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25th Annual Conference on Information Technology Education-SIGITE

作者： Banisakher, Mubarak Stewart, Shelly K. Hillsborough Commuity Coll Dept Comp Sci Plant City FL 33563 USA

ISBN: (纸本)9798400711060

This paper explores the "Closing the Cloud Computing Skills Gap" initiative, aimed at developing new programs in cloud computing to meet industry demands. The project involves creating a college credit certificate and an A.S. degree in Enterprise Cloud Computing, supported by an innovative online learning platform. Through collaboration with industry leaders and educational partners, the initiative seeks to enhance curriculum relevance, provide hands-on training, and foster a community of practice. Key accomplishments include curriculum development, high student engagement, and significant pass rates on certification exams.

关键词： cloudcomputing Skills Gap Curriculum Development Industry Collboration Workforce Development

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Cloud computing for integrated stochastic groundwater uncertainty analysis

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International Journal of Digital Earth 2013年第4期6卷 313-337页

作者： Yong Liu Alexander Y.Sun Keith Nelson Wesley E.Hipke National Center for Supercomputing Applications University of Illinois at Urbana-ChampaignUrbanaILUSA Bureau of Economic Geology University of Texas at AustinAustinTXUSA Arizona Department of Water Resources PhoenixAZUSA

One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely *** paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure *** technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the *** scientific use cases are presented in this paper using this service with significant computational *** case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping *** case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability *** of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.

关键词： MODFLOW groundwatersustainability uncertaintyanalysis global sensitivity analysis cloudcomputing WindowsAzure Dropbox alternative conceptual model cyberinfrastructure eScience

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QoS Recommendation in Cloud Services

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IEEE ACCESS 2017年 5卷 5171-5177页

作者： Zheng, Xianrong Xu, Li Da Chai, Sheng Old Dominion Univ Informat Technol & Decis Sci Dept Norfolk VA 23529 USA Queens Univ Sch Comp Kingston ON K7L 2N8 Canada

As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud services. The approach is used to predict both QoS ratings and rankings for cloud services. To evaluate the effectiveness of the approach, we conduct extensive simulations. Results show that the approach can achieve more reliable rankings, yet less accurate ratings, than a collaborative filtering approach using the Pearson coefficient.

关键词： cloudcomputing quality of service (QoS) recommender systems collaborative filtering

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Hadoop framework implementation and performance analysis on a cloud

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第2期25卷 705-716页

作者： Ozen, Goksu Zekiye Tekerek, Mehmet Sultanov, Rayimbek Kyrgyz Turkish Manas Univ Dept Comp Engn Bishkek Kyrgyzstan Kahramanmaras Sutcu Imam Univ Dept Comp Educ & Instruct Technol Kahramanmaras Turkey

The Hadoop framework uses the MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. Parameters such as distributing method over nodes, the number of job sheld in a parallel fashion, and the number of nodes in the cluster affect the execution time of jobs. The aim of this paper is to determine how the numbers of nodes, maps, and reduces affect the performance of the Hadoop framework in a cloud environment. For this purpose, tests were carried out on a Hadoop cluster with 10 nodes hosted in a cloud environment by running PiEstimator, Grep, Teragen, and Terasort benchmarking tools on it. These benchmarking tools available under the Hadoop framework are classified as CPU-intensive and CPU-light applications as a result of tests. In CPU-light applications, increasing the numbers of nodes, maps, and reduces does not improve the efficiency of these applications;they even cause an increase in time spent on jobs by using system resources unnecessarily. Therefore, in CPU-light applications, selecting the numbers of nodes, maps, and reduces as minimum is found as the optimization of time spent on a process. In CPU-intensive applications, according to the phase thats mall job pieces is processed, it is found that selecting the number of maps or reduces equal to total number of CPUs on a cluster is the optimization of time spent on a process

关键词： Bigdata Hadoop cloudcomputing MapReduce KVMvirtualizationenvironment benchmarkingtools Ganglia

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An Efficient and Privacy-Preserving Biometric Identification Scheme in Cloud Computing

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IEEE ACCESS 2018年 6卷 19025-19033页

作者： Zhu, Liehuang Zhang, Chuan Xu, Chang Liu, Ximeng Huang, Cheng Beijing Inst Technol Sch Comp Sci & Technol Beijing 10081 Peoples R China Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which, however, brings potential threats to users' privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric To execute a biometric identification, the database owner encrypts the query data and submits it to the cloud. The cloud performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates that the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with previous protocols, experimental results show that the proposed scheme achieves a better performance in both preparation and identification procedures.

关键词： Biometricidentification dataoutsourcing privacy-preserving cloudcomputing

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A secure and efficient public auditing system of cloud storage based on BLS signature and automatic blocker protocol

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第7期34卷 4008-4021页

作者： Jalil, Baidaa Abdulrahman Hasan, Taha Mohammed Mahmood, Ghassan Sabeeh Abed, Hazim Noman Univ Diyala Coll Sci Comp Sci Dept Diyala Iraq

Cloud computing is a model that enables users to store their data remotely and enjoy the applications and services provided by this model on demand through a common set of configurable computing resources, without the burden of local data storage as well as maintenance. However, users no longer have physical possession of external data, and this makes the task of protecting data integrity in cloud computing extre-mely important so that users can use the cloud storage as if it were local, without worrying about the need to verify its integrity. Hence, public cloud storage auditing is critical, so users can use a Third -party auditor (TPA) to verify data integrity. In order to provide effective TPA security, the audit should not achieves any security vulnerabilities to the privacy of user data and does not provide any additional online burden to the user. In this paper, we propose an effective public auditing system for cloud data based on the signature of Boneh-Lynn-Shacham (BLS), in order to ensure public auditing and maintain data privacy. The proposed system also realizes batch auditing and data dynamic process. In addition, the proposed system enhances the level of security authentication through an Automatic Blocker Protocol (ABP) to protect the system from unauthorized TPA. An extensive security and performance analysis shows that the proposed system is very secure and effective (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： cloudcomputing Publicauditing Privacypreserving Third-partyauditor

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Measuring the effect of monitoring on a cloud computing system by estimating the delay time of requests

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第7期34卷 3968-3972页

作者： Khalil, M. M. Khomonenko, A. D. Matushko, M. D. Univ Diyala Dept Comp Sci Diyala Iraq Emperor Alexander I St Petersburg State Transport Head Comp & Informat Syst St Petersburg Russia

Leveraging cloud computing services to improve the flexibility and scalability of the IT services delivered to end-users is becoming increasingly important. However, organizations using cloud computing services face many challenges: decreased visibility into the performance of services being delivered to their end -users. Organizations using cloud computing services need to have visibility not only into the performance of applications that have moved to the cloud, but also into the different computing resources on which these applications depend. The monitoring system monitors the running state of virtual machines, response time and throughput. By analysing this data, you can identify the causes of service delays. During the operation of the monitoring system, virtual machines are suspended, a service delay occurs in order to avoid this delay. Therefore in this paper, a cloud computing model is proposed that takes into account the time spent for monitoring, calculations and probabilities of delay and waiting time are made, blocking probabilities are also put to account and results are shown in diagrams and the effects are discussed.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： cloudcomputing Monitoring MSAzure AWS Loadbalancing Queuingtheory

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An Adaptive Auto-Configuration Tool for Hadoop 19

An Adaptive Auto-Configuration Tool for Hadoop

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19th International Conference on Engineering of Complex Computer Systems (ICECCS)

作者： Li, Changlong Zhuang, Hang Lu, Kun Sun, Mingming Zhou, Jinhong Dai, Dong Zhou, Xuehai Univ Sci & Technol China Comp Sci & Technol Hefei 230022 Anhui Peoples R China

ISBN: (纸本)9781479954827

With the coming concept of 'big data', the ability to handle large datasets has become a critical consideration for the success of industrial organizations such as Google, Amazon, Yahoo! and Facebook. As an important Cloud Computing framework for bulk data processing, Hadoop is widely used in these organizations. However, the performance of MapReduce is seriously limited by its stiff configuration strategy. Even for a single simple job in Hadoop, a large number of tuning parameters have to be set by users. This may easily lead to performance loss due to some misconfigurations. In this paper, we present an adaptive automatic configuration tool (AACT) for Hadoop to achieve performance optimization. To achieve this goal, we propose a mathematical model which will accurately learn the relationship between system performance and configuration parameters, then configure Hadoop system based on this mathematical model. With the help of AACT, Hadoop is able to adapt the hardware and software configurations dynamically and drive the system to an optimal configuration in acceptable time. Experimental results show its efficiency and adaptability, and that it is ten times faster compared with default configuration.

关键词： Benchmarktesting cloudcomputing Computers Hardware Mathematicalmodel Optimization Systemperformance Auto-Configuration Hadoop Self-Learning Computer hardware Computers Unsupervised learning SERPINA3 gene Mathematical Model optimal configuration Facebook

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Cloudqueue: An Internet-Scale Messaging Infrastructure Based on Hadoop

Cloudqueue: An Internet-Scale Messaging Infrastructure Based...

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7th International Conference on Computer Science and Education

作者： Shi, Xiaping Shi, Dongdong Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781467302395

With the development of the Internet of Things technology, more and more large-scale distributed Internet of Things applications emerge, to which system communication and collaboration is crucial. Traditional messaging middleware can not meet the development needs of Internet of Things. Based on open-source Hadoop cloud computing platforms, we designed a messaging middleware system: Cloudqueue. In the cloud computing era, it can provide high-performance, high reliability and large capacity of Internet-scale messaging service for the Internet of Things applications. Through the asynchronous communication, reliable transmission and cross-data center features provided by the Cloudqueue platform, the system integration of Internet of things applications and massive data processing will be more flexible and efficient.

关键词： cloudcomputing MOM Hadoop HBase ZooKeeper

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An Evolutionary Game Theoretic Approach for Configuring Cloud-integrated Body Sensor Networks 13

An Evolutionary Game Theoretic Approach for Configuring Clou...

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13th IEEE International Symposium on Network Computing and Applications (NCA)

作者： Yi Cheng-Ren Suzuki, Junichi Phan, Dung H. Omura, Shingo Hosoya, Ryuichi Univ Massachusetts Boston MA 02125 USA OGIS Int Inc San Mateo CA 94402 USA

ISBN: (纸本)9781479953936

This paper considers a cloud-integrated architecture for body sensor networks (BSNs), called Body-in-the-Cloud (BitC), and studies an evolutionary game theoretic approach to configure BSNs in an adaptive and stable manner. BitC allows BSNs to adapt their configurations (i.e., sensing intervals and sampling rates as well as data transmission intervals for nodes) to operational conditions (e.g., data request patterns) with respect to multiple conflicting objectives such as resource consumption and data yield. Moreover, BitC allows each BSN to perform an evolutionarily stable configuration strategy, which is an equilibrium solution under given operational conditions. Simulation results show that BitC effectively configures BSNs by seeking the trade-offs among conflicting objectives.

关键词： Bandwidth Datacommunication Energyconsumption Games Sensors Sociology Statistics cloudcomputing evolutionarygametheory virtualmachineplacement Body sensor networks Sociology Game theory Bandwidth BSN gene Sensors Operating conditions Hunting Game animals Resource utilization Evolutionary

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