computing has emerged as a new model of service provisioning, in this model, one of the most challenging problems is workflow scheduling, i.e., the problem of satisfying users' QoS while minimizing the execution cost of cloud workflow. This paper propose a workflow scheduling algorithm called Control Structure Reduction algorithm(CSR). In CSR, the workflows represented by DAG (Directed Acyclic Graph) can be converted into an equivalent sequence control structure by such means as mergers and reduction. Then we can easily identify the critical path of the workflow process. By using Time Float Distribution Algorithm, the total time float is allocated to each task based on critical tasks in critical path, eventually to enlarge the cost optimization intervals of all tasks. The simulation results show that CSR has a promising performance in decreasing the execution cost for large workflows.