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Investigating, quantifying and controlling the co-location attack's conditional value at risk of VM placement strategies

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期149卷 464-477页

作者： Thabet, Marwa Hnich, Brahim Berrima, Mouhebeddine Univ Sousse ISITCom Sousse 4011 Tunisia Univ Monastir Fac Sci Monastir Tunisia Univ Sfax CES Lab ENIS Sfax Tunisia Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia

Co-location attacks jeopardize data privacy and performance of virtual machines (VMs) in cloud computing, which can result in unexpected monetary losses that are not supported or covered by the investments made by users and providers. The uncertainty about the VM type (attacker, target or neutral) exacerbates the difficulty of the problem. However, existing methods, particularly VM placement strategies, fail to capture the risk of significant losses. The measures of risk have a crucial role in coping with the losses that might be incurred by the involved parties in finance or the insurance industry. In this paper, we introduce the notion of risk to the co-location resistant VM placement strategies. We develop a novel framework that assesses the co-location attack risk of an assignment returned by a specific VM placement strategy. A thorough analysis of multiple existing VM placement algorithms highlights the significance of incorporating this measure as it reveals a discernible correlation pattern with respect to co-location attack coverage and resource consumption. Based on these findings, we propose a new Approximate Co-Location Resistant Placement Strategy with Risk Constraints (ACLRPS-RC) to protect the involved parties from overwhelming security losses. The simulations on CloudSim using real workloads show the ability of the new strategy to meet a given level of risk while maintaining an adequate number of used servers. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： optimization algorithms Cloud security Co -location attack Co-location-resistant assignment Conditional value at risk Virtual machines placement Heuristic methods

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On Centralized and Distributed Mirror Descent: Convergence Analysis Using Quadratic Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第5期68卷 3139-3146页

作者： Sun, Youbang Fazlyab, Mahyar Shahrampour, Shahin Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Mirror descent (MD) is a powerful first-order optimization technique that subsumes several optimization algorithms including gradient descent (GD). In this work, we leverage quadratic constraints and Lyapunov functions to analyze the stability and characterize the convergence rate of the MD algorithm as well as its distributed variant using semidefinite programming (SDP). For both algorithms, we consider both strongly convex and nonstrongly convex assumptions. For centralized MD and strongly convex problems, we construct an SDP that certifies exponential convergence rates and derive a closed-form feasible solution to the SDP that recovers the optimal rate of GD as a special case. We complement our analysis by providing an explicit $O(1/k)$ convergence rate for convex problems. Next, we analyze the convergence of distributed MD and characterize the rate numerically using an SDP whose dimensions are independent of the network size. To the best of our knowledge, the numerical rate of distributed MD has not been previously reported in the literature. We further prove an $O(1/k)$ convergence rate for distributed MD in the convex setting. Our numerical experiments on strongly convex problems indicate that our framework certifies superior convergence rates compared to the existing rates for distributed GD.

关键词： Convergence optimization Mirrors Lyapunov methods Euclidean distance Symmetric matrices Sun Convex optimization decentralized optimization mirror descent optimization algorithms quadratic constraints semidefinite programming

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DAMNet: Dual Attention Mechanism Deep Neural Network for Underwater Biological Image Classification

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IEEE ACCESS 2023年 11卷 6000-6009页

作者： Qu, Peixin Li, Tengfei Zhou, Ling Jin, Songlin Liang, Zheng Zhao, Wenyi Zhang, Weidong Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Peoples R China Anhui Univ Sch Internet Hefei 230039 Anhui Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

Due to the complex background and biodiversity of underwater biological images makes the identification of marine organisms difficult. To solve these above problems, we propose a dual attention mechanism deep neural network for underwater biological image classification (DAMNet). Firstly, tthe proposed DAMNet uses multi-stage stacking to suppress the complex underwater background, and the multiple stacking can reduce the number of parameters of the model and improve the generalization ability. Secondly, the dual attention mechanism module is combined with the improved reverse residual bottleneck based on deep convolution to extract the feature information of underwater biological images from space and channel aspects to obtain better discrimination and feature extraction capability. Finally, the gravity optimizer is selected to update the model weights, and the exponential translation can improve the model's convergence speed and learning rate. Extensive experiments on a dataset consisting of seven types of underwater biological images demonstrate that the DAMNet model has higher learning ability and robustness compared to the state-of-the-art methods. Our DAMNet model achieves 96.93% classification accuracy in all categories, which is at least a 2 percentage point improvement compared to other models.

关键词： Feature extraction Stacking Data mining Deep learning Transformers Training Convolution Underwater images attention mechanism neural networks reverse residuals optimization algorithms

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A novel analytical evaluation of the laboratory-measured mechanical properties of lightweight concrete

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第3期87卷 221-229页

作者： Sivakumar, S. Prakash, R. Srividhya, S. Vikram, A. S. Vijay PSNA Coll Engn & Technol Dept Civil Engn Dindigul 624622 India Govt Coll Engn Dept Civil Engn Tirunelveli 627007 India VaruvanVadivelan Inst Technol Dept Civil Engn Dharmapuri 636701 India Global Inst Engn & Technol Dept Civil Engn Vellore 632509 India

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash -based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm optimization (AFSO), Particle Swarm optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximu

关键词： box plot corrplot deflection Lytag lightweight aggregate mathematical modeling optimization algorithms

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Feature Extraction from Text using Grasshopper optimization algorithm for identifying hate speech

Feature Extraction from Text using Grasshopper optimization ...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Dhankhar, Anu Prakash, Amresh Juneja, Sapna Amity Institute of Biotechnology Amity University Gurugram India Kiet Group of Institutions Ghaziabad India

ISBN: (纸本)9798350344387

The optimization algorithms have ability to eliminate features that are extraneous and redundant, shrink the vector space, manage computation time, and enhance performance on problems requiring more accurate classification. Feature selection and feature extraction have always been crucial, especially in text categorization. Using optimization algorithms, engineering methods are used in these can be improved still further. Using five machine learning (ML) models - Decision Tree (DT), Naive Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) and Deep Neural Network (DNN) - this paper implements one such optimization algorithm, Grasshopper optimization (GOA), using various feature extraction and selection methods on textual datasets. The machine learning model performs more effectively due to the suggested feature selection and feature extraction strategies. In order to identify hate speech, this study takes into consideration text-based datasets. The positive, negative, and neutral sentiments of tweets are extracted from the Twitter API to create the text collection. On the application of GAO, Combining Random Forest with the method of feature extraction TF-IDF shows a greatest accuracy improvement of 24% as compared with without applied any optimization algorithm and 13% accuracy increased as compared with another optimization algorithm i.e., ACO. GOA provides an accuracy of up to 93%, which is superior to ACO optimization algorithm's 80% accuracy when used with the Random Forest model. © 2023 IEEE.

关键词： Grasshopper optimization algo hate speech machine learning optimization algorithms text-based

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optimization enabled deep learning methods for Container-based Cloud Computing Environment

Optimization enabled deep learning methods for Container-bas...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Indumathi, G. Sarala, R. Department of Computer Science and Engineering Puducherry Technological University Puducherry India

ISBN: (纸本)9798350305708

Cloud applications are now evolving towards a more granular micro-service paradigm, where fewer and simpler working elements are joined for providing end-to-end services, in response to the demand for agile development and administration. The increase in the utilization of container have gained more insight and helped in ensuring maximized and large portability with minimized overhead and rapid deployment in the cloud platform. But the rapid growth of container technology has introduced phenomenal changes in the management and automation of containers in the cloud computing environment. In specific, container resource allocation is the most potential challenge realized from the dimension of cloud providers since it possesses a direct impact on system performance and resource management. In this paper, a comprehensive review on optimization enabled deep learning methods for Container-based Cloud Computing Environment is presented with its merits and limitations. It has presented the possible number of deep learning models-based container scheduling process that helps in significant load prediction in the cloud platform. This study also outlines the prospective research areas that might be explored moving forward with this implementation research. © 2023 IEEE.

关键词： Cloud Computing Container-based micro-services Deep Learning Load Prediction optimization algorithms

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Sufficient Conditions for Convergent Recursive Extrapolation of qLPV Scheduling Parameters Along a Prediction Horizon

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第6期68卷 3182-3193页

作者： Morato, Marcelo Menezes Normey-Rico, Julio Elias Sename, Olivier Univ Fed Santa Catarina Dept Automat & Syst BR-88040900 Florianopolis Brazil Univ Grenoble Alpes CNRS Grenoble INP Inst EngnGIPSA Lab F-28000 Grenoble France

predictive control (MPC) algorithms have long been applied to nonlinear processes. In a quasi-linear parameter varying (qLPV) setting, nonlinearities are included into bounded scheduling parameters, which are given as a function of endogenous variables;these scheduling parameters are a priori unknown along a future prediction horizon, which complicates MPC design. To address this problem, the literature points out two options: robust MPC approaches, considering the scheduling to be uncertain;or suboptimal ones that set values for these parameters along the horizon. With respect to the latter group, this article proposes an extrapolation algorithm that estimates the future values of the qLPV scheduling parameters for a fixed prediction horizon of N steps;the method is based on a recursive procedure using simple Taylor expansions. Sufficient conditions for convergent extrapolation are presented with regard to the form and class of the scheduling function and the robustness of the MPC. Different benchmark examples from the literature are presented to illustrate the algorithm, which is also compared to state-of-the-art techniques.

关键词： Scheduling Trajectory Processor scheduling Prediction algorithms Extrapolation Real-time systems Computational modeling Estimation linear parameter-varying systems nonlinear predictive control optimization algorithms

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Using data-driven learning methodology for a solid waste-to-energy scheme and developed regression analyses for performance prediction

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 178卷 622-641页

作者： Peng, Li Alsenani, Theyab R. Li, Mingkui Lin, Haitao Sabeh, Hala Najwan Alturise, Fahad Alkhalifah, Tamim Alkhalaf, Salem Hassine, Siwar Ben Hadj Hunan City Univ Key Lab Energy Monitoring & Edge Comp Smart City Yiyang 413002 Peoples R China Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Elect Engn Al Kharj 11942 Saudi Arabia Yuxi Normal Univ Yuxi 653100 Yunnan Peoples R China Tishk Int Univ Fac Appl Sci Informat Technol Dept Erbil Iraq Qassim Univ Coll Sci & Arts Ar Rass Dept Comp Ar Rass Qassim Saudi Arabia King Khalid Univ Coll Sci & Arts Muhayel Dept Comp Sci Abha Saudi Arabia

Adopting innovative technologies like machine learning is crucial for achieving our sustainability goals. It has great potential for improving waste management and energy generation. The development of energy systems with machine learning algorithms is advancing rapidly. By conducting an in-depth comparison between the linear and non-linear regression models, this study makes a significant contribution to the field of waste-to energy systems. The focus of this research is to forecast the performance of a newly designed solid waste-to multi-generation energy system. This multi-generational energy system is designed to provide multiple outputs simultaneously, including power, heat, hydrogen, oxygen, and distilled water. In order to estimate the outputs of this system, both linear and non-linear algorithms are utilized and their respective performances are thoroughly analyzed and compared. The linear algorithms demonstrate notable precision through the creation of models that exhibit R-square values exceeding 96 %. In contrast, the non-linear algorithms demonstrate increased precision with R-square values surpassing 97 %, and even suggesting R-square values as impressive as 99 %, thereby attesting to the superior performance of these algorithms. Linear regression models are capable of providing predictions and identifying trends. However, non-linear regression models exhibit enhanced accuracy in predicting outcomes and are more efficient in capturing dynamic trends.

关键词： Waste-to-energy Regression models Waste management Poly-generation scheme Machine learning optimization algorithms

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optimization of Differential Evolution Algorithm Considering Soil Properties in the Remediation of Heavy Metal Pollution in Mining Soil 3

Optimization of Differential Evolution Algorithm Considering...

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3rd International Conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Huang, Chunxia Huang, Lixing Guangxi Minzu Normal University Chongzuo China Guangxi Vocational Institute of Technology Chongzuo China

ISBN: (纸本)9798350343922

Differential evolution algorithm is an optimization method that obtains the global optimal solution through random search without a clear goal. The article first took the remediation of heavy metal pollution in mining soil as an example to construct a mathematical model for minimizing the cost of soil heavy metal pollution remediation. Then, combined with differential evolution algorithm, the optimization was carried out by combining soil attributes with differential evolution algorithm, and compared with traditional genetic algorithm and particle swarm optimization algorithm. The experimental results showed that the differential evolution algorithm can better handle complex function optimization problems, resulting in a removal rate of up to 95%, a migration rate of up to 89%, and an ecological risk index of down to 3. Using differential evolution algorithm under fixed cost constraints for different soil properties can obtain optimal remediation solutions. Compared to traditional genetic algorithms and particle swarm optimization algorithms, differential evolution algorithms have better capabilities. © 2023 IEEE.

关键词： optimization algorithms

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Experimental Validation of Analytical Predictions for the "Deviant" Density of Oleo-Nanofluids Based on the nanoFin Effect

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ASME OPEN JOURNAL OF ENGINEERING 2024年 3卷 031014页

作者： Bhattacharya, Ritwik Lee, Jonghyun Sen, Anindito Banerjee, Debjyoti Texas A&M Univ Dept Mech Engn 3123 TAMU College Stn TX 77843 USA Texas A&M Univ Microscopy & Imaging Ctr MIC 3123 TAMU College Stn TX 77843 USA Texas A&M Univ J Mike Dept Mech Engn Walker 66 3123 TAMU College Stn TX 77843 USA

Experimental validation of the analytical predictions for the "deviant" density enhancement of nanofluids is presented in this study. Experimental measurements of density were complemented by transmission electron microscopy (TEM) of these nanofluid samples. The analytical model predicts the effective density of the nanofluid as a function of the mass fractions and densities of the nanoparticle, the base solvent, and the "compressed phase" of the solvent encapsulating the nanoparticle. In this study, casein is used as the nanoparticle candidate at a mass fraction of 1% and paraffin oil (which is essentially a phase change material (PCM)) is used as the neat solvent (base fluid). The experimental measurements demonstrate anomalous enhancement of the density of the oleo-nanofluids, which is 7% in excess of the value predicted by the conventional mixing rule (with a measurement uncertainty less than 1.2%). The formation of a compressed phase within the nanofluid domain is responsible for the "surplus" density (deviant density) of the oleo-nanofluid. The predicted values are found to closely match the actual dimensions of the nanostructures measured in the TEM images, hence conclusively validating the numerical model for estimating the magnitude of the deviant density contributed by the compressed phase. This also confirms the presence of a compressed phase (which is a consequence of the "nanoFin effect"), that was investigated and validated in this study. This surplus densification in hydrocarbons (such as PCM and oil-based nanofluids) makes them potentially attractive candidates as radiation shielding materials (e.g., for deep space exploration applications).

关键词： advanced energy systems simulation advanced manufacturing advanced materials and processing simulation advanced materials characterization aerospace heat transfer aerospace systems experimental heat transfer experimental research for nuclear safety experimental techniques experimental/measurement techniques experimentation in fluid mechanics and energetic extended surfaces extended surfaces/fins flows in biological systems flows in porous media fluid control systems fluid flow fluid mechanics/dynamics fluidics heat and mass transfer heat and mass transfer simulation heat energy generation/storage/transfer heat transfer and film cooling heat transfer augmentation techniques heat transfer enhancement hierarchical modeling liquid-solid low temperature heat transfer micro and nano systems design and synthesis of micro/nanoscale heat transfer microfluidics microfluidics and nanofluidics microscale fluid flow and heat and mass transfer microstructural size effect microstructure microstructure effect microstructure property relationships microsystems microtechnology microtexture miniaturization minimally invasive devices mixed convection multi-scale modeling multiphase flow multiphase flow characterization and analysis multiphase flows multiphysics modeling and simulation multi-scale modeling and simulation nano devices nano surface engineering nano systems nanoscale flows nanofluids nanofluids heat transfer nanomanufacturing nanomaterials nanomechanics nanoscale heat transfer nanostructure nanostructure (quantum dot nanowire) nanotechnolgy nanotechnology nanotribology Newtonian flows non-Fourier heat conduction non-newtonian flows nuclear safety and security numerical modeling of heat transfer and fluid flow optimization algorithms optimization and control optimization and design sensitivity analysis of non-linear systems petroleum petroleum engineering petroleum engineering simulation petroleum transport/pipelines/multiphase flow pharmaceuticals phase change materials phase transitions phase change and convective heat

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