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Layer Dependency-Aware Learning Scheduling algorithms for Containers in Mobile Edge computing

IEEE TRANSACTIONS ON MOBILE computing 2023年第6期22卷 3444-3459页

作者： Tang, Zhiqing Lou, Jiong Jia, Weijia Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Beijing Normal Univ BNUZhuhai AI Res Inst Zhuhai 519087 Guangdong Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Guangdong Peoples R China Beijing Normal Univ BNU Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai 519087 Guangdong Peoples R China

Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

关键词： Task analysis Containers Image edge detection Scheduling Feature extraction Heuristic algorithms Scheduling algorithms Mobile edge computing dependency-aware scheduling container reinforcement learning

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Research on multi-objective scheduling of automated material transportation systems based on intelligent algorithms

Research on multi-objective scheduling of automated material...

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2023 international conference on Computer Application and Information Security, ICCAIS 2023

作者： Dai, Yunfeng Zhao, Feng Cui, Hongmei Wang, Yihua Wang, Yubing State Grid Yancheng Power Supply Company Jiangsu Yancheng China

ISBN: (纸本)9781510675032

Automated material transportation systems are an important component of modern intelligent algorithms, and their multi-objective scheduling management plays an important role in improving material transportation efficiency and reducing costs. This article combines a method based on big data and intelligent algorithms to apply to multi-objective scheduling in automated material transportation systems, and combines it with the material transportation system. Firstly, intelligent and robotic technologies are used for data processing and discovery in response to the massive data volume of material system transportation. Then the intelligent algorithm is applied to the material transportation scheduling, which uses genetic algorithm and Tabu search algorithm to achieve multi-objective scheduling. Finally, the application was validated in a practical material scenario system, and the experimental results showed that the sensors achieved 53%, the controllers achieved 49%, and real-time monitoring achieved 36% in material transportation. © 2024 SPIE.

关键词： Big data

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FairComp: 2nd international Workshop on Fairness and Robustness in Machine Learning for Ubiquitous computing 24

FairComp: 2nd International Workshop on Fairness and Robustn...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable Computers (UbiComp/ISWC)

作者： Meegahapola, Lakmal Spathis, Dimitris Constantinides, Marios Zhang, Han Yfantidou, Sofia van Berkel, Niels Dey, Anind K. Swiss Fed Inst Technol Zurich Switzerland Nokia Bell Labs Cambridge England Univ Washington Seattle WA USA Aristotle Univ Thessaloniki Thessaloniki Greece Aalborg Univ Aalborg Denmark Univ Cambridge Cambridge England

ISBN: (纸本)9798400710582

How can we ensure that Ubiquitous computing (UbiComp) research outcomes are ethical, fair, and robust? While fairness in machine learning (ML) has gained traction in recent years, it remains unexplored, or sometimes an afterthought, in the context of pervasive and ubiquitous computing. This workshop aims to discuss fairness in UbiComp research and its social, technical, and legal implications. From a social perspective, we will examine the relationship between fairness and UbiComp research and identify pathways to ensure that ubiquitous technologies do not cause harm or infringe on individual rights. From a technical perspective, we will initiate a discussion on model generalization and robustness, as well as data processing methods to develop bias mitigation approaches tailored to UbiComp research. From a legal perspective, we will examine how new policies shape our community's work and future research. Building on the success of the First FairComp Workshop(1) at UbiComp 2023, we have established a vibrant community centered around the topic of fair, robust, and trustworthy algorithms within UbiComp, while also charting a clear path for future research endeavors in this field.

关键词： fairness bias generalization robustness privacy discrimination responsible AI ethical AI Ubicomp Human-Computer Interaction

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Exploring the Efficiency of Text-Similarity Measures in Automated Resume Screening for Recruitment 10

Exploring the Efficiency of Text-Similarity Measures in Auto...

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17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Alsharef, Ahmad Sonia Nassour, Hasan Sharma, Jitender Yogananda School of AI Computer and Data Science Shoolini University Solan India School of Management Sciences and Liberal Arts Shoolini University Solan India

ISBN: (纸本)9789380544472

With many online recruitment portals requesting job applicants to upload their resumes, the automated process of screening and shortlisting candidates can accelerate selection and decision-making. This study explores the use of text similarity measures as an alternative to experienced human hiring managers in processing resumes. Three text similarity measures: Cosine, Sqrt-Cosine, and Improved Sqrt-Cosine (ISC) similarity were utilized as computer programs in scanning resumes for the recruitment of a business development manager and a software engineer. The decisions of the algorithms were compared to those of an expert hiring manager within the same scenarios. The findings indicate that ISC and Sqrt-Cosine were closer to the expert-human decision than Cosine similarity. These specific text-similarity algorithms can also make acceptable decisions even when recruiting for high-level positions and can do so in seconds when executed as a program on a normal CPU processor. This study suggests that these algorithms can efficiently facilitate the process of decision-making in recruitment and shortlisting candidates and can be an effective alternative to expert hiring managers. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Decision making

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A data processing System for Secondary School Students' Physical Fitness Test Based on data Mining Algorithm

A Data Processing System for Secondary School Students' Phys...

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2023 IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Xin, Changcheng Qidong Huilong Middle School Sports Group Jiangsu Nantong China Graduate School Jose Rizal University Mandaluyong Philippines

ISBN: (纸本)9798350398465

In recent years, the development of information technology and data mining algorithms has penetrated into all aspects of people's lives, and the design of a data processing system for senior high school physical fitness test can use computer technology to alleviate the difficulties and pressure of manual statistics. The purpose of this paper is to study the design of a data processing system for senior high students' physical fitness test based on data mining algorithms. Based on the needs of senior high school fitness tests, a senior high school physical fitness test data processing system was developed for the first time, which can import the test data into the database and introduce the PFT-OI algorithm. The experiment mainly compared the PFT-OI algorithm with k-mean clustering, and proved that the PFT-OI algorithm has better detection rate. © 2023 IEEE.

关键词： data mining

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An Overview of data Security algorithms in Cloud computing 3rd

An Overview of Data Security Algorithms in Cloud Computing

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3rd international conference on Applied Intelligence and Informatics (AII)

作者： Amalarethinam, D. I. George Rajakumari, S. Edel Josephine Bharathidasan Univ Jamal Mohamed Coll Autonomous Dept Comp Sci Tiruchirappalli 620020 Tamil Nadu India

ISBN: (纸本)9783031686382;9783031686399

Cloud computing is one of the current research areas in computer science. Recently, Cloud is the buzz word used everywhere in IT industries;It introduced the notion of 'pay as you use' and revolutionized developments in IT. The rapid growth of modernized cloud computing leads to 24 x 7 accessing of e-resources from anywhere at any time. It offers storage as a service where users' data can be stored on a cloud which is managed by a third party who is called Cloud Service Provider (CSP). Since users' data are managed by a third party, it must be encrypted ensuring confidentiality and privacy of the data. There are different types of cryptographic algorithms used for cloud security;in this article, the algorithms and their security measures are discussed.

关键词： cloud resources services applications security plaintext encryption key cipher text decryption

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Enhancing User Experiences: Evaluating Movie Recommendation algorithms in Dynamic Environments

Enhancing User Experiences: Evaluating Movie Recommendation ...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Fahad Iqbal, T. Gnanajeyaraman, R. Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha School of Engineering Department of CSE Chennai India

ISBN: (纸本)9798350319200

The abundance of content and services in the modern digital environment makes it difficult to give people the best recommendations. To address this, recommendation systems use machine learning and data analysis to generate personalised suggestions that improve user experiences and decision-making. This study looks into recommendation systems, focusing in particular on algorithms for movie recommendations. In order to identify the best user-centric recommendations, it closely examines six noteworthy methods, illuminating their usefulness in sifting through the profusion of options. This investigation helps the study improve recommendation system tactics for providing relevant and interesting content suggestions in a changing digital environment. © 2023 IEEE.

关键词： data analysis Movie recommendation algorithms Personalization Recommendation systems User experiences

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Survey on Big data and Cloud computing: Storage Challenges and Open Issues

Survey on Big Data and Cloud Computing: Storage Challenges a...

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2023 IEEE international conference on Communications, Information, Electronic and Energy Systems, CIEES 2023

作者： Megahed, Mohammed Ezzat Badry, Rasha M. Gaber, Sayed Abdel Fayoum University Faculty of Computers and Artificial Intelligence Information Systems Department Fayoum Egypt Helwan University Faculty of Computers and Artificial Intelligence Information Systems Department Helwan Egypt

ISBN: (纸本)9798350336917

The convergence of Big data (BD) and cloud computing has considered as a prominent topic in the IT field, driven by the production of vast amounts of data from diverse sources. Cloud computing is a storage and resource management technology, reduces the cost of storage requirements. The large sets of data, commonly referred to as BD, pose significant challenges in terms of processing and analysis. This study provides a comprehensive review of the utilization of cloud computing for BD processing by introducing BD definitions, main characteristics, and classifications alongside discussions on cloud computing. The research summarizes open research issues related to the integration of BD and cloud computing. © 2023 IEEE.

关键词： Big data

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Research and Design of Intelligent Classroom Technology Adoption Willingness Analysis Method and System Based on data Mining and Recommendation algorithms

Research and Design of Intelligent Classroom Technology Adop...

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2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Zheng, Yuqing Li, Rongna Chen, Hui Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China

ISBN: (纸本)9798350359244

This inquiry delves into the design and implementation of a system for analyzing the willingness to adopt intelligent classroom technology, grounded in data mining and recommendation algorithms. As information technology burgeons, intelligent classroom technology plays an increasingly pivotal role in contemporary education. However, fluctuation exists in educators' willingness to embrace these technologies. This study constructs an analytical system aimed at augmenting educators' adoption willingness. Employing an array of data mining techniques, such as cluster analysis and relationship mining, along with item-based collaborative filtering algorithms, it adeptly handles and scrutinizes a voluminous dataset of educator willingness. The findings reveal that these methodologies effectively identify key factors influencing educators' adoption willingness and offer tailored strategies and recommendations. The system's design emphasizes user-friendliness, real-time data updates, and stringent data security and privacy safeguards. Ultimately, this system not only enhances the adoption rates of educational technology but also significantly contributes to elevating the quality of education. © 2023 IEEE.

关键词： data mining

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Text Classification using Machine Learning algorithms on Encrypted data 9

Text Classification using Machine Learning Algorithms on Enc...

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9th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Behera, Sagarika Kolipakula, Nikhita Swetha, P. Yogish, Yogish Prathuri, Jhansi Rani Cmr Institute of Technology Dept. of Computer Sc. and Engineering Bengaluru India

ISBN: (纸本)9798350334395

Text classification is a fundamental activity in natural language processing (NLP), that includes classifying text resources into predetermined groups. With the growing concerns over data privacy and confidentiality, there is a need to perform text classification on encrypted data to protect sensitive information. In recent years, homomorphic encryption has emerged as a powerful technique that makes computations possible on encrypted data without compromising its confidentiality. This paper provides an overview of text classification using machine learning algorithms on encrypted data. In this research work, we have taken a data set of movie reviews. The data set is cleaned by removing stop words and performing stemming, lemmatization, and tokenization. We utilized a semantic model with inverse document frequency weighting and term frequency weighting to represent the data (TF-IDF). For the classification of the data set, we have applied six machine learning algorithms Logistic Regression, Naïve Bayes, KNN, Support Vector Machine, Random Forest, and Decision Tree. The plain text is encrypted using a somewhat homomorphic encryption technique called the RSA method. The ciphertext is subjected to the machine learning algorithms mentioned above. We have compared the results for accuracy. The Random Forest algorithm was found to give 75.56% accuracy compared to other algorithms on encrypted data. In this work, we have calculated the time taken by each algorithm for execution on plain text and ciphertext. It was observed that the Random Forest method took more time and the KNN method took less time comparatively. © 2023 IEEE.

关键词： Logistic regression

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