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A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

computers, Materials & Continua 2023年第12期77卷 2791-2811页

作者： Laila Almutairi Shabnam Mohamed Aslam Department of computer Engineering Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia Department of information technology Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia

The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关键词： EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation

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Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

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Digital Communications and Networks 2022年第4期8卷 476-484页

作者： Zhanyang Xu Dawei Zhu Jinhui Chen Baohua Yu School of computer and Software Nanjing University of Information Science and TechnologyNanjingChina college of information science and technology Shihezi UniversityXinjiangChina

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT.By feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is improved.Generally,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology.The efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud infrastructure.Nevertheless,the execution efficiency of PW decreases due to inappropriate sub-task and data placement.In addition,the power consumption explodes due to massive data acquisition.To address these challenges,a PW placement method named PWP is devised.Specifically,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement strategies.The simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.

关键词： IoT-based power system Cloud Workflow Machine learning NSDE

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IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage

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Tsinghua science and technology 2024年第2期29卷 589-604页

作者： Shuang Xu Xiaojie Liu Dengao Li Jumin Zhao college of computer science and technology(college of Data science) Taiyuan University of TechnologyTaiyuan 030024China Pengcheng Laboratory Shenzhen 518055China college of information and computer Taiyuan University of TechnologyTaiyuan 030024China

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area.Herein,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground devices.In this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range.Then,we propose a hybrid deployment algorithm based on the improved quick artificial bee colony.The algorithm is composed of a centralized deployment algorithm and a distributed one.The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed.Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

关键词： wireless robotic networks network coverage deployment algorithm improved quick artificial bee colony

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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

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computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti Abdulkareem Alaloosy Haitham Khalaf Abdulkareem Alzahrani Duaa Al_Dosary Department of computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq college of Medicine University of AnbarRamadiIraq Department of computer science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia college of computer sciences and information technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

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How does working from home affect developer productivity?——A case study of Baidu during the COVID-19 pandemic

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science China(information sciences) 2022年第4期65卷 141-155页

作者： Lingfeng BAO Tao LI Xin XIA Kaiyu ZHU Hui LI Xiaohu YANG college of computer science and technology Zhejiang University Baidu Inc. Faculty of information technology Monash University

Nowadays, working from home(WFH) has become a popular work arrangement due to its many potential benefits for both companies and employees(e.g., increasing job satisfaction and retention of employees). Many previous studies have investigated the impact of WFH on the productivity of employees.However, most of these studies usually use a qualitative analysis method such as surveys and interviews,and the studied participants do not work from home for a long continuing time. Due to the outbreak of coronavirus disease 2019(COVID-19), a large number of companies asked their employees to work from home, which provides us an opportunity to investigate whether WFH affects their productivity. In this study, to investigate the difference in developer productivity between WFH and working onsite, we conduct a quantitative analysis based on a dataset of developers’ daily activities from Baidu Inc., one of the largest IT companies in China. In total, we collected approximately four thousand records of 139 developers’ activities of 138 working days. Out of these records, 1103 records are submitted when developers work from home due to the COVID-19 pandemic. We find that WFH has both positive and negative impacts on developer productivity in terms of different metrics, e.g., the number of builds/commits/code reviews. We also notice that WFH has different impacts on projects with different characteristics including programming language,project type/age/size. For example, WFH has a negative impact on developer productivity for large projects.Additionally, we find that productivity varies for different developers. Based on these findings, we get some feedback from developers of Baidu and understand some reasons why WFH has different impacts on developer productivity. We also conclude several implications for both companies and developers.

关键词： WFH developer productivity Developer Productivity

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Image-based 3D model retrieval using manifold learning

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Frontiers of information technology & Electronic Engineering 2018年第11期19卷 1397-1408页

作者： Pan-pan MU San-yuan ZHANG Yin ZHANG Xiu-zi YE Xiang PAN college of computer science and technology Zhejiang University college of Mathematics and information science Wenzhou University college of computer science and technology Zhejiang University of Technology

We propose a new framework for image-based three-dimensional(3D) model retrieval. We first model the query image as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite(SPD) matrix, which is a point on a Riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learning. To solve this heterogeneous matching problem, we map the Euclidean space and SPD Riemannian manifold to the same high-dimensional Hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn a metric in this Hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for image-based 3D model retrieval.

关键词： Model retrieval Euclidean space Riemannian manifold Hilbert space Metric learning

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of computer science and information technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of computer science University of Engineering and TechnologyMardan23200Pakistan EIAS Data science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical sciences.The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases.The use of IIF for detecting autoimmune diseases is widespread in different medical areas.Nearly 80 different types of autoimmune diseases have existed in various body parts.The IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)system.The data scientists conducted various research works using an automatic CAD system with low accuracy.The diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)approach.The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune diseases.The technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the body.In the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL.Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models.These models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,respectively.Therefore,DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases.The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts.Also,the proposed technique is used to detect autoimmune diseases with a minimal mar

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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Organizational Data Breach:Building Conscious Care Behavior in Incident Response

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computer Systems science & Engineering 2022年第2期40卷 505-515页

作者： Adlyn Adam Teoh Norjihan Binti Abdul Ghani Muneer Ahmad Nz Jhanjhi Mohammed A.Alzain Mehedi Masud Department of information Systems Faculty of Computer Science&Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia School of computer science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of information technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of computer science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident responses.This research study draws upon the literature in the areas of information security,incident response,theory of planned behaviour,and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour formation.The applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore’s history to-date,the 2018 SingHealth data breach.The single in-depth case study observed information security awareness,policy,experience,attitude,subjective norms,perceived behavioral control,threat appraisal and self-efficacy as emerging prominently in the framework’s applicability in incident handling.The data analysis did not support threat severity relationship with conscious care behaviour.The findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.

关键词： End user computing organizational behavior incident response data breach computer emergency response team cyber-attack

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Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

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computer Systems science & Engineering 2022年第3期40卷 913-926页

作者： Norjihan Binti Abdul Ghani Suraya Hamid Muneer Ahmad Younes Saadi N.Z.Jhanjhi Mohammed A.Alzain Mehedi Masud Department of information Systems Faculty of Computer Science&Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia School of computer science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of information technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of computer science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The world health organization(WHO)terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific treatment.Early and accurate detection of spread in affected regions can save precious lives.Despite the severity of the disease,a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams.However,the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data,as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are required.This research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache Hadoop.The method consists of two main parts:the first part collects data from social media using Apache Flume,while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache Hive.To overcome the noisy and unstructured nature of the data,the process of extracting information is characterized by pre and post-filtration phases.As a result,only with the integration of Flume and Hive with filtration and polarity analysis,can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social network.We introduce how the Apache Hadoop ecosystem–Flume and Hive–can provide a sentiment analysis capability by storing and processing large amounts of data.An important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.

关键词： Big data analysis data filtration text analysis sentiment analysis social media event detection

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Multi-dimensional information-driven many-objective software remodularization approach

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Frontiers of computer science 2023年第3期17卷 45-62页

作者： Amarjeet PRAJAPATI Anshu PARASHAR Amit RATHEE Department of computer science Engineering&information technology Jaypee Istitute of Information TechnologyNoida 201307India Department of computer science&Engineering Thapar Institute of Engineering&TechnologyPunjab 147004India Department of computer science Government College BarotaHaryana 131301India

Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality criteria.However,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software systems.Therefore,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization solution.Additionally,such modularization can be good from the quality metrics perspective but may not be acceptable to the developers.To produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization objectives.Further,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization solution.To assess the effectiveness of the proposed approach,we applied it over five software projects.The obtained remodularization solutions are evaluated with the software quality metrics and developers view of remodularization.Results demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.

关键词： software restructuring remodularization multiobjective optimization software coupling and cohesion

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