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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

Procedia computer Science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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IoT-Edge-Cloud Computing Framework for QoS-Aware Computation Offloading in Autonomous Mobile Agents: Modeling and Simulation 1

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6th International Conference on Mobile, Secure and Programmable Networking, MSPN 2020

作者： Ismail, Leila Materwala, Huned Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

ISBN: (数字)9783030675509

ISBN: (纸本)9783030675493

Edge-cloud computing is an emerging computational model that allows offloading of service requests by the autonomous mobile agents from the edge-server to the cloud-server. This is to reduce the network latency prevalent in the cloud-IoT model. However, Quality-of-Service (QoS)-Aware computation offloading in a heterogeneous and dynamic edge-cloud environment remains an open problem. In this paper, we propose a queuing theory-based edge-cloud computing framework for QoS-aware offloading in mobile autonomous agents. This framework model decides whether to execute an incoming request to the edge-server on the edge itself or to offload to one of the heterogeneous cloud servers such that the request’s execution time is the minimum. To model a request’s execution time, we consider the processing capabilities and the queues overheads of the edge and cloud servers, and the edge-cloud communications’ time. The details of the evaluation results, using dataset generated from real-life scenarios, are presented in the paper. © 2021, Springer Nature Switzerland AG.

关键词： Quality of service

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers 6

Performance and energy-aware bi-objective tasks scheduling f...

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6th Information Systems International Conference, ISICO 2021

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021 The Authors. Published by Elsevier B.V.

关键词： Quality of service

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Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection

Semi-supervised stacked autoencoder-based deep hierarchical ...

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作者： Yuan, Chengsheng Chen, Xianyi Yu, Peipeng Meng, Ruohan Cheng, Weijin Wu, Q. M. Jonathan Sun, Xingming School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing210044 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are inherent human traits and neither easily revealed nor deciphered, can be used for real-time individual authentication systems. However, the main security issue of real-time fingerprint authentication systems is that most fingerprint scanners are vulnerable to presentation attacks by artificial replicas, made from plastic clay, gelatin, silicon, wood glue, etc. One anti-spoofing attack scheme, called real-time fingerprint liveness detection (RFLD), has been proposed to discriminate live or fake fingerprints. Currently, to resolve the presentation attacks, most RFLD solutions all relied on handcrafted feature extraction and selection. The features extracted by manual method are shallow features of the samples;however, autoencoder can automatically learn deep hierarchical semantic features representation of the samples, thus replacing the operations extracted with hand-designed features. In this paper, we apply stacked autoencoder to RFLD to significantly lower the work-force burden of the feature extraction engineering, and our model consists of two parts: parameter pre-training based on unsupervised learning and FLD based on supervised learning. The performance has been verified on two public fingerprint datasets: LivDet 2011 and 2013, and the experimental results indicate that our proposed approach works well for RFLD as well as the detection performance is satisfactory. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Opinion Mining-Based Conjoint Analysis of Consumer Brands 4th

Opinion Mining-Based Conjoint Analysis of Consumer Brands

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4th International Conference on Smart Computing and Informatics, SCI 2020

作者： Ravi, Kumar Priyadarshini, Aishwarya Ravi, Vadlamani IoTWoRKS HCL Technologies Ltd Plot 3A SEZ Noida Uttar Pradesh201301 India Department of Computer Science and Engineering IIIT Bhubaneswar Bhubaneswar751003 India Center of Excellence in Analytics Institute for Development and Research in Banking Technology Castle Hills Road No. 1 Masab Tank Hyderabad500057 India

ISBN: (纸本)9789811615016

Nowadays, online customer reviews in the form of feedback and ratings are available for a large number of product or service-based categories. These product reviews signify individually perceived strengths and shortcomings of a given product category, which can essentially provide design engineers and market researchers with invaluable insights into product design. In order to analyze the available individual customer opinions and turn them into aggregate consumer preferences that can be used in the product development and improvement process, we proposed a hybrid of sentiment analysis and conjoint analysis to determine the best brand among a list of brands for a specified product category. The outcome of this hybrid model is compared with the hybrid of sentiment analysis and multi-criteria decision making. The proposed framework estimates the relative effect of product attributes and brand names on the overall preference of consumers. We demonstrated the effectiveness of our approach using product review data taken from the literature. Our proposed approach turned out to be useful in determining the best of market availability. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Differential privacy for industrial internet of things: Opportunities, applications and challenges

arXiv

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arXiv 2021年

作者： Jiang, Bin Li, Jianqiang Yue, Guanghui Song, Houbing College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518000 China Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy. Copyright © 2021, The Authors. All rights reserved.

关键词： Data privacy

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Trusted anonymous data exchange: Threat sharing using blockchain technology

arXiv

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arXiv 2021年

作者： Allouche, Yair Tapas, Nachiket Longo, Francesco Shabtai, Asaf Wolfsthal, Yaron IBM Cyber Security Center of Excellence Beer-Sheva8410501 Israel Department of Engineering Universita' degli Studi di Messina Messina98166 Italy Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Cyber attacks are becoming more frequent and sophisticated, introducing significant challenges for organizations to protect their systems and data from threat actors. Today, threat actors are highly motivated, persistent, and well-founded and operate in a coordinated manner to commit a diversity of attacks using various sophisticated tactics, techniques, and procedures. Given the risks these threats present, it has become clear that organizations need to collaborate and share cyber threat information (CTI) and use it to improve their security posture. In this paper, we present TRADE - TRusted Anonymous Data Exchange - a collaborative, distributed, trusted, and anonymized CTI sharing platform based on blockchain technology. TRADE uses a blockchain-based access control framework designed to provide essential features and requirements to incentivize and encourage organizations to share threat intelligence information. In TRADE, organizations can fully control their data by defining sharing policies enforced by smart contracts used to control and manage CTI sharing in the network. TRADE allows organizations to preserve their anonymity while keeping organizations fully accountable for their action in the network. Finally, TRADE can be easily integrated within existing threat intelligence exchange protocols - such as trusted automated exchange of intelligence information (TAXII) and OpenDXL, thereby allowing a fast and smooth technology adaptation. Copyright © 2021, The Authors. All rights reserved.

关键词： Smart contract

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Ballistic vs. diffusive transport in metals

arXiv

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arXiv 2024年

作者： Sivan, Y. Un, I.W. Sarkar, S. School of Electrical and Computer engineering Ben-Gurion University of the Negev P.O. Box 653 8410501 Israel Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter School of Physics South China Normal University Guangzhou510006 China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou510006 China Department of Physics and Nanotechnology SRM Institute of Science and Technology Kattankulathur603 203 India

Using the Boltzmann transport model, we show that, somewhat unintuitively, ballistic transport of electrons in metals is weaker than diffusive transport. This happens because the femtosecond-scale collision rates of the non-thermal electrons makes their mean-free path negligible. Our predictions are correlated with various photoluminescence and nonlinear optics experimental examples both for Continuous Wave (CW) and pulsed illumination, and open the way to easy modelling of the non-thermal electron distributions in metal nanostructures of arbitrary complexity. © 2024, CC BY.

关键词： Photoluminescence

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An efficient artificial intelligence approach for early detection of cross-site scripting attacks

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Decision Analytics Journal 2024年 11卷

作者： Younas, Faizan Raza, Ali Thalji, Nisrean Abualigah, Laith Zitar, Raed Abu Jia, Heming Department of Computer Science & Information Technology The University Of Lahore Lahore 54000 Pakistan Department of Software Engineering The University Of Lahore Lahore 54000 Pakistan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi 38044 United Arab Emirates School of Information Engineering Sanming University Sanming 365004 China

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim's browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications. © 2024 The Author(s)

关键词： Artificial intelligence Cross-site scripting attacks Deep learning Feature engineering Feature fusion Machine learning

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Sensor Bias Fault Diagnosis for a Class of Nonlinear Uncertain Hybrid Systems

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IFAC-PapersOnLine 2020年第2期53卷 13662-13667页

作者： Constantinos Heracleous Christodoulos Keliris Christos G. Panayiotou Marios M. Polycarpou KIOS Research and Innovation Center of Excellence (CoE) and Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

This paper presents a sensor bias fault diagnosis approach for a class of hybrid systems with nonlinear uncertain discrete-time dynamics, measurement noise, and autonomous and controlled mode transitions. The proposed approach uses an observer based on a modified hybrid automaton framework and a fault detection scheme that employs a filtering method tighter mode-dependent thresholds for the detection of sensor faults (even with small magnitude). An autonomous guard events identification (AGEI) module is also developed and linked with both the fault detection scheme and the hybrid observer to eliminate any false alarms due to autonomous mode transitions and allow effective mode estimation. Finally, an adaptive sensor fault estimation scheme is included, which is activated once a fault is detected to isolate and estimate the sensor bias fault magnitude.

关键词： Hybrid systems fault diagnosis bias sensor faults filtering adaptive estimation

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