Workload prediction is critical in enabling proactive resource management of cloud *** workload prediction is valuable for cloud users and providers as it can effectively guide many practices,such as performance assur...
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Workload prediction is critical in enabling proactive resource management of cloud *** workload prediction is valuable for cloud users and providers as it can effectively guide many practices,such as performance assurance,cost reduction,and energy consumption ***,cloud workload prediction is highly challenging due to the complexity and dynamics of workloads,and various solutions have been proposed to enhance the prediction *** paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature *** existing surveys,for the first time,we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective,i.e.,application-oriented rather than prediction methodologies per ***,we first introduce the basic features of workload prediction,and then analyze and categorize existing efforts based on two significant characteristics of cloud applications:variability and ***,we also investigate how workload prediction is applied to resource ***,open research opportunities in workload prediction are highlighted to foster further advancements.
In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensi...
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In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.
Accurate long-term traffic forecasting is crucial for urban traffic management and planning. Existing methods focus mainly on short-term predictions and struggle with long-term spatial-temporal dependencies due to com...
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Fiber materials are key materials that have changed human history and promoted the progress of human civilization. In ancient times, humans used feathers and animal skins for clothing, and later they widely employed n...
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Fiber materials are key materials that have changed human history and promoted the progress of human civilization. In ancient times, humans used feathers and animal skins for clothing, and later they widely employed natural fibers such as cotton, hemp, silk and wool to make fabrics(Fig. 1a). Chinese ancestors had mastered the art of natural fiber weaving as early as the Neolithic *** thousand years ago, people were already familiar with and adept at techniques for spinning natural fibers [1].
Evolving data streams containing concept drift breaks the assumption that data are independent and identically distributed (IID) in traditional machine learning models. A series of ensemble-based models have been adap...
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Petri Nets(PNs)are used for modeling and analyzing discreteevent systems,such as communication protocols,trafficsystems,human-computer interaction,and fault ***’state space explosion problem means that the state spac...
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Petri Nets(PNs)are used for modeling and analyzing discreteevent systems,such as communication protocols,trafficsystems,human-computer interaction,and fault ***’state space explosion problem means that the state spaceof PNs grows exponentially with PNs’*** thefundamental reachability problem is still an NP-Hard problemin *** has been proved that the equivalence problem forthe reachability sets of arbitrary PNs is undecidable except forsome subclass of PNs[1].That is,the reachability problem ofarbitrary PNs cannot be solved ***,there is noefficient and accurate algorithm to solve the problem.10172In recent years,with the emergence of big data and thedevelopment of computing hardware,a series ofbreakthroughs have been achieved in machine learning,suchas AlphaGo,AlphaFold,and ChatGPT[2−4].As a data-drivenapproach,machine learning can learn potential mappingrelationships between inputs and outputs from large-scaledata.
Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Ori...
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Spatial-temporal modeling considering the particularity of traffic data is a crucial part of traffic forecasting. Many methods take efforts into relatively independent time series modeling and spatial mining and then ...
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The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and th...
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The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.
Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positive...
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