Microservice has been increasingly recognized as a promising architectural style for constructing large-scale cloud-based applications within and across organizational boundaries. This microservice-based architecture greatly increases application scalability, but meanwhile incurs an expensive performance overhead, which calls careful design of modeling task scheduling. However, these problems have thus far remained largely unexplored. In this paper, we develop prediction method independent...

Next-generation e-Science features large-scale, compute-intensive workflows of many computing modules that are typically executed in a distributed manner. With the recent emergence cloud and rapid deployment infrastructures, an increasing number scientific have been shifted or active transition to environments. As makes utility, scientists across different application domains facing same challenge reducing financial cost addition meeting traditional goal performance optimization. We develop...

Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of machinery equipment industrial environments. With the wide deployment multimodal sensors rapid development Internet Things (IoT), data generated modern production has become increasingly diverse complex. However, traditional methods for anomaly based on single source cannot fully utilize to capture anomalies systems. To address this challenge, we propose new model environments using temporal data....

Blockchain is a revolutionary technology that has reshaped the trust model among mutually distrustful peers in distributed network. While blockchain well-known for its initial usage public manner, such as cryptocurrency of Bitcoin, consortium blockchain, which requires authentication all involved participants, also been widely adopted various domains. Nevertheless, there lack comprehensive study terms architecture design, consensus mechanisms, comparative performance, etc. In this study, we...

Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose effective intrusion scheme based on deep learning techniques. The proposed employs a denoising autoencoder (DAE) with weighted loss function for feature selection, which determines limited number features to reduce dimensionality. selected data is then classified by compact multilayer perceptron (MLP) identification. Extensive experiments are conducted...

Device-free localization (DFL) plays an important role in many applications, such as wildlife population and migration tracking. Most of current DFL systems leverage the distorted received signal strength (RSS) changes to localize target(s). However, they assume a fixed distribution RSS change measurements, although are by different types targets. It inevitably causes fail if targets for modeling testing belong categories. This paper presents TLCS-a transferring compressive sensing based...

Next-generation e-Science features large-scale, compute-intensive workflows of many computing modules that are typically executed in a distributed manner. With the recent emergence cloud and rapid deployment infrastructures, an increasing number scientific have been shifted or active transition to environments. As makes utility, scientists across different application domains facing same challenge reducing financial cost addition meeting traditional goal performance optimization. We...

Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address limitations faced by centralized systems. While many existing efforts are successful in decentralization with multiple servers (i.e., full nodes) handle faulty nodes, an important issue arisen that external clients have rely on a relay node communicate the nodes blockchain. Compromization such may result security breach and even blockage IoT sensors from network....

Because of their proficiency in capturing the category characteristics graphs, graph neural networks have shown remarkable advantages for graph-level classification tasks, that is, rumor detection and anomaly detection. Due to manipulation special means (e.g. bots) on online media, rumors may spread across whole network at an overwhelming speed. Compared with normal information, popular usually a propagation structure, especially early stage information dissemination. More specifically,...

Recently, cross-border logistics has experienced rapid development. Cross-border courier orders come in various formats, featuring diverse layouts. Additionally, there is no standardized format for the writing of address and other information on these orders. It challenging current automated recognition models to handle such images. In this paper, we presented an end-to-end trainable neural network model based feature enhancement, SwFB, capable achieving conversion from raw images structured...

Cloud computing enables the delivery of computing, software, storage, and data access through web browsers as a metered service. In addition to commercial applications, an increasing number large-scale workflow-based scientific applications are being supported by cloud computing. order meet rapidly growing dynamic demands users, service provider needs employ efficient cost-effective job schedulers guarantee workflow completion time well improve resource utilization for high throughput. Based...

In the data mining of road networks, trajectory clustering moving objects is particular interest for its practical importance in many applications. Most existing approaches to this problem are based on distance measurement, and suffer from several performance limitations including inaccurate clustering, expensive computation, incompetency handle high dimensional data. This paper investigates complex network theory explores application networks address these issues. Specifically, we model a...

Density peaks clustering (DPC) algorithm is a novel density-based algorithm, which simple and efficient, not necessary to specify the number of clusters in advance, can find any nonspherical class clusters. However, DPC relies heavily on calculation methods cutoff distance threshold local density cannot analyze complex manifold data, especially datasets with uneven distribution multiple same cluster. To solve these problems, we propose an improved based layered k-nearest neighbors subcluster...

Gesture detection based on radio frequency signals has gained increasing popularity in recent years due to several benefits it brought, such as eliminating the need carry additional devices and providing better privacy. In traditional methods, significant breakthroughs have been made improve recognition accuracy scene robustness, but limited computing power of edge (the first-level equipment receive signals) requirement fast response for not adequately addressed. this article, we propose a...

The elastic resource provision, non-interfering sharing and flexible customized configuration provided by the Cloud infrastructure has shed light on efficient execution of many scientific applications. Due to increasing deployment data centers computer servers around globe escalated higher electricity price, energy cost running computing, communication cooling together with amount CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>...

Workflow techniques have been widely used as a major computing solution in many science domains. With the rapid deployment of cloud infrastructures around globe and economic benefit cloud-based storage services, an increasing number scientific workflows shifted or are active transition to clouds. As scale applications continues grow, it is now common deploy data-and network-intensive multi-cloud environments, where inter-cloud data transfer oftentimes plays significant role both workflow...

Radiation source detection is an important problem in homeland security-related applications. Deploying a network of detectors expected to provide improved due the combined, albeit dispersed, capture area multiple detectors. Recently, localization-based algorithms provided performance gains beyond simple "aggregated" as result localization being enabled by networked We propose following three approaches: 1) source-attractor radiation (SRD); 2) triangulation-based (TriRSD); and 3) ratio...

Convolutional neural networks have been widely used for image recognition as they are capable of extracting features with high accuracy. In this paper, we propose a DenseFood model based on densely connected convolutional network architecture, which consists multiple layers. A combination softmax loss and center is during the training process to minimize variation within same category maximize across different ones. For performance comparison, three models, namely, DenseFood, DenseNet121,...