The deployment of billions of Internet of Things (IoT) devices is driving unprecedented data generation at the network edge, demanding high computational power for real-time deep learning (DL) while raising serious concerns about energy consumption. While edge computing offers a viable paradigm for decentralized DL by preserving data privacy and reducing latency, the substantial energy costs of DL training and inference pose a major challenge for resource-constrained edge devices. This work provides a comprehensive review of state-of-the-art studies that address energy efficiency at the intersection of DL and edge computing. Moving beyond isolated solutions, we analyze the critical need for a co-design approach integrating hardware and software with adaptive resource management to build sustainable systems. The paper systematically examines hardware-level optimizations and software-level techniques for reducing energy consumption while maintaining model accuracy. Furthermore, it investigates how adaptive management of compute, memory, and communication resources is key to dynamic energy savings. Finally, the paper synthesizes recent trends, identifies emerging opportunities, and discusses open challenges, positioning hardware-software co-design as the most promising approach for achieving scalable and energy-efficient deep learning in edge computing.

Time series forecasting techniques have significant value in domains such as industrial production, financial markets, and energy management. Accurate prediction of future time series is vital for decision making and operational optimization. However, existing methods often face two major difficulties, i.e., insufficient available training data and the challenging requirement for long-term predictions, where errors tend to accumulate over time. These limitations highlight the need for more reliable long-term forecasting methods. This work proposes a novel hybrid approach that combines VMD with attention-based transformer-style models to address these challenges. The VMD module decomposes raw sequences into simpler and more stable elements, reducing noise and irregular patterns. The transformer-style model captures long-range dependencies through its attention mechanism to identify meaningful long-term relationships in data. The proposed method is evaluated on multiple benchmark datasets, including ETT, ECL, and Traffic, which represent different real-world scenarios. The experimental result shows that the proposed method reaches higher prediction accuracy compared to existing methods across all datasets and prediction horizons, especially in long-term forecasting scenarios.

Time series forecasting is a fundamental task in many domains, such as finance, energy, and intelligent systems. It is increasingly important in modern computing environments, including cloud computing and distributed resource management. However, real-world time series often exhibit complex temporal dependencies, high volatility, and multi-scale nonlinear patterns, making accurate forecasting challenging. To address these issues, this work proposes SVBTformer, a novel and effective forecasting model that enhances the Transformer-based Informer architecture with structured temporal learning modules. Specifically, SVBTformer integrates Savitzky–Golay (SG) filtering for noise reduction and signal smoothing, followed by Variational Mode Decomposition (VMD) to extract multi-resolution temporal components. Then, an improved Informer network called BTformer is employed to enhance the modeling capability for time series and strengthen the extraction of temporal dependencies. This work extensively experiments on publicly available benchmarks spanning multiple domains, including the ETT dataset for electric power demand, foreign exchange rates, and meteorological measurements. The results demonstrate that SVBTformer consistently outperforms state-of-the-art models, such as Informer and Autoformer, across most evaluation metrics, delivering superior accuracy and robustness. These gains underscore SVBTformer’s strong generalization capability and suitability for deployment in various real-world time series applications.

With the rapid advancement and integration of Internet of Things technology into manufacturing, industrial workshops in computer, communication, and consumer electronics (3C) manufacturing are increasingly confronted with complex computational tasks during production. However, the limited hardware resources and computational capabilities of local devices often hinder efficient task execution. Computational offloading offers a viable solution by allowing complex computational tasks to be processed on either edge or cloud servers, enhancing the efficiency of computational task handling in production environments. A critical challenge lies in optimizing task offloading among local devices, edge servers, and cloud servers to maximize production efficiency while ensuring reasonable task scheduling. To address this challenge, this work proposes a flexible computational offloading strategy based on an edge-cloud architecture in a smartphone manufacturing workshop. First, a framework for edge-cloud workshop manufacturing is constructed, integrating various smartphone production devices. Based on the edge-cloud framework, a constrained optimization problem for computation offloading is formulated, using latency as the objective in industrial production settings. A time consumption model is employed to optimize computational time, and a novel scheduling strategy named Ivy-Genetic Evolution Algorithm (IGEA) is designed to solve the scheduling problem. The IGEA integrates genetic operators into the Ivy Algorithm to introduce a randomness strategy. Experimental results demonstrate that IGEA significantly outperforms state-of-the-art approaches in optimizing production efficiency.

Medical image segmentation challenges stem from complex lesion morphologies and real-time clinical needs. Here we present MAR-Net, a novel framework that integrates adaptive attention mechanisms, hierarchical contextual modeling, and efficient training strategies to address these challenges. The architecture employs a CBAM-based dual-attention module to dynamically enhance discriminative features while suppressing redundant information, improving lesion boundary localization. Cascaded dilated convolutions expand the receptive field for global context capture, complemented by a multi-scale decoder that integrates deep semantic and shallow spatial features. A multi-scale training strategy with hierarchical loss supervision optimizes model adaptability without compromising inference efficiency. Experimental validation on ISIC and CholecSeg8K datasets demonstrates MAR-Net’s superiority: it outperforms mainstream methods across segmentation accuracy, recall rate, and other metrics, achieving notable improvements for complex lesions of varying sizes. Notably, MAR-Net maintains high performance on both dermoscopic and laparoscopic images, showcasing its broad applicability. These results confirm MAR-Net as a robust medical segmentation solution, balancing precision and efficiency for clinical use.

The Dynamic Job Shop Scheduling Problem (DJSP), a critical challenge in 3C manufacturing, requires efficient resource allocation under dynamically changing production conditions where jobs arrive unpredictably. Traditional optimization methods struggle to provide scalable solutions due to the high computational cost of searching for optimal schedules in large and complex environments. To solve this problem, this work proposes the Dual-Graph convolutional networks assisted Dynamic Cooperative Hunting Optimizer (DG-DCHO), which integrates graph-convolutional networks (GCN) with metaheuristic optimization to generate high-quality schedules while significantly improving computational efficiency. GCN generator processes graph representations of the job-shop environment and captures complex dependencies among jobs and machines to construct high-quality initial schedules that serve as initial solutions for the optimization process. GCN evaluator estimates makespan values directly from schedule representations and replaces costly fitness evaluation that minimizes computational overhead and improves optimization speed. Dynamic Cooperative Hunting Optimizer (DCHO) serves as the base optimizer and generates scheduling solutions by balancing global exploration with local exploitation through an adaptive search strategy. Experimental results across various DJSP instances demonstrate that DG-DCHO consistently outperforms state-of-the-art scheduling algorithms by producing superior solutions while requiring fewer computational resources, making itself a scalable and effective framework for real-time dynamic scheduling in large-scale 3C manufacturing systems.

Neurological dysfunction encompasses a variety of diseases resulting from neural damage. Accurate assessment of neurological function is critical for diagnosis and the development of effective treatment plans. A significant number of patients with neurological disorders exhibit ocular abnormalities. Analyzing ocular status through eye movement capture plays a pivotal role in understanding various neurological dysfunctions. However, current methods of analyzing ocular status for neurological function assessments lack precision and objectivity, often relying heavily on physicians’ subjective judgment. This work proposes the Ocular-enhanced Face Keypoints Network (OFKNet), a facial keypoint detection model based on deep convolutional neural networks. OFKNet employs ConvNeXt as its backbone network and introduces a multi-scale input enhancement strategy. Additionally, a region enhancement module based on MobileNetV3 is designed to optimize features in the canthus area. Multiscale feature fusion and channel weighting are achieved through an improved Path Aggregation Network and Squeeze-and-Excitation modules. To validate OFKNet’s accuracy, we compared it with state-of-the-art models, including MediaPipe FaceLandmarker, InsightFace, Dlib68, and Dlib81, using a patient dataset we collected. Experimental results demonstrate that OFKNet outperforms existing models, particularly in calibration accuracy around the eyes. By monitoring eye movements in real-time, OFKNet ensures high-precision extraction of key points in each frame, accurately reflecting changes in patients’ ocular movements.

Water quality anomaly detection refers to the identification of abnormal changes in water parameters, which is crucial for ensuring environmental safety and preventing contamination events. With the growing volume of water environment sensing data and increasing demand for intelligent, transparent water quality management systems, achieving accurate, rapid, and interpretable anomaly detection has become a critical challenge in early warning systems. To tackle this challenge, this work proposes an anomaly detection model named Shapelet Temporal Evolution Graph Network (STEG), which constructs time-aware Shapelets and adopts graph attention networks to build Shapelets evolution graphs, learning multidimensional dynamic relationships within and between time segments. By incorporating both local and global temporal evolution factors, the approach ensures the interpretability of both the detection process and its resulting outputs. Experiments on two real-world datasets show that STEG outperforms state-of-the-art methods in terms of anomaly detection accuracy and generalization. Moreover, it provides clear and transparent reasoning for water quality anomaly detection.

Accurate estimated time of arrival (ETA) prediction for long vehicular trips remains challenging in intelligent transportation systems (ITS) due to heterogeneous traffic patterns and limited local data availability. While federated learning (FL) addresses privacy concerns by decentralizing data training, traditional FL frameworks often struggle with high computational costs and poor adaptability to multi-task scenarios. To overcome these limitations, this paper proposes a Lightweight Multi-task Federated Learning (LMFL) framework for efficient and privacy-preserving ETA prediction. LMFL integrates a novel SE-CIFG, combining a Squeeze-Excitation (SE) attention module to prioritize critical spatio-temporal features and a Coupled Input and Forget Gate (CIFG) to simplify long-term traffic dependency modeling. Additionally, LMFL employs a Federated Gradient Compression Algorithm (FedGCA) to reduce communication overhead between edge and cloud using adaptive thresholding and sparse tensor encoding. Real-world traffic simulation dataset demonstrates that LMFL achieves significantly higher predictive accuracy compared to existing methods, achieving an average 17.1% improvement in prediction precision while reducing training time by 4.1%.

Water quality prediction methods forecast the short- or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality’s nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates convNeXt V2, multimodal bottleneck transformer, low-rank multimodal fusion, iTransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy.

Given the forthcoming emergence of 6G communication models, the integration of terrestrial and nonterrestrial infrastructures is receiving increasing attention due to its widespread reach and broadcasting/multicast functions. The utilization of edge computing in space-related applications is appealing. However, the issue of positioning satellite edge servers and deploying services has yet to be resolved. Besides, existing studies mainly concentrate on energy consumption and latency problems, often neglecting the user mobility and potential privacy leakage issues in a mobile edge computing (MEC) environment. Yet it is crucial to optimize computation offloading and resource allocation for satellite-terrestrial edge computing networks. This work designs an innovative architecture for collaborative computation among multiple mobile devices and MEC servers deployed in ground stations and satellites. Based on this architecture, we formulate a nonlinear integer optimization problem to minimize the total system energy consumption. The model integrates several complex real-life nonlinear constraints, including operator cost, edge servers’ computing capacity, storage capacity, resource and latency, and privacy ones. To tackle the problem, this work proposes an advanced hybrid algorithm named a slime mold algorithm with genetic operations and individual updates of grey wolf optimizer (SMG2). SMG2 optimizes user mobility and privacy protection while optimizing server and service placement to minimize total energy consumption. Simulation experiments demonstrate that SMG2 reduces energy consumption drastically over the state of the art.

Water quality forecasting is a time series analysis task involving estimating future water conditions, vital in environmental management and pollution control. However, existing time series analysis methods focus only on historical observational data, neglecting information from other modalities, leading to incomplete feature extraction and affecting forecasting accuracy and robustness. In addition, the complex spatial dependencies between water quality monitoring stations and the nonlinear fluctuations in water quality indicators caused by meteorological factors present additional challenges. This work proposes a Spatio-Temporal Multimodal Fusion architecture for long-term water quality forecasting, named STMF, to address these issues. It first captures spatio-temporal dependencies by integrating temporal features with upstream-downstream relationships among monitoring stations. Then, STMF further designs a Low-rank Cross-modal Interaction Fusion (LRCIF) method, which fuses spatio-temporal features with precipitation features from the remote sensing image, as an additional modality, effectively leveraging complementary information from multiple data sources to enhance the accuracy and stability of water quality forecasting. Experimental results on real-world water quality datasets demonstrate that the proposed STMF significantly outperforms existing state-of-the-art methods in prediction accuracy. In particular, for long-term forecasting tasks with a 192-step horizon, STMF improves MSE and MAE by 14% and 12%, respectively, compared to unimodal models. It further validates the effectiveness of the multimodal fusion strategy. Overall, STMF offers an effective solution for water quality monitoring and management.

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture.

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction uses historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from the time series data and investigate complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called DPSGT for short, which integrates Dual Patch Savitsky–Golay filtering and Transformer. First, DPSGT adopts the SG filtering to decompose the time series data and reduce the noise interference to improve long–term prediction capabilities. Second, to tackle the limitation of temporal representation capability, DPSGT adopts dual patches to ravel temporal series into local and global patches, which can tackle local semantic information and enlarge the receptive field. Third, it utilizes a transformer mechanism to address the nonlinear problem of the water quality time series and improve the accuracy of the prediction. Two real-world datasets are utilized to evaluate the proposed DPSGT, and experiments prove that DPSGT improves root mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 by 6%, 5%, 8%, and 7%, respectively, compared with other benchmark models.

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm calledGeneticSimulated-annealing-basedParticle swarm optimization withAuto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs.

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy.

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption.

ABSTRACT Background In recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation models, and multimodal LMs. They are updated and iterated at a very fast pace. These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, and intelligent factory scheduling. Their development has contributed to various industries in human society. Aims The architectural flaws of LMs lead to several problems, including illusions and difficulty in locating errors, limiting their performance. Solving these problems properly can facilitate their further development. Methods This work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, localization fault, and ethical problems. Then, potential solutions to these problems are provided, including increase data and computation capability, neural‐symbolic synergy, and data orientation to human pattern. Discussion This work discusses developing vertical domain LMs on top of some base LMs. In addition, this work introduces three typical real‐world applications of LMs, including autonomous driving, smart industrial productions, and intelligent medical assistance. Conclusion By embracing the advantages of LMs and solving their fundamental problems, many industries are expected to achieve promising prospects in the future.

Over the past decades of rapid development, the global water pollution problem became prominent. Accurate water quality prediction can detect the trend and anomaly of water quality changes in advance, thereby taking timely measures to avoid water quality problems. Traditional statistical methods for water quality prediction tend to fail to capture the complex relationship among multiple water quality variables. Deep learning models face a challenge to capture both temporal dependence and spatial correlation of the water quality series data. To solve the above problems, this work proposes an adaptive and dynamic graph fusion water quality prediction model based on a spatiotemporal attention mechanism namedSpatial-TemporalGraphFusionTransformer (STGFT). It integrates a spatial attention encoder, a temporal attention encoder, an adaptive dynamic adjacency matrix generator, and a multi-graph fusion layer. Among them, the first two are adopted to capture the spatial correlations and temporal characteristics among different water quality monitoring stations, respectively. The generator can produce adaptive and dynamic adjacency matrices to reflect potential spatial relationships in a river network. Experimental results with real-life water quality datasets reveal that the prediction accuracy of STGFT outperforms the existing state-of-the-art models. Note to Practitioners—This paper is motivated by the problem of long-term water quality prediction. The highly volatile water quality data and the nonlinear characteristics of the time series greatly affect the accuracy of the forecasting task. Existing approaches fail to simultaneously capture spatial correlations and temporal characteristics among different water quality monitoring stations, affecting the accuracy of water quality predictions. This work proposes a water quality prediction method that captures the spatial correlations and temporal characteristics among different water quality monitoring stations. Moreover, it produces adaptive and dynamic adjacency matrices to reflect potential spatial relationships in a river network. Experimental results from three real-world datasets show that this approach is feasible and obtains more accurate prediction results. Furthermore, this method can also be applied to other areas of time series prediction, including finance, traffic, and smart manufacturing.

Water quality prediction provides timely insights for addressing potential water environmental issues. Transformer-based models have been widely used in water quality prediction. However, the following challenges exist: 1) Noise in the time series of water quality causes nonlinear models to be overfit; 2) It is difficult to identify temporal correlations in complex time series data; and 3) Information utilization is limited in long-term prediction. This work introduces a large-scale water quality prediction model named SVD-Autoformer to address them. SVD-Autoformer combines aSavitzky-Golay (SG) filter,variational modedecomposition (VMD), anauto-correlation mechanism, and a deep decomposition architecture, which is achieved in the renovation of the transformer. First, the SG filter removes noise while retaining valuable data features. SVD-Autoformer employs the SG filter as a data preprocessing tool to reduce noise and prevent nonlinear models from overfitting. Second, VMD extracts major modes of the signals and their respective center frequencies, thus providing richer features for the prediction. Third, the deep decomposition architecture with embedded decomposition modules allows for gradual decomposition during the prediction process. SVD-Autoformer employs the architecture to extract more predictable components from complicated water quality time series for long-term forecasting. Finally, SVD-Autoformer applies the auto-correlation mechanism to capture the temporal dependence and enhance information utilization. Numerous experiments are conducted and the results demonstrate that SVD-Autoformer provides superior prediction accuracy over other advanced prediction methods with real-world datasets. Note to Practitioners—This paper explores the critical aspects of time series water quality prediction, aiming to provide valuable insights for engineers and decision-makers. Traditional water quality prediction methods primarily rely on linear time series approaches and suffer from high computational complexity when dealing with large-scale data. This study is motivated by the transformer architecture with highly parallel computing capability and innovatively proposes deep decomposition architecture to extract more predictable components. In practice, to handle massive data with low time complexity, we introduce an auto-correlation mechanism. We conduct experiments using real-world datasets to demonstrate that this method achieves superior water quality prediction accuracy. Additionally, the method has been deployed in a real-world water quality prediction platform. Our future work includes its applications to different real-world datasets arising from electric power, intelligent transportation, and meteorological rainfall prediction.

In many fields, spatiotemporal prediction is gaining more and more attention,e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is a spatiotemporal prediction task. However, there are several challenges in water quality prediction: 1) Water quality time series has a complex nonlinear relationship, making it difficult to predict; 2) Water quality sensors are distributed on the river networks and have a strong spatial dependence on water quality prediction; and 3) Poor long-term forecast accuracy. To solve these problems, this work proposes a spatiotemporal prediction model called a Fusion Spatio-temporal Graph Convolution Neural network (FSGCN). First, This work uses a temporal attention mechanism to solve the nonlinear problem of water quality time series. Second, It adopts a graph convolution to extract spatial dependencies of river networks, and the fusion of spatiotemporal can more easily capture spatiotemporal features. Third, it adopts a temporal convolution residual mechanism, improving long-term series prediction accuracy. This work adopts two real-world datasets to evaluate the proposed FSGCN, and experiments demonstrate that FSGCN outperforms several state-of-the-art methods in terms of prediction accuracy.Note to Practitioners—This work considers the critical problem of spatiotemporal water quality prediction. Accurate water quality prediction can effectively prevent environmental pollution. Traditional water quality prediction only focuses on time series features without considering spatial features. In this work, a novel spatiotemporal prediction approach is proposed that combines spatial-temporal graph fusion construction networks for water quality time series prediction in a real-time manner. This work shows that this approach can achieve longer forecasting sequences and more accurate results than traditional forecasting methods. As a practical consequence of this research, spatiotemporal graph fusion convolution networks for water quality prediction can effectively integrate multi-dimensional data and improve the accuracy of long-term water quality prediction. This approach can also be applied to other fields, including intelligent transportation, smart manufacturing, finance, the Internet of Things, and urban computing.

Efficient and accurate identification of network anomalies is significant to network security systems. It is highly challenging to detect abnormal behaviors in the increasing network data accurately. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have unsatisfying detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. In this work, based on the improvement of variational autoencoders, stacked sparse shrink variational autoencoders (S3VAEs) are designed. In addition, anUnbalancedXGBoost classifier based onGenetic simulated annealing particle swarm optimization (UXG) is proposed. Finally, the feature extractor of S3VAEs is combined with the UXG classifier, and the anomaly detection model is obtained. Experimental results based on four real-life data sets demonstrate that the proposed anomaly detection model achieves higher classification accuracy and F1 than several state-of-the-art algorithms.

With the rapid development of the Internet, various network invasive behaviors are increasing rapidly. This seriously threatens the economic development of individuals, enterprises, and society. Network intrusion detection is important in network security systems, which can be regarded as a classification problem. It aims to distinguish between the specific categories of various network behaviors and determine whether the behavior belongs to network intrusion. However, network intrusions present a diverse and fast-changing trend, making categorizing difficult. Due to feature redundancy, uneven distribution of sample numbers, and inefficient parameter optimization, traditional rule-based approaches fail to achieve satisfying classification accuracy. This work proposes a multi-classification intrusion detection model based on Stacked Sparse Shrink AutoEncoder (SSSAE), Genetic Simulated annealing-based particle swarm optimization optimized Tabnet classifier (GS-Tabnet), and Decision Fusion (DF), called for SGTD short. Among them, SSSAE extracts multiple feature sets from the input data. Then GS-Tabnet trains a classifier for each feature set. Finally, the decision fusion fuses the results from these classifiers to obtain the final classification result. SGTD is compared with eight multi-classification benchmark models, and its intrusion detection accuracy is superior to its peers.

Water quality prediction has emerged as a prominent research problem in recent years, which entails a spatiotemporal prediction task. However, several challenges are associated with water quality prediction: 1) water quality time series exhibit complex nonlinear relationships, making prediction challenging; 2) water quality sensors are distributed across river networks, leading to strong spatial dependencies in water quality prediction; 3) current methods like traditional machine learning methods have poor accuracy in long-term water quality prediction. To solve these problems, this work proposes a spatiotemporal prediction model named Graph Attention Transformer with Dilated Causal Convolution and Laplacian Eigenvectors (GTDL). First, dilated causal convolution is adopted to extract temporal features of input sequences. Second, a transformer with Laplacian eigenvectors is designed to extract spatial dependencies of river networks. Third, residual connections are utilized at the output stage to enhance the accuracy of the long-term prediction. Finally, GTDL is evaluated with two real-world water quality datasets and experimental results prove that GTDL outperforms other baseline methods regarding the prediction accuracy. Specifically, compared with five state-of-the-art prediction models, GTDL improves the prediction accuracy by 11.3%-63.5% and 6%-52.4% on two datasets, respectively.

Edge computing offers a groundbreaking architecture for supplying computing, storage, and networking resources to propel the Internet of Things forward. By situating them at the network's edge, this model makes computational power more accessible to users. If tasks are executed entirely at the edge, energy and resource constraints of edge nodes may lead to poor performance. Therefore, it is widely recognized that offloading certain tasks to cloud data centers (CDCs), which possess abundant execution resources, is advantageous. However, implementing CDCs is not widespread and lacks flexibility in isolated regions. This presents challenges and high costs for reliably completing tasks quickly. Consequently, employing more adaptable unmanned aerial vehicles (UAVs) as CDCs in specific scenarios is crucial. The work presents the idea of mobile edge computing supported by the UAV. By considering the needs of user services, we enhance the energy efficiency of the UAV by optimizing their trajectories, transmission power, and computational load distribution. Furthermore, the work introduces an improved algorithm called GeneticSimulated-annealing-based Particle Swarm Optimizer (GSPSO) to optimize the energy efficiency of the UAV. Experimental simulations show that regarding the energy efficiency of the UAV, GSPSO exhibits superior search efficiency, surpassing genetic algorithm, simulated annealing, and particle swarm optimization by 7.39%, 15.03%, and 27.93%, respectively.

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively.

Water quality prediction methods forecast the future short or long-term trends of its changes, providing proactive advice for water pollution prevention and control. Existing water quality prediction methods only consider the historical data of single-type or multi-type water quality. However, meteorology and other factors also have a significant impact on water quality indicators. Therefore, only considering the historical data of water quality is not feasible. Unlike existing studies, this work proposes a hybrid water quality prediction model called CMI to solve the above problem. Before prediction, CMI incorporates a multimodal fusion mechanism of water quality time series and remote sensing images of meteorological rainfall. Moreover, CMI integrates the model of ConvNeXt V2 and a multimodal bottleneck transformer to extract image features for fusing the time series and images. Furthermore, it utilizes an emerging model of iTransformer to realize prediction with the fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMI outperforms other state-of-the-art fusion algorithms, and the water quality prediction accuracy with fused meteorological data is 13% higher on average than that with only water quality time series.

Many new IoT applications have emerged with the fast evolution of 5G and the Internet of Things (IoT). These applications place higher demands on network energy consumption and processing capabilities. Mobile edge computing (MEC) significantly enhances execution efficiency, while energy harvesting (EH) modules further augment the operational features of IoT devices. However, existing studies mainly concentrate on energy consumption and latency problems, often neglecting issues about user mobility and potential privacy leakage within the MEC environment. Therefore, optimizing computation offloading and resource allocation for MEC-enabled IoT networks is essential. This work proposes an innovative architecture with EH for collaborative computing between multiple mobile devices (MDs) and MEC servers. To tackle the problem, this work also proposes an advanced hybrid algorithm named Self-adaptive Bat Optimizer with Genetic operations and individual update of Grey wolf optimizer (SBG2). With SBG2, this work aims to minimize the energy consumption of MDs while providing user mobility and privacy protection. Simulation experiments show that SBG2 reduces energy consumption by 79.15%, 93.20%, and 89.58%, respectively, compared to the other three typical algorithms.

Mobile edge computing (MEC) emerges as a vital paradigm to support the increasing use of mobile users (MUs) with capabilities similar to cloud computing. While most research concentrates on MEC facilitated by terrestrial base stations (BSs), its applicability in scenarios such as disaster rescue and field operations is limited. Efforts have been made to explore MEC assisted by unmanned aerial vehicles (UAVs) with efficient scheduling algorithms. However, relying solely on UAVs for MEC has limitations, particularly for computation-intensive applications. This work proposes a hybrid MEC system lever-aging UAVs and BS. Multiple UAVs and a BS are deployed to provide MEC services directly from UAVs or indirectly from the BS. We formulate an energy-efficient scheduling problem to minimize energy consumption by jointly optimizing UAV trajectories, task associations, and allocation of computing and transmitting resources. To solve it, this work designs a hybrid algorithm named _S_uccess History-based parameter Adaptation for Differential volution with a Niching-based population size reduction strategy and an efficient nsemble sinusoidal scheme (SHADE-NE). Experimental results validate the superiority of SHADE-NE over its benchmark peers, thus proving that SHADE-NE greatly enhances the performance of the system.

The advent of smart manufacturing in Industry 4.0 signifies the arrival of the era of connections. As an excellent communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, a method named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. It extracts the information models of OPC UA servers into resource description framework triples, utilizes web ontology language for semantic enrichment and inference, and employs a semantic similarity model for event ontology mapping to improve query efficiency. The method's effectiveness is validated through functional queries using the SPARQL protocol in Apache Jena. The query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture.

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

Water pollution is continuously increasing in water ecosystems across all continents. Surface water sensors can record data on water quality indicators at regular intervals, and the associated water quality sequences show abnormal trends when extreme weather or unusual industrial discharges occur. Therefore, governments can take timely actions to minimize damage and protect the water environment by detecting these abnormal trends promptly. However, current methods make it difficult to interpret different correlations among water quality parameters effectively. To solve this problem, this work proposes a parameter correlation-aware anomaly detection model, which integrates Dual sliding windows, Convolutional LSTM, and a Deep neural network with dropout, called for DCLD short. First, DCLD designs dual sliding windows to capture local and global patterns within the sequence of water quality. Second, DCLD adopts a stacked long short-term memory with a convolutional neural network to capture complex features and long-term dependencies in the time series. Third, DCLD uses a deep neural network incorporating the dropout algorithm to extract abstract features. DCLD mitigates overfitting risks and enhances the model's generalization capacity. Finally, DCLD is evaluated with two real-world water quality datasets, and its anomaly detection accuracy is improved by 5.41% and 0.79% on average over its peers.

Multi-objective optimization algorithms are essential for addressing real-world challenges characterized by conflicting objectives. Although conventional algorithms are effective in exploring solution spaces and generating non-dominated solutions, solution quality and dynamic adaptability of true Pareto fronts need to be improved. This work proposes a multi-objective algorithm that integrates Non-dominated sorting genetic algorithm II (NSGA-II) and Multi-Objective Reinforcement Learning (N-MORL). N-MORL consists of two parts including upstream and downstream components. In the upstream component, this work improves the Variance-stabilized Multi-objective Proximal Policy Optimization (VMPPO) for enhanced convergence stability by adjusting its iteration mechanism. Additionally, this work optimizes variance networks and action sampling to balance exploration and exploitation, which improves experience sampling efficiency. This work adopts high-quality solution sets yielded by MORL as the initial solution set for downstream NSGA-II, guiding the exploration space and increasing the solution number. High-quality initial solutions significantly accelerate the iterative convergence speed of N-MORL. N-MORL provides the quality and the number of solutions, better covering or approaching the true Pareto front. Experimental results with five benchmark multi-objective functions demonstrate that N-MORL outperforms the other three multi-objective evolutionary algorithms regarding high-quality solutions with the same iterations.

Recent years have seen a surge in Internet of Things (IoT) technologies, with billions of mobile devices (MDs) straining limited computing and networking resources. Mobile edge computing offloads tasks from MDs to edge servers, saving energy and reducing network pressure. Edge servers provide closer services yet have fewer resources than cloud servers. A new heterogeneous edge and cloud computing paradigm combines the benefits of both. Edge servers provide close proximity services to MDs, while the cloud owns enough resources. The existence of mobile IoT devices makes it more practical to consider mobility when allocating resources of edge servers to decrease the energy consumption of the heterogeneous edge and cloud while meeting the latency needs of tasks. This work formulate a constrained energy consumption optimization problem and design a hybrid algorithm named Genetic Simulated-annealing-based particle swarm optimization (PSO) to yield a near-optimal solution. Simulation results prove that compared to genetic algorithm, PSO, simulated-annealing-based PSO, and Trex, GSPSO reduces the total energy consumption by 38.64%, 54.63%, 45.94%, and 36.21%, respectively.

In a cloud-edge system, data analysis, processing, and storage can be performed in edge servers, avoiding transferring data to more distant cloud servers. This greatly improves the efficiency of data processing, saves network bandwidth and cloud resources, and reduces operating and maintenance costs. However, it is a challenge of how to perform task scheduling. It is difficult to schedule tasks for joint optimization of the total energy consumption and completion time of a task sequence within a limited time in a resource-constrained cloud-edge system. The work proposes an improved Simulated-Annealing-based Firefly Algorithm with Linear position update, called SAFAL for short. SAFAL incorporates a simulated annealing mechanism and an efficient position update strategy into the firefly algorithm, enabling fireflies to find the optimal solution more quickly and avoid getting trapped in local optima. SAFAL adopts a probabilistic mapping operator to map the position of each firefly to a task scheduling sequence, thus linking the firefly space and the task space. Several test instances in cloud-edge systems are designed to validate the superiority of SAFAL over the firefly algorithm, simulated annealing, and firefly algorithm with a self-adaptive strategy. Results show that the weighted cost of total energy consumption and completion time of SAFAL is reduced by 16.32%, 17.62%, and 14.21%, respectively, with 20 tasks.

With the increasing amount of data and the need for real-time processing, Mobile Edge Computing (MEC) is growing rapidly, driving the shift from traditional cloud computing to distributed edge architectures. When offloading these applications with large amounts of data on mobile devices, a lot of computing and storage resources and high energy consumption are required. Yet, mobile devices' computing power, resource storage, and battery power are often limited and cannot meet these needs. To solve a computation offloading problem for joint optimization of time, cost, and energy, this work proposes an improved hybrid algorithm called Chaos and Lévy flights-based Whale Optimization Algorithm (CLWOA) to solve the multi-user offloading problem in an MEC-Cloud system. Each task is offloaded to local processors of mobile devices, edge servers, and cloud servers in proportion to jointly minimize the completion time, energy consumption, and total cost. Finally, compared with the whale optimization algorithm, lévy flight whale optimization algorithm, refined whale optimization algorithm, and chaos-based whale optimization algorithm, CLWOA reduces the weighted cost by 1.89%, 0.31%, 0.19%, and 0.42%, respectively.

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios.

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%.

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

Resource usage prediction in cloud data centers is critically important. It can improve providers’ service quality and avoid resource wastage and insufficiency. However, the time series of resource usage in cloud environments is characterized by multidimensional, nonlinear, and high-volatility characteristics. Achieving high-accuracy prediction for time series with such characteristics is necessary but difficult. Traditional prediction methods based on regression algorithms and recurrent neural networks cannot effectively extract nonlinear features from data sets. Besides, many deep learning models suffer from gradient explosion or gradient vanishing during the training stage. Current commonly used prediction methods fail to uncover some vital information about the frequency domain features in the time series. To resolve these challenges, we design a Forecasting method based on the Integration of a Savitzky–Golay (SG) filter, a frequency enhanced decomposed transformer (FEDformer) model, and a frequency-enhanced channel attention mechanism (FECAM), named FISFA. It adopts the SG filter to reduce noise and smooth sequences in the raw sequences of resources. Then, we develop a hybrid transformer-based model integrating FEDformer and the FECAM, effectively capturing the frequency domain patterns. Besides, a meta-heuristic optimization algorithm, i.e., genetic simulated annealing-based particle swarm optimizer, is proposed to optimize key hyperparameters of FISFA. Then, FISFA predicts the future needs for multidimensional resources in highly fluctuating traces in real-life cloud environments. Experimental results demonstrate that FISFA achieves higher accuracy and performs more efficient prediction than several benchmark forecasting methods with realistic data sets collected from Alibaba and Google cluster traces. FISFA improves the prediction accuracy on average by 32.14%, 25.49%, and 27.71% over vanilla long short-term memory, transformer, and Informer methods, respectively.

Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming and advanced image editing. However, SMDs often face constraints in computational capacity and battery duration, restricting their ability to process these tasks instantaneously. Cloud computing can circumvent these limitations by computation offloading, but cloud data centers (CDCs) are often deployed at long distances from users, which results in longer computational latency. To address the latency issue, the incorporation of small base stations (SBSs) in the vicinity of the user provides services with high bandwidth and low latency. The primary challenge lies in balancing the economics of the system consisting of different SMDs, SBSs, and a CDC, i.e., minimizing cost while still meeting the latency requirements of applications. In this work, a cost-minimized computation offloading framework is formulated and solved by a two-stage optimization algorithm named Lévy flight and Simulated Annealing-based Grey wolf optimizer (LSAG). The optimal edge selection strategy is defined in the first stage for dealing with the case of several available SBSs. The second stage coordinates task scheduling and optimizes the allocation of resources among SMDs, SBSs, and CDC. LSAG integrates the extended search property of Lévy flight and the individual selection strategy of simulated annealing in the grey wolf optimizer, which reduces the risk of falling into local optima and finds the global optimum. Experimental results of executing real-life tasks show that LSAG outperforms its state-of-the-art peers in terms of cost and speed of convergence.

Precise and real-time prediction of future network attacks can not only prompt cloud infrastructures to fast respond and protect network security, but also prevents economic and business losses. In recent years, neural networks, e.g., Bi-directional Gated Recurrent Unit network and Temporal Convolutional Network (TCN), have been proven to be suitable for predicting time series data. Attention mechanisms are also widely used for the prediction of the time series of network attacks. This work proposes a hybrid deep learning prediction method that combines capabilities of Savitzky-Golay filter, TCN, Multi-head self attention, and Bi-directional Gated Recurrent Unit (STMB) for the prediction of network attacks. This work first adopts a Savitzky-Golay filter to smooth possible outliers and noise in network attack traffic data. It applies TCN to extract abstract features from one-dimensional time series to make full use of data. It then adopts multi-head self-attention to capture internal correlations among multi-dimensional features, by increasing weights of key features and reducing those weight of non-key features, making that SMTB captures important features adaptively. Finally, this work adopts Bi-directional Gated Recurrent Unit to extract bi-directional and long-term correlations in the time series to imporve the prediction accuracy. This work also utilizes a hybrid algorithm named genetic simulated-annealing-based particle swarm optimizer to determine the hyperparameter setting of STMB. Experimental results with real-life datasets show that STMB outperforms several commonly-used algorithms in terms of prediction accuracy.

Accurate workload and resource prediction are critical to realizing proactive, dynamic, and self-adaptive resource allocation for building cost-effective, energy-efficient, and green cloud data centers (CDCs), providing satisfactory quality services to users and high revenue to cloud providers. However, it is challenging because patterns of dramatically increasing and large-scale workload and resource usage in CDCs vary significantly with time. Current prediction methods often fail to handle implicit noise data and capture nonlinear, long and short-term, and spatial characteristics in workload and resource time series, thus leading to limited prediction accuracy. To tackle these issues, this work designs a novel prediction approach named VSBG that seamlessly and innovatively combines Variational mode decomposition, Savitzky Golay, Bi-directional long short-term memory (LSTM), and Grid LSTM to predict workload and resource usage in CDCs accurately. VSBG innovatively integrates variational mode decomposition (VMD) and a Savitzky Golay (SG) filter in a four-step manner before exploring its prediction. VSBG leverages VMD to divide non-stationary workload and resource time series into multiple mode functions. Then, VSBG designs a quadratic penalty, solves it with a Lagrangian multiplier, and adopts a logarithmic operation and the SG filter to smooth the first mode function to eliminate noise interference. Finally, VSBG, for the first time, systematically and simultaneously captures depth and temporal characteristics of fluctuating and complex time series data with two BiLSTM layers, between which a GridLSTM layer lies, thereby accurately predicting workload and resources in CDCs. Extensive experiments with different real-world datasets prove that VSBG outperforms a holistic set of state-of-the-art algorithms on prediction accuracy and convergence speed.

Current Industrial Internet supports the sharing of information on heterogeneous resources and elements in a process of industrial production. It enables intelligent production processes and supports cost-effective scheduling. However, collaborative manufacturing and scheduling planning for enterprises with multiple plants cause several major challenges because of a large number of decision variables and constraints of manufacturing abilities of plants, resources of production, etc. Existing methods cannot comprehensively optimize the cost of multiple products in different plants, and fail to consider machine-level optimization of tasks of manufacturing. We propose a comprehensive machine-level architecture for enterprises with multiple plants. Based on this architecture, we formulate a limited non-linear integer optimization problem to decrease the total cost of transportation, production, and sales. In it, several real-life complicated nonlinear constraints are jointly considered, and they include constraints of storage space, replacement times, pairing production, substitution, and order fulfillment rates. To solve this optimization problem, we design a hybrid meta-heuristic optimization algorithm named Genetic Simulated annealing-based Particle Swarm Optimizer with Auto-Encoders (GSPAE). Extensive experiments with real-life data show that GSPAE decreases the total cost by 25% than other state-of-the-art methods.

Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs’ caching and computation capacities while meeting MUs’ latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP’s system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

The rapid expansion of Internet users results in an immense influx of network traffic within extensive cloud data centers. Accurate and instantaneous identification and forecasting of network traffic aid system managers in efficiently distributing resources, assessing network performance based on specific service demands and scrutinizing the health of network status. However, sources and distributions of traffic are different, which makes accurate warnings of cyberattack traffic difficult. Recently, emerging neural networks have demonstrated their efficacy in forecasting time series data of network cyberattacks. The time series has temporal and spatial features, which can be efficiently captured with Informer and convolutional neural networks (CNNs). To realize high-performance spatiotemporal detection of cyberattacks, this work for the first time designs a hybrid and spatiotemporal prediction framework, which integrates CNNs, Informer, and a Softmax classifier to realize high-classification accuracy of normal and abnormal cyberattacks. Real-life data are adopted to evaluate the proposed method, which yields significant improvement in classification accuracy over typical benchmark classification models.

Hybrid cloud-edge systems combine the advantages of cloud computing and mobile edge computing (MEC) to achieve flexible integration and fluidity of data between the cloud and the edge. To address dynamic and stochastic loads caused by mobile users (MUs) and time-varying tasks, MEC network operators need to continuously migrate installed services among edge servers, significantly increasing network maintenance costs. Existing studies often overlook the service migration cost resulting from MU mobility. Therefore, we present a joint optimization scheme focusing on minimizing the operational cost of hybrid cloud-edge systems while considering the dynamic service migration cost induced by MUs. With the rapid development of 5G/6G technologies, many MUs require connectivity to edge nodes (ENs) or cloud data centers (CDCs) for processing. Minimizing the operational cost of hybrid cloud-edge systems while considering many heterogeneous decision variables is a challenge. To solve this complex high-dimensional mixed-integer nonlinear problem, we develop a novel deep learning-based evolutionary algorithm called autoencoder-based multiswarm gray wolf optimizer based on genetic learning (AMGG). Experimental results with real data demonstrate that AMGG achieves lower system cost by 49.69% while strictly meeting task latency requirements of MUs compared with state-of-the-art algorithms.

Water quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationarity of water quality data suffers from time variation and human activities, which leads to a poor prediction accuracy because ANNs receive whole water quality data passively, including abnormal conditions. We consider such a tough problem in this article and propose an event-triggered deep fuzzy neural network (ET-DFNN) to pursue the better performance of water quality prediction in the complex water environment. First, a deep pretraining model is constructed to extract the effective features from raw water quality data. Second, we construct a DFNN model where the extracted effective features are considered as the input variables. Third, some events are defined to characterize the abnormal conditions of state evolution in water quality. The DFNN is trained and updated using different learning strategies only when the corresponding events are triggered, otherwise it ignores the current data sample and directly goes to the next data sample. The practical data-based experimental results show that the ET-DFNN achieves better prediction performance in accuracy and efficiency than its peers. Especially, the training efficiency of ET-DFNN is improved by 57.94% on total phosphorus prediction and 48.31% on biochemical oxygen demand prediction, respectively.

Current cloud data centers (CDCs) provide highly scalable, flexible, and cost-effective services to meet the performance needs of emerging applications. It is critical for CDC providers to predict future incoming workloads such that they can perform accurate resource provisioning in CDCs. Prediction accuracy is important and its improvement has been pursued in much existing work. This work adopts two different real-life Google data traces, based on which such prediction is conducted. Specifically, this work first gives a novel prediction mechanism that integrates wavelet decomposition, Savitzky–Golay (SG) filter, and autoregressive integrated moving average (ARIMA) to realize workload prediction in each time interval. The time series of the workload is smoothed with an SG filter and further divided into several components with wavelet decomposition. Then, an integrated approach is developed to predict statistical trends and their detail components. Real-life trace-driven experiments are done and the results suggest that the proposed method provides higher accuracy of prediction than its existing peers.

Evolutionary algorithms are commonly used to solve many complex optimization problems in such fields as robotics, industrial automation, and complex system design. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-learning-based Optimizer is designed to dynamically adjust parameters for balancing exploration and exploitation during its solution process. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate it by comparing it with several state-of-the-art algorithms through six benchmark functions. We further test it by applying it to solve a real-world computational offloading problem.

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5.

Current mobile edge computing (MEC) owns cloud resources at the network edge, which enables low-latency mobile services. In addition to fixed MEC servers, MEC proxy servers with certain mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs), and vehicles, have emerged as competitors in providing services. In this work, aiming at a task offloading problem of a UAV-assisted MEC system, a hybrid network environment with multiple mobile devices (MDs) and multiple UAVs is established. A constrained mixed integer nonlinear program of the UAV-assisted hybrid cloud-edge system is formulated. A novel hybrid metaheuristic algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) is presented to solve the program. Then, a task offloading and resource scheduling method is designed to intelligently minimize the total energy consumption of the hybrid system. Simulation results verify superiority of GSPSO over its three benchmark algorithms, thus demonstrating the proposed method significantly improves the energy efficiency of the UAV-enabled hybrid system.

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms.

Water quality prediction can accurately reflect the development trend of water quality, and it is an important means to prevent the water environment from being polluted and maintain the health of the water environment. Existing prediction methods generally cannot accurately capture non-linear characteristics of water quality, and suffer from issues of gradient disappearance and gradient explosion. This work designs a water quality prediction model called SMF2 to effectively solve these problems and increase the accuracy of prediction. SMF2 combines the Savitsky-Golay filter, seasonal-trend decomposition using loess for multiple seasonal components, Fourier transform frequency-enhanced block and frequency-enhanced attention, serving for noise smoothing, extraction of exact seasonal components, time domain-frequency domain interconversion, feature extraction, and time series prediction by frequency domain low-rank approximation transform, respectively. Experimental results based on a real-life water environment data set show that the proposed SMF2 outperforms other advanced algorithms in terms of prediction accuracy.

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations.

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers.

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms.

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially.

Water quality prediction provides timely management to solve possible water environmental problems, which is of great importance. However, the following challenges exist: 1) The existence of noise in the water quality time series can lead to overfitting of nonlinear models; 2) It is difficult to capture temporal dependencies in complex time series data; 3) Long-term forecasting is difficult to achieve. To address the above difficulties, this work proposes a multi-step water quality prediction model, called SG-Autoformer, which combines the Savitzky-Golay filter, the inner series decomposition, and an auto-correlation mechanism. First, SG-Autoformer performs noise reduction on the water quality time series to suppress overfitting of nonlinear models. Second, it embeds series decomposition inside the encoder and decoder, which obtains more predictable components from complex time series for long-term prediction. Third, SG-Autoformer utilizes the auto-correlation mechanism to capture the time dependence and improve information utilization. Extensive experiments with real-world datasets show that SG-Autoformer outperforms other advanced prediction methods in terms of prediction accuracy.

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system.

Cloud-edge hybrid systems are known to support delay-sensitive applications of contemporary industrial Internet of Things (IoT). While edge nodes (ENs) provide IoT users with real-time computing/network services in a pay-as-you-go manner, their resources incur cost. Thus, their profit maximization remains a core objective. With the rapid development of 5G network technologies, an enormous number of mobile devices (MDs) have been connected to ENs. As a result, how to maximize the profit of ENs has become increasingly more challenging since it involves massive heterogeneous decision variables about task allocation among MDs, ENs, and a cloud data center (CDC), as well as associations of MDs to proper ENs dynamically. To tackle such a challenge, this work adopts a divide-and-conquer strategy that models applications as multiple subtasks, each of which can be independently completed in MDs, ENs, and a CDC. A joint optimization problem is formulated on task offloading, task partitioning, and associations of users to ENs to maximize the profit of ENs. To solve this high-dimensional mixed-integer nonlinear program, a novel deep-learning algorithm is developed and named as a Genetic Simulated-annealing-based Particle-swarm-optimizer with Stacked Autoencoders (GSPSA). Real-life data-based experimental results demonstrate that GSPSA offers higher profit of ENs while strictly meeting latency needs of user tasks than state-of-the-art algorithms.

Wastewater treatment processes (WWTPs) have been considered as complex control problems, because effluent water standard, stability and multioperational conditions need to be taken into account. In this article, an event-driven model predictive control with deep learning (EMPC-DL) is proposed for the control problems to improve the running performance of WWTPs. First, several events are defined based on different operational conditions reflected by operational data. Then, an event-driven deep belief network (EDBN) is developed based on deep learning to approximate the nonlinear characteristics of the WWTPs. Second, a quadratic optimization is designed to solve the control law of MPC based on the predictive output of the EDBN. The major advantage of quadratic optimization is its efficiency, which is achieved by an efficient strategy that only needs one-step prediction of EDBN during one-time rolling optimization. Third, this article gives convergence and stability analysis of EMPC-DL. Finally, the feasibility and applicability of EMPC-DL are demonstrated on the benchmark simulation model No. 1 (BSM1). The experimental results show that EMPC-DL achieves the more satisfactory performance in modeling, controlling, and tracking water quality parameters than its peers.

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers’ requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integratesVariational mode decomposition, anAdaptive Savitzky-Golay (SG) filter, aMulti-head attention mechanism,Bidirectional andGrid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces.

As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, namedParticle swarm optimization based onGeneticLearning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs’ association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.

Real-time and precise prediction for traffic of networks is critically important for allocating the optimal computing/network resources based on users' business requirements, analyzing the network performance, and realizing intelligent congestion control and high-accuracy anomaly detection. The dramatic growth of users' applications significantly increases the volume, uncertainty, and complexity of workload, thereby making it highly challenging to precisely predict future net-work traffic. Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) can be effectively used to analyze and predict time series. This work designs an improved prediction approach for the prediction of network traffic, which combines a Savitzky-Golay filter, TCN, and LSTM, called ST-LSTM for short. It first removes the noise of data with the filter of Savitzky-Golay. It then investigates temporal characteristics of data by using TCN. At last, it investigates the long-term dependency in the time series by using LSTM. Experimental results on a real-life website dataset show the prediction accuracy of ST-LSTM is higher than autoregressive integrated moving average, support vector regression, eXtreme Gradient Boosting, backpropagation, TCN, and LSTM, in terms of several commonly used performance indicators.

Accurate prediction of water quality indicators can effectively prevent sudden water pollution events, and control pollution diffusion. Neural networks, e.g., long short-term memory (LSTM) and encoder-decoder network, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear. Accurate prediction of water quality becomes a big challenge. This work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with temporal attention mechanism and LSTM. Specifically, VBAED first adopts VMD to decompose the ground truth time series, and the decomposed results are used as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by an LSTM decoder with a temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods.

Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems, but also maintain the Internet environment. Currently, classification methods with autoencoders for feature learning have been proved to be suitable for the network intrusion detection. This work proposes a new hybrid classification method named SABD for network intrusion detection. SABD integrates Stacked sparse contractive autoencoders, Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. SABD integrates the feature extraction of stacked sparse contractive autoencoders with the classification ability of attention-based bidirectional LSTM. Specifically, stacked sparse contractive autoencoders are used for extracting features, which are sent to the attention-based bidirectional LSTM for the classification. Finally, the decision fusion algorithm is adopted to integrate classification results of multiple classifiers and yield the final results. Experimental results based on real-life UNSW-NB15 data demonstrate that the proposed SABD outperforms its state-of-the-art peers in terms of classification accuracy.

Fog computing extends a paradigm of cloud computing to a network edge, thus reducing transmission delay of users’ tasks and providing fast services. Dramatic increase of mobile devices brings a big challenge of how to keep low-response processing of tasks. To solve this challenge, this work aims to minimize the latency of tasks while meeting energy limits of mobile devices, and formulates a constrained optimization problem. It designs an improved optimization algorithm by following core steps of whale optimization algorithm (WOA) inspired by whale hunting behaviors. It is named C haotic D ifferential W OA with Lévy flight (CDWL). CDWL integrates merits of WOA, chaotic differential evolution, and random walks of Lévy flight. In this way, CDWL achieves strong global search and quick convergence. Real-life experiments demonstrate CDWL outperforms its six state-of-the-art peers.

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high performance in predicting resource usage and workload sequences. Much noise implicit in the original sequences of resources and workloads is another reason for their low performance. To address these problems, this work proposes a hybrid prediction model named SABG that integrates an adaptive Savitzky-Golay (SG) filter, Attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. SABG adopts an adaptive SG filter in the data pre-processing to eliminate noise and extreme points in the original time series. It uses bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Then, it utilizes an attention mechanism to explore importance of different data dimensions. SABG aims to predict resource usage and workloads in highly variable traces in cloud computing systems. Extensive experimental results demonstrate that SABG achieves higher-accuracy prediction than several benchmark prediction approaches with datasets from Google cluster traces.

Recognizing key entities on texts of water environment accurately and rapidly can not only extract important information of water environment, but also improve the water quality. In recent years, Chinese named entity recognition becomes a research focus and many methods based on neural networks have been proven effective on entity recognition. This work proposes an improved hybrid prediction model named LBPSC for Chinese named entity recognition for the water environment data, which combines Lattice structure, Bi-directional long short-term memory (BiLSTM), Positional feature encoding, Sentence self-attention and conditional random field (CRF). LBPSC employs a three-phase end-to-end methodology for Chinese named entity recognition. It first adopts a BiLSTM with lattice structure to extract both character and word features from two directions, thereby avoiding word segmentation errors. It then innovatively combines a sentence self-attention mechanism with positional feature encoding to better handle sentences and add the position information to the trained features after BiLSTM. Then, a CRF layer is adopted to decode features and finally output the predicted tag of the data. Experimental results with real-life dataset demonstrate that LBPSC outperforms other deep learning algorithms in terms of prediction accuracy.

The water quality data has missing values and lacks integrity because water environment monitoring equipments are easily damaged by environmental influences, thereby affecting the analysis accuracy of downstream tasks. Traditional data imputation methods include mea/Mast filling, K-nearest neighbor, matrix factorization, Lahrangian interpolation, etc., do not consider time dependence or fail to use complex relations among multiple features. Inspired by successful applications of various variants of Generative Adversarial Networks (GANs) on time series data, this work proposes a time series data imputation method called GEDA, which integrates -GAN, an Encoder-Decoder structure, and an Autoregressive network. GEDA adopts GAN to learn the probability distribution of multi-feature time series, and imputes the missing values with the generated data. Then, GEDA combines feature extraction of the encoder-decoder structure, and time dependence capturing of the autoregressive network. Real-life dataset-based experimental results demonstrate GEDA outperforms several state-of-the-art data imputation methods in terms of accuracy.

Precise real-time prediction of the number of future network attacks cannot only prompt cloud infrastructures to fast respond to them and protect network security, but also prevents economic and business losses. In recent years, neural networks, e.g., Bi-direction Long and Short Term Memory (LSTM) and Temporal Convolutional Network (TCN), have been proven to be suitable for predicting time series data. Attention mechanisms are also widely used for the time series prediction. In this work, we propose a novel hybrid deep learning prediction method by combining the capabilities of a Savitzky-Golay (SG) filter, TCN, Multi-head self attention, and BiLSTM for the prediction of network attacks. This work first adopts a SG filter to eliminate noise in the raw data. It applies TCN to extract short-term features from the sequences. It then adopts multi-head self attention to capture intrinsic connections among features. Finally, this work adopts Bi-LSTM to extract bi-directional and long-term correlations in the sequences. Experimental results with a real-life dataset show that the proposed method outperforms several typical algorithms in terms of prediction accuracy.

With the rapid development of cloud computing technologies, more and more individual users and enterprises choose to deploy their key applications in green data centers (GDCs), and the scale of GDCs is increasing rapidly. To ensure service quality and maximize the revenue, cloud service providers in GDCs need to reasonably and efficiently allocate computing resources and schedule tasks of users. Traditional heuristic algorithms face challenges of uncertainty and complexity in GDCs for scheduling tasks. To solve them, this work establishes an improved resource allocation and task scheduling method based on deep reinforcement learning. It considers the dependency among different tasks, and builds a workload model based on the real-life data in Google cluster trace. In addition, a deep reinforcement learning-based scheduling model is proposed to reasonably allocate and schedule resources (CPU and memory) in GDCs. Based on two models, an Improved Deep Q-learning Network (IDQN) is proposed to autonomously learn the changing environment of GDCs, and yield the optimal strategy for resource allocation and task scheduling. Real-life data-based experiments demonstrate that IDQN achieves lower task rejection rates and energy cost than several typical task scheduling methods.

Water quality prediction refers to the prediction of future water quality changes based on past data. Traditional prediction models cannot capture intricate and nonlinear features. Typical machine learning methods extract nonlinear characteristics, but they suffer from overfitting problems due to data noise. Most current deep learning models have problems of gradient disappearance and explosion, and often fail to capture long-term dependence. To solve above-mentioned problems, this work proposes a multi-indicator time series prediction method named SG-Informer for river water quality prediction. SG-Informer integrates the Savitsky-Golay filter, the ProbSparse self-attention mechanism of an encoder, and a generative style decoder, serving as data smoothing and noise elimination, network scale reduction, and prediction speed improvement, respectively. SG-Informer establishes a high-quality water quality time prediction model, which effectively predicts the future water quality time series trend. Based on real-life data sets of water quality, multi-indicator and single-indicator prediction experiments are performed. Experimental results demonstrate that the proposed SG-Informer outperforms several state-of-the-art prediction methods in terms of prediction accuracy.

Water quality prediction is of great significance for water environmental protection and management. Traditional water quality prediction methods are mainly based on linear models, and they fail to extract nonlinear relationships. Recurrent neural network-based ones have shortcomings such as being difficult for long-term prediction and unable to capture spatial dependencies. To address these issues, this work proposes an improved spatio-temporal prediction model called GATWNet, which combines a -G raph AT tention network with a W aveNet model based on dilated causal convolution to predict water quality at multiple sites over a future time period. GATWNet jointly captures the spatial information of river networks and the temporal information of each water quality monitoring sensor. Two real-world datasets-based experimental results demonstrate the proposed GATWNet achieves higher prediction accuracy than several baseline models.

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies; 2) There are complex nonlinear relations in water quality time series; and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy.

Cloud-edge hybrid systems can support delay-sensitive applications of industrial Internet of Things. Edge nodes (ENs) as service providers, provide users computing/network services in a pay-as-you-go manner, and they also suffer from the high cost brought by providing computing resources. Thus, the problem of profit maximization is highly important to ENs. However, with the development of 5G network technologies, a large number of mobile devices (MDs) are connected to ENs, making the above-mentioned problem a high-dimensional challenge, which is highly difficult to solve. This work formulates a joint optimization problem of task offloading, task partitioning, and associations of large-scale users to ENs to maximize the profit of ENs. This work focuses on applications that can be split into multiple subtasks, each of which can be completed in MDs, ENs and a cloud data center. Specifically, a mixed integer nonlinear program is formulated to maximize ENs’ profit. Then, a novel hybrid algorithm named Genetic Simulated-annealing-based Particle swarm optimizer with a Stacked Autoencoder (GSPSA) is designed to solve it. Real-life data-based experimental results demonstrate that compared with other peer algorithms, GSPSA increases the profit of ENs while strictly meeting latency needs of users’ tasks. The dimension of the problem that can be solved is increased by more than 50% with GSPSA.

As a new paradigm, industrial Internet provides information sharing of various elements and resources in a whole industrial production process. It makes industrial production processes intelligent and provides low-cost and efficient scheduling. Manufacturing planning for multi-plant enterprises in industrial Internet brings many big challenges due to numerous optimization variables and limits of manufacturing capacities of plants, production resources, etc. Current studies fail to jointly consider the cost of different products in multiple heterogeneous plants, and ignore machine-level scheduling of manufacturing tasks. This work designs an improved framework for multi-plant enterprises, based on which a constrained non-linear integer program for reducing the total cost including production cost and transportation one is formulated. It jointly considers many complex nonlinear constraints, e.g., limits of replacement times, storage space, substitution and pairing production. It investigates machine-level task scheduling where different machines have heterogeneous manufacturing capacities. To solve it, this work proposes an algorithm named Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO). Realistic data-based experiments demonstrate GSPSO reduces the cost of a multi-plant system by at least 23% than its typical peers.

Autonomous driving poses high demands on computing and communication resources. Vehicular edge computing is presented to offload real-time computing tasks from connected and automated vehicles (CAVs) to high-performance edge servers. However, it brings additional communication overhead due to limited bandwidth, and increases delay of tasks. To solve it, this work first proposes an offloading architecture including multiple CAVs, roadside units and cloud. We minimize the total cost of a hybrid system by jointly considering task offloading ratios, and allocation of communication and computing resources. Furthermore, a mixed integer non-linear program is formulated and solved by a novel meta-heuristic algorithm called Self-adaptive Gray Wolf Optimizer with Genetic Operations (SGWOGO). SGWOGO achieves joint optimization of computation offloading among CAVs, roadside units and cloud, and allocation of their resources. Finally, real-life data-driven simulation results demonstrate that SGWOGO achieves lower cost in fewer iterations compared with its several state-of-the-art peers.

Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by Bidirectional Long Short-Term Memory (BiLSTM) and Encoder-Decoder (ED) frameworks have been shown to be suitable for prediction of time series data. However, traditional statistical methods cannot capture nonlinear characteristics of the water quality, and deep learning models often suffer from gradient disappearance and gradient explosion problems. This work proposes a hybrid water quality prediction method called VBEG, which combines V ariational Mode Decomposition (VMD), B iLSTM, an E D structure, and G enetic Simulated annealing-based particle swarm optimization (GSPSO). VBEG first adopts VMD to deal with nonlinear features in the original time series. Then, VBEG combines BiLSTM and the ED structure to capture bi-directional long-term correlations, and realize dimensionality reduction, respectively. Furthermore, VBEG adopts GSPSO to optimize its hyperparameters. Experimental results with real-life datasets demonstrate that the proposed VBEG outperforms two current state-of-the-art algorithms in terms of prediction accuracy.

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky–Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy.Note to Practitioners—This work considers real-time and high-accuracy prediction of network traffic. It is highly important to well predict network traffic by capturing long-term dependence and effectively extracting high- and low-frequency information from time series data. Yet, it is a big challenge to achieve it because there are unstable characteristics and strong nonlinear features in the network traffic due to continuous expansion of network scale and fast emergence of new services. Current prediction methods usually have oversimplified theoretical assumptions, need significant time and memory, or suffer problems of gradient disappearance or early convergence. Thus, they fail to effectively capture the nonlinear characteristics of large-scale network sequences. This work proposes a hybrid prediction method named SG and TCN-based LSTM (ST-LSTM), which integrates the merits of the Savitzky–Golay filter, the temporal convolutional network (TCN), and the long short-term memory (LSTM), serving as smoothing time series, capturing short-term local features, and capturing long-term dependence, respectively. Experimental results based on the real-life dataset demonstrate that it achieves better prediction accuracy than its state-of-the-art peers, including the TCN and the LSTM. It can be readily implemented and deployed in many real-life industrial areas including smart city, edge computing, cloud computing, and data centers.

The enormous energy consumed by clouds becomes a significant challenge for cloud providers and smart grid operators. Due to performance concerns, applications typically run in different clouds located in multiple sites. In different clouds, many factors, including electricity prices, available servers, and task service rates, exhibit spatial variations. Therefore, it is important to manage and schedule tasks among multiple clouds in a high-quality-of-service and low-energy-cost manner. This work proposes a task scheduling method to jointly minimize energy cost and average task loss possibility (ATLP) of clouds. A problem is formulated and tackled with an adaptive biobjective differential evolution based on simulated annealing to determine a real-time and near-optimal set of solutions. A final knee solution is further chosen to specify suitable servers in clouds and task allocation among web portals. Simulation results based on realistic data prove that less average loss possibility of tasks, and smaller energy cost is obtained with it than its widely used peers.Note to Practitioners—This work considers joint optimization of both ATLP and average energy cost of all clouds. It is of great significance to execute tasks among multiple clouds by jointly allocating all tasks among multiple web portals and specifying suitable servers in different clouds. Yet, it is challenging to achieve joint optimization in a market where factors, including prices of electricity and available servers, show spatial variations. Current studies are coarse-grained and fail to jointly achieve average energy cost minimization and quality-of-service optimization of tasks. In this work, a novel algorithm named adaptive simulated-annealing-based biobjective differential evolution is proposed for an energy cost and quality-of-service-optimized task scheduling strategy in a real-time manner. Experiments prove that it realizes lower energy cost and ATLP compared with its typical widely used peers. It can also be applied to other industrial areas, including smart manufacturing, Internet of Things, and smart city.

Infrastructure in Distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.

A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users’ expectations on both aspects. To tackle this challenge, this work first formulates the problem into a constrained biobjective optimization problem. A biobjective algorithm, named simulated-annealing-based adaptive differential evolution (SADE), is presented to simultaneously reduce both the response time of tasks and energy cost. Meanwhile, a method of minimal Manhattan distance is adopted to search for a final knee, for achieving a good balance between response time minimization and energy cost reduction. Experimental results on real-life datasets, i.e., the electricity prices and tasks collected from a Google cluster trace, have proved that SADE yields less task response time and lower energy cost compared with state-of-the-art algorithms.

Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based Multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.

Accurate and reliable prediction of renewable energy is critical to the operation and optimization of resources in cloud data centers. It is also vital to reduce energy cost and harmful gas emission. However, it is highly challenging to achieve it due to unstable characteristics of renewable energy. Traditional prediction methods are mainly time series forecasting ones, and their prediction accuracy is unsatisfactory since they ignore spatial dependence in wind speed data. This work proposes a spatio-temporal prediction method to predict the wind speed data. It adopts a Savitzky-Golay filter to smooth the wind speed data to reduce the noise interference. It learns the spatial dependence through a graph convolutional network, and adopts a gated recurrent unit to extract temporal dependence of the wind speed data. In this way, this method effectively removes the noise and obtains temporal and spatial features of the wind speed data, thereby achieving better prediction accuracy. Experimental results demonstrate that the proposed approach outperforms other baseline peers by using real-world datasets.

Mobile Devices (MDs) support various delay/computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed, and its servers are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. This work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called Particle swarm optimization based on Genetic Learning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs and MBS, users’ connection to SBSs, MDs’ CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption.

Mobile edge computing (MEC) is an emerging architecture that supports computing, storage, and networking resources to users’ mobile devices (MDs). MDs have limited resources and energy capacity, and they have to offload some tasks of computational/delay-intensive applications to their nearby small base station (SBS), which is a paradigm of MEC. Although task offloading decreases energy consumed by MDs, it brings additional transmission delay among MDs and SBS/cloud data center (CDC), and processing delay in SBS and CDC. It is challenging to minimize the total cost of a heterogeneous system including MDs, an SBS and a CDC while strictly guaranteeing delay limits of tasks. This work adopts different queuing systems to analyze the performance of MDs, the SBS and the CDC in the heterogeneous MEC system. Then, this work formulates a constrained optimization problem to minimize the total cost of the system. To solve it, we design a hybrid method named Genetic Simulated-annealing-based Particle swarm optimization (GSP) integrating genetic operations of genetic algorithm and the Metropolis acceptance rule of simulated annealing into particle swarm optimization. Real-life data-based simulation results demonstrate that GSP’s solution outperforms several state-of-the-art algorithms in terms of total cost.

Smart mobile devices (SMDs) can meet users' high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.

The significant growth in the number and types of tasks of heterogeneous applications in green cloud data centers (GCDCs) dramatically increases their providers' revenue from users as well as energy consumption. It is a big challenge to maximize such revenue, while minimizing energy cost in a market where prices of electricity, availability of renewable power generation, and behind-the-meter renewable generation contract models differ among the geographical sites of the GCDCs. A multiobjective optimization method that investigates such spatial differences in the GCDCs is for the first time proposed to trade off such two objectives by cost-effectively executing all tasks while meeting their delay constraints. In each time slot, a constrained biobjective optimization problem is formulated and solved by an improved multiobjective evolutionary algorithm based on decomposition. Realistic data-based simulations prove that the proposed method achieves a larger total profit in faster convergence speed than the two state-of-the-art algorithms. Note to Practitioners-This article considers the tradeoff between profit maximization and energy cost minimization for the green cloud data center (GCDC) providers while meeting the delay constraints of all tasks. Current task-scheduling methods fail to take the advantage of spatial variations in many factors, e.g., prices of electricity and availability of renewable power generation at geographically distributed GCDC locations. As a result, they fail to execute all tasks of heterogeneous applications within their delay bounds in a high-revenue and low-energy-cost manner. In this article, a multiobjective optimization method that addresses the disadvantages of the existing methods is proposed. It is realized by a proposed intelligent optimization algorithm. Simulations demonstrate that in comparison with the two state-of-the-art scheduling algorithms, the proposed one increases the profit and reduces the convergence time. It can be readily implemented and integrated into actual industrial GCDCs.

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief network (GDBN) and an optimal controller. First, GDBN can automatically determine its size with transfer learning to achieve high performance in system identification, and it serves just as a predictive model of a controlled system. The model can accurately approximate the dynamics of the controlled system with a uniformly ultimately bounded error. Second, quadratic optimization is conducted to obtain an optimal controller. This work analyzes the convergence and stability of DeepMPC. Finally, the DeepMPC is used to model and control a second-order CSTR system. In the experiments, DeepMPC shows a better performance in modeling, tracking, and antidisturbance than the other state-of-the-art methods.

A growing number of global companies select Green Data Centers (GDCs) to manage their delay-constrained applications. The fast growth of users’ tasks dramatically increases the energy consumed by GDCs owned by a company, e.g., Google and Amazon. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GDCs. Therefore, hybrid cloud is widely employed to smartly outsource some tasks to public clouds. However, the temporal variation in many factors including revenue, price of power grid, solar irradiance, wind speed, price of public clouds makes it challenging to schedule all tasks of each application in a cost-effective way while strictly meeting their expected delay constraints. This work proposes a temporal task scheduling algorithm investigating the temporal variation in green hybrid cloud to schedule all tasks within their delay constraints. Besides, it explicitly presents a mathematical equation of service rates and task refusal. The maximization problem is formulated and tackled by the proposed hybrid optimization algorithm called Genetic Simulated-annealing-based particle swarm optimization. Trace-driven experiments demonstrate that larger profit are achieved than several existing scheduling algorithms.

A large number of services provided by cloud/edge computing systems have become the most important part of Internet services. In spite of their numerous benefits, cloud/edge providers face some challenging issues, e.g., inaccurate prediction of large-scale workload and resource usage traces. However, due to the complexity of cloud computing environments, workload and resource usage traces are highly-variable, thus making it difficult for traditional models to predict them accurately. Traditional models fail to deal with nonlinear characteristics and long-term memory dependencies. To solve this problem, this work proposes an integrated prediction method that combines Bi-directional and Grid Long Short-Term Memory network (BG-LSTM) models to predict workload and resource usage traces. In this method, workload and resource usage traces are first smoothed by a Savitzky-Golay filter to eliminate their extreme points and noise interference. Then, an integrated prediction model is established to achieve accurate prediction for highly-variable traces. Using real-world workload and resource usage traces from Google cloud data centers, we have conducted extensive experiments to show the effectiveness and adaptability of BG-LSTM for different traces. The performance results well demonstrate that BG-LSTM achieves better prediction results than some typical prediction methods for highly-variable real-world cloud systems.

Cloud computing is increasingly implemented by a growing number of organizations in recent years. Their critical business applications are deployed in distributed cloud data centers (CDCs) for fast response and low cost. The ever-increasing consumption of energy makes it highly important to schedule tasks efficiently in CDCs. In addition, many factors in CDCs, e.g., the wind and solar energy and prices of power grid have spatial differences. It becomes a challenging problem of how to achieve the energy cost minimization for CDCs in such a market. This work applies a G/G/1 queuing system to evaluate the optimization of servers in each CDC. Furthermore, a single-objective constrained optimization problem is given and addressed by a proposed Simulated-annealing-based Bees Algorithm to yield a close-to-optimal solution. Based on it, a Fine-grained Task Scheduling (FTS) algorithm is designed to minimize the energy cost of CDCs by intelligently scheduling heterogeneous tasks among distributed CDCs. In addition, it also determines running speeds of servers and the number of switched-on servers in each CDC while strictly meeting tasks' delay bounds. Realistic data-driven results demonstrate that FTS outperforms its typical benchmark scheduling peers in terms of energy cost and throughput.

As an emerging framework, edge computing achieves Internet of Things by providing computing, storage and network resources. It moves computation to edge devices located near users. Nevertheless, nodes in the edge often own limited resources and constrained energy capacities. It is impossible to entirely execute tasks in the edge due to their unsatisfied quality of service. Cloud data centers (CDCs) own almost unlimited resources yet they might cause large transmission delay and high resource cost. Consequently, it is highly needed to intelligently offload tasks between CDC and edge. This work proposes a task offloading algorithm for hybrid cloud-edge systems to achieve profit maximization of a system provider with response time bound assurance. It comprehensively investigates CPU, memory and bandwidth limits of nodes in the edge, and constraints of available energy and servers in CDC. These factors are integrated into a single-objective constrained optimization problem, which is solved by a simulated-annealing-based migrating birds optimization algorithm to yield a close-to-optimal offloading policy between CDC and the edge. Real-life data-driven experimental results show that its profit outperforms its four typical peers.

An increasing number of organizations choose distributed green data centers (DGDCs) and use their infrastructure resources to deploy and manage multiple applications that flexibly provide services to users around the world in a cost-effective way. The dramatic growth of tasks makes it highly challenging to maximize the total profit of a DGDC provider in a market, where the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs all vary with geographical sites. Different from existing studies, this paper designs a profit-sensitive spatial scheduling (PS3) approach to maximize the total profit of a DGDC provider by smartly scheduling all tasks of multiple applications to meet their response time constraints. PS3 can well utilize such spatial diversity of the above factors. In each time slot, the profit maximization for the DGDC provider is formulated as a constrained nonlinear program and solved by the proposed genetic-simulated-annealing-based particle swarm optimization. Real-life trace-driven simulation experiments demonstrate that PS3 realizes higher total profit and throughput than two typical task scheduling methods. Note to Practitioners-This paper investigates the profit maximization problem for a DGDC provider, while the average response time of all arriving tasks of each application is within their corresponding constraint. Existing task scheduling approaches fail to jointly consider the spatial variations in many factors, including the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs. Consequently, they cannot schedule all tasks of multiple applications within their response time constraints in a profit-sensitive way. In this paper, a profit-sensitive spatial scheduling (PS3) method that tackles the drawbacks of previous approaches is presented. It is achieved by adopting a proposed genetic-simulated-annealing-based particle swarm optimization algorithm that solves a constrained nonlinear program. Simulation experiments prove that compared with two typical scheduling approaches, it increases the total profit and throughput. It can be readily realized and incorporated into real-life industrial DGDCs. The future work should improve the proposed method by analyzing the indeterminacy in green energy and the uncertainty in tasks.

Deep belief network (DBN) is an efficient learning model for unknown data representation, especially nonlinear systems. However, it is extremely hard to design a satisfactory DBN with a robust structure because of traditional dense representation. In addition, backpropagation algorithm-based fine-tuning tends to yield poor performance since its ease of being trapped into local optima. In this article, we propose a novel DBN model based on adaptive sparse restricted Boltzmann machines (AS-RBM) and partial least square (PLS) regression fine-tuning, abbreviated as ARP-DBN, to obtain a more robust and accurate model than the existing ones. First, the adaptive learning step size is designed to accelerate an RBM training process, and two regularization terms are introduced into such a process to realize sparse representation. Second, initial weight derived from AS-RBM is further optimized via layer-by-layer PLS modeling starting from the output layer to input one. Third, we present the convergence and stability analysis of the proposed method. Finally, our approach is tested on Mackey-Glass time-series prediction, 2-D function approximation, and unknown system identification. Simulation results demonstrate that it has higher learning accuracy and faster learning speed. It can be used to build a more robust model than the existing ones.

With the high volume and velocity of scientific data produced on high-performance computing systems, it has become increasingly critical to improve the compression performance. Leveraging the general tolerance of reduced accuracy in applications, lossy compressors can achieve much higher compression ratios with a user-prescribed error bound. However, they are still far from satisfying the reduction requirements from applications. In this paper, we propose and evaluate the idea that data need to be preconditioned prior to compression, such that they can better match the design philosophies of a compressor. In particular, we aim to identify a reduced model that can be utilized to transform the original data to a more compressible form. We begin with a case study of Heat3d as a proof of concept, in which we demonstrate that a reduced model can indeed reside in the full model output, and can be utilized to improve compression ratios. We further explore more general dimension reduction techniques to extract the reduced model, including principal component analysis, singular value decomposition, and discrete wavelet transform. After preconditioning, the reduced model in conjunction with difference between the reduced model and full model is stored, which results in higher compression ratios. We evaluate the reduced models on nine scientific datasets, and the results show the effectiveness of our approaches.

The prediction of water quality has great significance for the management of water environment and the protection of water resources. Traditional water quality prediction methods are relatively simple, and linear models are often used to predict water quality. However, such models limit the accuracy of prediction and lack the analysis of nonlinear characteristics of water quality. In addition, due to the complex water environment, the water quality time series has large noise, which makes it difficult for traditional models to effectively predict water quality indicators under complex environmental conditions. To solve this problem, this work proposes an integrated prediction method that combines Savitzky-Golay filter with Long Short-Term Memory (LSTM)-based Encoder-Decoder neural network to predict water quality at the next time interval. In this approach, the water quality time series is first smoothed by Savitzky-Golay filter, and LSTM can extract valid information from complex time series. Based on them, an integrated model is for the first time established and can well characterize statistical characteristics. Experimental results demonstrate that it achieves better prediction results than some typical prediction methods.

A deep belief network (DBN) is one of the most effective ways to realize a deep learning technique, and has been attracting more and more attentions in nonlinear system modeling. However, it can not provide satisfactory results in learning speed and modeling accuracy, which is mainly caused by gradient diffusion. To address these problems and promote its development in cross-models, we propose an efficient DBN with a fuzzy neural network (DBFNN) for nonlinear system modeling. In this novel framework, DBN is considered as a pre-training technique to realize fast weight-initialization and to obtain a feature-representation vector. An FNN-based learning framework is developed for supervised modeling so as to eliminate the gradient diffusion issue, where its input happens to be the feature-representation vector. As a novel cross-model, DBFNN combines the advantages of both pre-training technique of DBN and an FNN model to improve nonlinear system modeling capability. A classical benchmark problem is used to demonstrate its superiority over existing single-models in learning speed and modeling accuracy.

Infrastructure in distributed cloud computing systems (DC2Ss) is concurrently shared by multiple different applications to flexibly and cost-effectively provide various services. It becomes a challenge to maximize the profit of DC2Ss due to the fact that Internet service provider (ISP) bandwidth price, availability of green energy, electricity price, and revenue brought by tasks differ from multiple sites. To solve it, this work designs a Location-Sensitive Resource optimization approach by considering spatial variations in DC2Ss to achieve profit maximization for DC2Ss by optimizing ISP and server resources. This work formulates the profit maximization problem as convex optimization and solved with the interior point method. Realistic data-based simulations prove that higher profit and throughput are achieved than two widely used approaches.

More and more large-scale enterprises choose distributed green clouds (DGCs) to cost-effectively deploy their applications. The significant increase of users' tasks makes it highly challenging to achieve profit maximization for a DGC provider under the fact that prices of power grid, revenues, and the amount of wind and solar energy in DGCs all change with different sites. This work develops a Profit-Aware Spatial Task Scheduling (PASTS) method for the profit maximization of a DGC provider. PASTS well investigates such spatial differences of these mentioned factors, and it smartly schedules tasks to meet their response time constraints. A nonlinear constrained program is designed and tackled by a hybrid meta-heuristic algorithm that combines particle swarm optimization with genetic mechanism and simulated annealing. Realistic data-based results prove that PASTS provides higher profit and throughput than two recent typical algorithms.

With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods.

A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. Note to Practitioners-Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.

The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods.

A growing number of organizations deploy multiple heterogeneous applications in infrastructures of distributed green data centers (DGDCs) to flexibly provide services to users around the world in a low-cost and high-quality way. The skyrocketing growth in types and number of heterogeneous applications dramatically increases the amount of energy consumed by DGDCs. The spatial and temporal variations in prices of power grid and availability of renewable energy make it highly challenging to minimize the energy cost of DGDC providers by intelligently scheduling arriving tasks of heterogeneous applications among GDCs while meeting their expected delay bound constraints. Unlike existing studies, this paper proposes a spatiotemporal task scheduling (STTS) algorithm to minimize energy cost by cost-effectively scheduling all arriving tasks to meet their delay bound constraints. STTS well investigates spatial and temporal variations in DGDCs. In each time slot, the energy cost minimization problem is formulated as a nonlinear constrained optimization one and addressed with the proposed genetic simulated-annealing-based particle swarm optimization. Trace-driven experiments show that STTS achieves larger throughput and lower energy cost than several typical task scheduling approaches while strictly meeting all tasks' delay bound constraints. Note to Practitioners-This paper investigates the energy cost minimization problem for a DGDC provider while meeting delay bound constraints for all arriving tasks. Previous scheduling methods do not jointly consider spatial and temporal variations in prices of power grid and availability of renewable energy in DGDCs. Therefore, they fail to adopt such variations to minimize the energy cost of a DGDC provider. In this paper, a new method that avoids disadvantages of previous methods is proposed. It is realized by adopting a hybrid metaheuristic algorithm named GSP to solve a nonlinear constrained optimization problem. Experimental results demonstrate that compared with several typical methods, it reduces energy cost and increases throughput. It can be readily integrated into realistic industrial DGDCs. The future work requires engineers to consider the effect of indeterminacy and uncertainty of green energy on scheduling methods.

Cost-effective task scheduling is an important operation in green infrastructure-as-a-service clouds (GICs) as the energy consumed by users' tasks is drastic. The irregular task arrival forces private GIC to adopt hybrid clouds to outsource some tasks to dynamic and reliable virtual machines (VMs) of public external clouds. However, temporal differences in revenue, electricity prices, wind and solar energy, and VM running prices of public external clouds make it difficult to dispatch all tasks in a cost-effective way while satisfying users' specified response time constraints. Unlike existing methods, we propose a multiqueue scheduling (MQS) method that investigates such temporal differences in hybrid GICs (HGICs). Specially, this work first gives mathematical relations between rejected tasks and service rates of servers in private GIC. In each iteration of MQS, this paper formulates a profit maximization problem for HGIC and solves it by a novel meta-heuristic optimization method by combing simulated annealing, particle swarm optimization, and genetic algorithm. Trace-driven experiments based on real-life data demonstrate that profit and throughput of MQS are larger than typical task scheduling algorithms while meeting tasks' response time constraints.

A growing number of global companies select Green Cloud Data Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. This work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed cloud data centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.

The infrastructure resources in distributed green data centers (DGDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGDC provider in a market where bandwidth price of Internet service providers (ISPs), electricity price and the availability of renewable green energy all vary with geographic locations. Unlike existing studies, a Geographical Scheduling method of Multi-Application Tasks (GSMAT) that exploits spatial diversity in DGDCs is proposed to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay bound constraints. In each time slot, the cost minimization problem for DGDCs is formulated as a constrained optimization one and solved by the proposed Simulated-annealing-based Bat Algorithm (SBA). Trace-driven experiments demonstrate that GSMAT achieves lower cost and higher throughput than two typical scheduling methods.

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks' delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a timeaware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks.

Nowadays many companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a data center provider to maximize its revenue by intelligently scheduling tasks in software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, thus may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. The core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. Comparing with state-of-the-art methods, the experimental results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-trip time of tasks of all applications.

A key factor of win–win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.

As cloud computing is becoming growingly popular, consumers' tasks around the world arrive in cloud data centers. A private cloud provider aims to achieve profit maximization by intelligently scheduling tasks while guaranteeing the service delay bound of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging problem of how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. Previous works usually provide an admission control to intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of a private cloud, and cause revenue loss. This paper studies the problem of how to maximize the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the temporal variation of prices in hybrid clouds. The temporal task scheduling provided by PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of PMA is solved by the proposed hybrid heuristic optimization algorithm, simulated annealing particle swarm optimization (SAPSO). Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation experiments demonstrate that the proposed method can greatly increase the throughput and the profit of a private cloud while guaranteeing the service delay bound.

The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

Currently, most web service recommendation studies concentrate on mining association patterns among services from historical compositions and recommending proper services based on patterns derived. However, latent negative patterns which indicate the inappropriate combinations of services, are mostly ignored. Therefore, by combining additional negative patterns with the already-exploited positive patterns in the large spares network of web services, we present a more comprehensive and accurate model for service recommendation. More specifically, we combine positive and negative composition patterns mined from service annotated tags. The extensive experiments conducted on a real-life dataset show that our method can outperform not only traditional APriori -based recommendation method but also Link Prediction-based one. The experiments on a synthetic dataset show that our method can also be effective to make recommendations in large-scale service network.

Multiple heterogeneous applications concurrently run in distributed cloud data centers (CDCs) for better performance and lower cost. There is a highly challenging problem of how to minimize the total cost of a CDCs provider in a market where the bandwidth and energy cost show geographical diversity. To solve the problem, this paper first proposes a revenue-based workload admission control method to judiciously admit requests by considering factors including priority, revenue and the expected response time. Then, this paper presents a cost-aware workload scheduling method to jointly optimize the number of active servers in each CDC, and the selection of Internet service providers for the CDCs provider. Finally, trace-driven simulation results demonstrate that the proposed methods can greatly reduce the total cost and increase the throughput of the CDCs provider in comparison to existing methods. Note to Practitioners-A cloud provider deploys its applications in geographically distributed CDCs to improve stability and reliability. For cost and performance, each CDC provides services through multiple ISPs that deliver traffic between millions of users and the CDCs provider. The geographical diversity of the bandwidth and energy cost brings the CDCs provider a big challenge of how to minimize the bandwidth and energy cost of the CDCs provider. This paper first proposes a revenue-based workload admission control method to selectively admit requests. Then, this paper proposes a cost-aware workload scheduling method to allocate requests among multiple available Internet service providers connecting to distributed CDCs. The scheduling strategy can intelligently dispatch requests, and achieve lower cost and higher throughput for the CDCs provider.

The balance between customer-perceived application performance and cloud provider's profit is a key to achieve win-win in cloud economy. Current researches on cloud resource allocation do not sufficiently address the issue of minimizing energy cost and maximizing revenue for various applications in virtualized cloud data center (VCDC). This paper presents a new approach to realize the optimization of VCDC's profit based on the service-level agreements between cloud providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines of different service classes. An analytic probabilistic model is then developed for non-equilibrium VCDC states. Next, a smart controller is proposed for fine-grained resource provisioning and sharing among multiple applications. A novel hybrid meta-heuristic algorithm based on simulated annealing and particle swarm optimization is developed to solve the formulated profit maximization problem. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Finally, the effectiveness of the proposed approach is validated with trace-driven simulation.

Cloud computing focuses on delivery of reliable, secure, sustainable, dynamic and scalable resources provisioning for hosting virtualized application services in shared cloud data centers. For an appropriate provisioning mechanism, we developed a novel cloud data center architecture based on virtualization mechanisms for multi-tier applications, so as to reduce provisioning overheads. Meanwhile, we proposed a novel dynamic provisioning technique and employed a flexible hybrid queueing model to determine the virtualized resources to provision to each tier of the virtualized application services. We further developed meta-heuristic solutions, which is according to different performance requirements of users from different levels. Simulation experiment results show that these proposed approaches can provide appropriate way to judiciously provision cloud data center resources, especially for improving the overall performance while effectively reducing the resource usage extra cost and maximizing the global profit of cloud infrastructure providers.

In the process of web service composition, the check and prevention of semantic incompatibility is one of the most important issues. In this paper, a controlled Petri net (CtlPN)-based model for web service composition is proposed. Meanwhile, the optimal controller is constructed, such that the appropriate vectors of controllable place and arc are appended in the key transition which can lead to deadlock states. In addition, for the semantic incompatibility case, a policy based on appending optimal controller is presented. It is proved that our policy can be a good solution. Finally, the proposed controller is transformed as the activity of BPEL.

Dynamic provisioning is a useful technique for handling the virtualized multi-tier applications in cloud environment. Understanding the performance of virtualized multi-tier applications is crucial for efficient cloud infrastructure management. In this paper, we present a novel dynamic provisioning technique for a cluster-based virtualized multi-tier application that employ a flexible hybrid queueing model to determine the number of virtual machines at each tier in a virtualized application. We present a cloud data center based on virtual machine to optimize resources provisioning. Using simulation experiments of three-tier application, we adopt an optimization model to minimize the total number of virtual machines while satisfying the customer average response time constraint and the request arrival rate constraint. Our experiments show that cloud data center resources can be allocated accurately with these techniques, and the extra cost can be effectively reduced.

The publication and discovery of web service is one of the most important issues in Service-oriented architecture. The common Universal Description, Discovery, and Integration (UDDI) still has some defects, such as register information can be invalidated since it is not able to update actively, its centralized approach leads to a high cost of server, and service providers can't delete register information in time when they stop to provide service. In this paper, first, a novel web service publication and discovery architecture based on JXTA is presented, which is applicable for internal enterprise. Then we make use of P2P network to publish and discover service, and propose distributed concurrent discovery mechanism to accelerate discovery speed. We also adopt a detector to discontinuously detect service, and update service information timely as well as remove unavailable service in the architecture. Finally, we prove that the proposed novel architecture can offer a good solution as well as can effectively resolve enterprise internal web service publication and discovery.