Maximum utilisation of minimal amount of resources is pivotal for achieving a sustainable operation in large-scale Cloud Data Centres. Prediction driven resource provisioning in Cloud Data Centres is a potential approach to execute Cloud workloads in a sustianable way. Traditional prediction models often struggle to deliver accurate predictions under dynamic and heterogeneous cloud workloads, as capturing long-range dependencies and sudden workload spikes is often challenging in Cloud environments. In addition, recent time series models such as the Adversarial Error Correction Generative Adversarial Network (AEC-GAN) characterise shortcomings when applied to cloud workload datasets, particularly whilst managing volatility and learning irregular patterns. To address such challenges, this paper proposes a novel prediction model using Data Augment Aware Transformer Rectification-based Generative Adversarial Networks (TR-GAN), which incorporates a continuous and conditional learning Transformer block in the GAN's generator module to serve both as a data distribution moderator and as a data augmentation generator, ultimately to deliver accurate predictions. TR-GAN is the first GAN-based model tailored for long-term cloud workload forecasting that explicitly couples data augmentation with sequence rectification. Unlike discriminative forecasters, Its generative formulation allows it to model the intrinsic variability and uncertainty of cloud workloads,generate context-aware synthetic data to improve generalization under sparse or irregular patterns, and iteratively refine predictions through an adversarial learning process, thereby reducing error accumulation in long-horizon forecasts. The prediction performance of the proposed model is evaluated with two widely-used cloud workload datasets, namely the Google clusters and Alibaba traces. Experimental results demonstrate that the proposed TR-GAN model can deliver a prediction improvement of around 15% than notable state-of-the-art models, including Informer, Autoformer and AEC-GAN, for long forecasting horizons.

Abstract Events are usually embedded in latent topics and the extraction of these latent topics are enabled by event detection algorithms. Unsupervised algorithms like Clustering algorithms are very useful for detecting events but with requirements which may not be relevant or easy to determine when using unstructured textual social media data. For instance, some algorithms are required to be used on specific data shapes, but determining the shape of an unstructured data may not be practical aside from the high level of noise in the data. Many of the existing algorithms work well with structured data, however, some of these algorithms can be adapted to unstructured data with the caveat that cluster formations may not contain consistent contextual information. We propose a novel Multi-Cycle Recursive Clustering Algorithm (MCRCA), able to sequentially eliminate noise, resulting in high homogeneous cluster formations. MCRCA does not require the initial specification of clusters numbers as the estimated number of clusters can be deduced at convergence. Our algorithm out-performs the classical LDA and K-Means algorithms in forming highly homogeneous clusters, context-wise.

The rapid growth of Internet of Vehicle (IoV) devices and Artificial Intelligence (AI) applications has accelerated the adoption of Cloud and Edge Computing. The advent of sixth-generation mobile communication technology (6G) further facilitates the deployment of Cloud-Edge collaborative computing in large-scale Intelligent Transportation Systems (ITS). Effective ITS must efficiently handle both latency-sensitive tasks (e.g., obstacle detection, traffic signal recognition) and computationally intensive tasks (e.g., path optimization, traffic flow prediction). However, existing Cloud-Edge collaborative frameworks struggle to accurately classify diverse workloads and provide efficient low-latency processing, leading to energy inefficiencies and task failures. To address these challenges, this paper introduces a Deep Learning-based Cloud-Edge Collaboration Framework (CECF) designed to optimize energy conservation in Cloud and Edge environments. CECF employs a DNN-based classifier to categorize workloads for processing in the Cloud or Edge. The classified tasks are managed by a dedicated Cloud scheduler (DSGA) and an Edge scheduler (EA-DFPSO), respectively. To enhance scheduling efficiency for highly variable Cloud tasks, DSGA incorporates a novel self-adaptive mutation algorithm and a random point fixed distance crossover method. Extensive evaluations using real-world workload traces demonstrate that CECF achieves up to a 8.5% improvement in system reliability and reduces energy consumption by 35.88% compared to baseline approaches.

SUMMARY The growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning.

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the Mt.Gox coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

Building upon pre-trained ViT models, many advanced methods have achieved significant success in COVID-19 classification. Many scholars pursue better performance by increasing model complexity and parameters. While these methods can enhance performance, they also require extensive computational resources and extended training times. Additionally, the persistent challenge of overfitting, due to limited COVID-19 dataset sizes, remains a hurdle. To address these challenges, we proposed a novel method to optimize pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks (SCNs), referred to as OPT-CO. We proposed two optimization methods: sequential optimization (SeOp) and parallel optimization (PaOp), by incorporating optimizers in a sequential and parallel manner, respectively. Our method can enhance model performance without necessitating a significant parameter expansion. Additionally, we introduced OPT-CO-SCN to avoid overfitting problems through the adoption of random projection for head augmentation. The experiments were carried out to evaluate the performance of our proposed model based on two publicly available datasets. Based on the evaluation results, our method achieved superior, performance surpassing other state-of-the-art methods.

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

Abstract Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber‐physical‐social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper‐connected smart sensing devices. As a part of future CPSS, online social networks are large‐scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state‐of‐the‐art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset.

Abstract With the development of edge computing and artificial intelligence (AI) technologies, edge devices are witnessed to generate data at unprecedented volume. The edge intelligence (EI) has led to the emergence of edge devices in various application domains. The EI can provide efficient services to delay‐sensitive applications, where the edge devices are deployed as edge nodes to host the majority of execution, which can effectively manage services and improve service discovery efficiency. The multilevel index model is a well‐known model used for indexing service, such a model is being introduced and optimized in the edge environments to efficiently services discovery while managing large volumes of data. However, effectively updating the multilevel index model by adding new services timely and precisely in the dynamic edge computing environments is still a challenge. Addressing this issue, this article proposes a designated key selection method to improve the efficiency of adding services in the multilevel index models. Our experimental results show that in the partial index and the full index of multilevel index model, our method reduces the service addition time by around 84% and 76%, respectively when compared with the original key selection method and by around 78% and 66%, respectively when compared with the random selection method. Our proposed method significantly improves the service addition efficiency in the multilevel index model, when compared with existing state‐of‐the‐art key selection methods, without compromising the service retrieval stability to any notable level.

E-commerce systems have become tremendously popular and important for modern business processes in the world of the digital economy. E-commerce business processes rely on the distributed and concurrent interaction process among Web applications of participants, such as clients, merchants, third-party payment platforms (TPPs), and bank systems. Such complex business interactions bridge the gap of trustiness among participants and introduce new security challenges in the form of logical vulnerabilities, which are prevalent in the business process at the application level. The most pressing challenge is to guarantee security throughout the checkout process at the conceptual design phase such that the logic errors can be detected before the actual implementation. Maintenance and repair of implemented e-commerce systems can be extremely costly. To this end, this article proposes a novel modeling and analyzing methodology for multiparticipants and multisessions e-commerce interaction processes based on colored Petri nets (CPNs). First, we define a novel model that can efficiently depict the key properties of e-commerce business interaction processes. Second, several modeling principles are formulated based on the design specification of e-commerce systems. Finally, the concept of Transaction-Logical Consistency is defined to analyze and verify the logical vulnerabilities of e-commerce systems. Through a discussed case study, we demonstrate the feasibility and applicability of the proposed methodology and its efficiency in detecting problems those can potentially lead to logical vulnerabilities.

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability.

Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

In the field of social computing, influence-based propagation only studies the maximized propagation of a single piece of information. However, in the actual network environment, there are more than one piece of competing information spreading in the network, and the information will influence each other in the process of spreading. This paper focuses on the problem of competitive propagation of multiple similar information, which considers the influence of communities on information propagation, and establishes overlapping interest communities based on label propagation. Based on users' interests and preferences, the influence probability between nodes of different types of information is calculated, and combining the characteristics of the community structure, the influence calculation method of nodes is proposed. Specifically, aiming at the shortcomings of strong randomness in existing overlapping community detection methods that are based on label propagation, this paper proposes the User Interest Overlapping Community Detection Algorithm based on Label Propagation (UICDLP). Furthermore, when the seed node set of competition information is known, this paper proposes the Influence Maximization Algorithm of Node Avoidance (IMNA). Finally, the experimental results verified that the proposed algorithms are effective and feasible.

Online social networks (OSNs) have emerged as a major platform for sharing information through social relationships and are one of the major sources of big data. Social networks can even accommodate sharing of live streaming data among the connected users. However, social information on social networks is often locally exploited rather than capturing the changes in the entire network over time. Obtaining user's influence statistics is limited only in their local vicinity, which may not facilitate capturing the changes in the user and post influences across the entire network, thereby resulting in lower accuracy while measuring user's topical influence. Moreover, low-influence users always exist in the network publishing low-quality posts. With the objectives of accurately capturing highly influential users and posts, this article proposes a novel dynamic social sensing model, named dynamic PageRank (DPRank) model, to evaluate the dynamic topical influence of the users of social information on social networks during the social information evolution. We deploy our proposed model to real-world Twitter data sets, which demonstrates the effectiveness of our proposed model against notable existing methods while identifying the true influence of users and posts in a dynamically evolving social network.

Since unmanned aerial vehicles (UAVs) can be controlled remotely in the absence of a unified means of identity authentication, they are quite vulnerable for illegal control by unidentifiable users. Only by tracing the identity of UAV itself, or providing management to pilots, current UAV identity authentication mechanism is far from achieving “single machine for single person.” With the development of artificial intelligence, it is possible to achieve automatic UAV identification. Therefore, this article proposes a behavior-based intelligent UAV identification and security supervision. Based on location tracking and flying data acquisition provided by the airborne black box, the UAV's behavioral data are collected on real time. Then, a reliable identification of UAVs is completed through the behavioral modeling, and a warning is issued in the potential illegal cases. It provides the government with intelligent control and disposal decision basis for flying UAVs.

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviors at the datacenters are being the focus of recent researches in the context of sustainable datacenters. But prediction analytics of cloud workloads suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-means based Rand Variable Learning Rate Backpropagation Neural Network (K-RVLBPNN) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and Backpropagation Neural Network (BPNN) algorithm. Experiments conducted on real-world Cloud datasets shows that the proposed model shows better prediction accuracy, outperforming the traditional Hidden Markov Model, Naïve Bayes Classifier, and our earlier RVLBPNN model, respectively.

Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.

The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively.

Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoT. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoT, which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the distributed multilevel (DM)-index model, for service maintenance and retrieval in the fog layer of industrial IoT to eliminate redundancy, narrow the search space, reduce both the number of traversed services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.

With the energy and power costs increasing alongside the growth of the IT infrastructures, achieving workload concentration and high availability in cloud computing environments is becoming more and more complex. Virtual machine (VM) migration has become an important approach to address this issue, particularly; live migration of the VMs across the physical servers facilitates dynamic workload scheduling of the cloud services as per the energy management requirements, and also reduces the downtime by allowing the migration of the running instances. However, migration is a complex process affected by several factors such as bandwidth availability, application workload and operating system configurations, which in turn increases the complications in predicting the migration time in order to negotiate the service-level agreements in a real datacenter. In this paper, we propose an adaptive approach named improved MIGration (iMIG), in which we characterize some of the key metrics of the live migration performance, and conduct several experiments to study the impacts of the investigated metrics on the Kernel-based VM (KVM) functionalities, as well as the energy consumed by both the destination and the source hosts. Our results reveal the importance of the configured parameters: speed limit, TCP buffer size and max downtime, along with the VM properties and also their corresponding impacts on the migration process. Improper setting of these parameters may either incur migration failures or causes excess energy consumption. We witness a few bugs in the existing Quick EMUlator (QEMU)/KVM parameter computation framework, which is one of most widely used KVM frameworks based on QEMU. Based on our observations, we develop an analytical model aimed at better predictions of both the migration time and the downtime, during the process of VM deployment. Finally, we implement a suite of profiling tools in the adaptive mechanism based on the qemu-kvm-0.12.5 version, and our experiment results prove the efficiency of our approach in improving the live migration performance. In comparison with the default migration approach, our approach achieves a 40% reduction in the migration latency and a 45% reduction in the energy consumption.