Journal articles: 'Cloud Computing Performance'

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Linthicum, David S. "Approaching Cloud Computing Performance." IEEE Cloud Computing 5, no. 2 (March 2018): 33–36. http://dx.doi.org/10.1109/mcc.2018.022171665.

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Mauch, Viktor, Marcel Kunze, and Marius Hillenbrand. "High performance cloud computing." Future Generation Computer Systems 29, no. 6 (August 2013): 1408–16. http://dx.doi.org/10.1016/j.future.2012.03.011.

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Aouat, Asmaa, El Abbassia Deba, Abou El Hassan Benyamina, and Djilali Benhamamouch. "Deployment in Cloud Computing." International Journal of Distributed Systems and Technologies 11, no. 1 (January 2020): 27–37. http://dx.doi.org/10.4018/ijdst.2020010103.

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Although clouds have adopted common communication protocols such as HTTP and SOAP, interoperability, integration, and coordination of all clouds remain a concern. Instead, companies are looking for solutions to deploy an infrastructure that spans multiple instances of public and private clouds. Each of the proposed cloud solutions has its own limitations, management APIs, and development cycles that must be monitored and managed to provide a consistent set. The objective of the article is to answer the question: Is there a platform to deploy, run and manage applications in a multi-cloud environment and to ensure their availability, performance, and optimal use of resources?

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Ahuja, Sanjay P., and Bhagavathi Kaza. "Performance Evaluation of Data Intensive Computing In the Cloud." International Journal of Cloud Applications and Computing 4, no. 2 (April 2014): 34–47. http://dx.doi.org/10.4018/ijcac.2014040103.

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Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.

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Pathak, Purvi, and Kumar R. "THE FEASIBILITY STUDY OF RUNNING HPC WORKLOADS ON COMPUTATIONAL CLOUDS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 445. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.20507.

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High-performance computing (HPC) applications require high-end computing systems, but not all scientists have access to such powerful systems. Cloud computing provides an opportunity to run these applications on the cloud without the requirement of investing in high-end parallel computing systems. We can analyze the performance of the HPC applications on private as well as public clouds. The performance of the workload on the cloud can be calculated using different benchmarking tools such as NAS parallel benchmarking and Rally. The workloads of HPC applications require use of many parallel computing systems to be run on a physical setup, but this facility is available on cloud computing environment without the need of investing in physical machines. We aim to analyze the ability of the cloud to perform well when running HPC workloads. We shall get the detailed performance of the cloud when running these applications on a private cloud and find the pros and cons of running HPC workloads on cloud environment.

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Alzakholi, Omar, Lailan Haji, Hanan Shukur, Rizgar Zebari, Shakir Abas, and Mohammad Sadeeq. "Comparison Among Cloud Technologies and Cloud Performance." Journal of Applied Science and Technology Trends 1, no. 2 (April 23, 2020): 40–47. http://dx.doi.org/10.38094/jastt1219.

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The cloud is the best method used for the utilization and organization of data. The cloud provides many resources for us via the internet. There are many technologies used in cloud computing systems; each one uses a different kind of protocols and methods. Many tasks can execute on different servers per second, which cannot execute on their computer. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros. Moreover, A comprehensive comparison of the tools used for the utilization of cloud computing systems is presented.

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Volkov, Aleksandr O. "EVALUATION OF CLOUD COMPUTING CLUSTER PERFORMANCE." T-Comm 14, no. 12 (2020): 72–79. http://dx.doi.org/10.36724/2072-8735-2020-14-12-72-79.

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For cloud service providers, one of the most relevant tasks is to maintain the required quality of service (QoS) at an acceptable level for customers. This condition complicates the work of providers, since now they need to not only manage their resources, but also provide the expected level of QoS for customers. All these factors require an accurate and well-adapted mechanism for analyzing the performance of the service provided. For the reasons stated above, the development of a model and algorithms for estimation the required resource is an urgent task that plays a significant role in cloud systems performance evaluation. In cloud systems, there is a serious variance in the requirements for the provided resource, as well as there is a need to quickly process incoming requests and maintain the proper level of quality of service – all of these factors cause difficulties for cloud providers. The proposed analytical model for processing requests for a cloud computing system in the Processor Sharing (PS) service mode allows us to solve emerging problems. In this work, the flow of service requests is described by the Poisson model, which is a special case of the Engset model. The proposed model and the results of its analysis can be used to evaluate the main characteristics of the performance of cloud systems.

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Zanoon, Nabeel. "Toward Cloud Computing: Security and Performance." International Journal on Cloud Computing: Services and Architecture 5, no. 5/6 (December 30, 2015): 17–26. http://dx.doi.org/10.5121/ijccsa.2015.5602.

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Addamani, Swapna, and Anirban Basu. "Performance Analysis of Cloud Computing Platform." International Journal of Applied Information Systems 4, no. 4 (October 10, 2012): 29–33. http://dx.doi.org/10.5120/ijais12-450697.

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Suakanto, Sinung. "Performance Measurement of Cloud Computing Services." International Journal on Cloud Computing: Services and Architecture 2, no. 2 (April 30, 2012): 9–20. http://dx.doi.org/10.5121/ijccsa.2012.2202.

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Marshall, Paul, Henry Tufo, and Kate Keahey. "High-performance computing and the cloud." XRDS: Crossroads, The ACM Magazine for Students 19, no. 3 (March 2013): 52–57. http://dx.doi.org/10.1145/2425676.2425692.

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Gentzsch, Wolfgang, Lucio Grandinetti, and Gerhard Joubert. "High performance computing in the cloud." Future Generation Computer Systems 29, no. 6 (August 2013): 1407. http://dx.doi.org/10.1016/j.future.2012.05.006.

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Qiang, Weizhong. "Performance and security in cloud computing." Journal of Supercomputing 75, no. 1 (January 2019): 1–3. http://dx.doi.org/10.1007/s11227-018-2671-4.

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Rani shaveta, Kumari, and Sangeeta Sharma. "PERFORMANCE ANALYSIS OF OPEN SOURCE STORAGE CLOUDS IN CLOUD COMPUTING." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (April 30, 2012): 43–48. http://dx.doi.org/10.24297/ijct.v2i1.2615.

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Cloud computing is one of the latest research area that helps in storing the information permanently on the servers and manages the different resources for the requested users to provide on-demand services. In order to create the more usable and economic value based cloud computing, the principles, goals and structure of the cloud engineering is of vital importance. The objective of this study is to analyze the CPU and memory performance of different open source clouds. We will use different open source cloud to measure the different performance metrics like CPU time for downloading and uploading of file, memory usage while downloading and uploading the file, standard deviation of CPU usage and standard deviation of memory usage.

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Chen, Chih-Yung, and Jih-Fu Tu. "A Novel Cloud Computing Algorithm of Security and Privacy." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/871430.

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The emergence of cloud computing has simplified the flow of large-scale deployment distributed system of software suppliers; when issuing respective application programs in a sharing clouds service to different user, the management of material becomes more complex. Therefore, in multitype clouds service of trust environment, when enterprises face cloud computing, what most worries is the issue of security, but individual users are worried whether the privacy material will have an outflow risk. This research has mainly analyzed several different construction patterns of cloud computing, and quite relevant case in the deployment construction security of cloud computing by fit and unfit quality, and proposed finally an optimization safe deployment construction of cloud computing and security mechanism of material protection calculating method, namely, Global Authentication Register System (GARS), to reduce cloud material outflow risk. We implemented a system simulation to test the GARS algorithm of availability, security and performance. By experimental data analysis, the solutions of cloud computing security, and privacy derived from the research can be effective protection in cloud information security. Moreover, we have proposed cloud computing in the information security-related proposals that would provide related units for the development of cloud computing security practice.

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Kohler, Jens, Kiril Simov, and Thomas Specht. "Analysis of the Join Performance in Vertically Distributed Cloud Databases." International Journal of Adaptive, Resilient and Autonomic Systems 6, no. 2 (July 2015): 65–87. http://dx.doi.org/10.4018/ijaras.2015070104.

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Cloud Computing becomes interesting for enterprises across all branches. Renting computing capabilities from external providers avoids initial investments, as only those resources have to be paid that were used eventually. Especially in the context of “Big Data” this pay-as-you-go accounting model is particularly important. The dynamically scalable resources from the Cloud enable enterprises to store or analyze these huge amounts of unstructured data without using their own hardware infrastructure. However, Cloud Computing is currently facing severe data security and protection issues. These challenges require new ways to store and analyze data, especially when huge data volumes with sensitive data are stored at external locations. The presented approach separates data on database table level into independent chunks and distributes them across several clouds. Hence, this work is a contribution to a more secure and resilient cloud architecture as multiple public and private cloud providers can be used independently to store data without losing data security and privacy constraints.

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AlZain, Mohammed A., Alice S. Li, Ben Soh, and Eric Pardede. "Multi-Cloud Data Management using Shamir's Secret Sharing and Quantum Byzantine Agreement Schemes." International Journal of Cloud Applications and Computing 5, no. 3 (July 2015): 35–52. http://dx.doi.org/10.4018/ijcac.2015070103.

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Cloud computing is a phenomenal distributed computing paradigm that provides flexible, low-cost on-demand data management to businesses. However, this so-called outsourcing of computing resources causes business data security and privacy concerns. Although various methods have been proposed to deal with these concerns, none of these relates to multi-clouds. This paper presents a practical data management model in a public and private multi-cloud environment. The proposed model BFT-MCDB incorporates Shamir's Secret Sharing approach and Quantum Byzantine Agreement protocol to improve trustworthiness and security of business data storage, without compromising performance. The performance evaluation is carried out using a cloud computing simulator called CloudSim. The experimental results show significantly better performance in terms of data storage and data retrieval compared to other common cloud cryptographic based models. The performance evaluation based on CloudSim experiments demonstrates the feasibility of the proposed multi-cloud data management model.

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Priyadarshinee, Pragati. "Cloud Computing Adoption." International Journal of Cloud Applications and Computing 8, no. 1 (January 2018): 97–116. http://dx.doi.org/10.4018/ijcac.2018010105.

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The purpose of this article is to develop a cloud computing adoption scale for Indian manufacturing, service, and process Industries. The scale development procedure has been followed from the previous studies and is refined further for clear understanding to adopt easily. In the first step, we have conducted a qualitative study for item selection. The second step includes pilot testing of 110 responses for measurement scale purification and finally, we are validating the scale with 660 sample respondents through convergent validity and discriminant validity. The initial result showed very poor threshold values for the items “Top management support” and “Marketplace establishment” which are having a strong literature support. This measurement scale will help managers to evaluate the level of cloud adoption to increase the business performance. The study is a first time attempt to develop a validated scale for cloud adoption that can be used in Indian Industries.

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Zheng, Wanbo, Yuandou Wang, Yunni Xia, Quanwang Wu, Lei Wu, Kunyin Guo, Weiling Li, Xin Luo, and Qingsheng Zhu. "On dynamic performance estimation of fault-prone Infrastructure-as-a-Service clouds." International Journal of Distributed Sensor Networks 13, no. 7 (July 2017): 155014771771851. http://dx.doi.org/10.1177/1550147717718514.

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The cloud computing paradigm enables elastic resources to be scaled at run time satisfy customers’ demand. Cloud computing provisions on-demand service to users based on a pay-as-you-go manner. This novel paradigm enables cloud users or tenant users to afford computational resources in the form of virtual machines as utilities, just like electricity, instead of paying for and building computing infrastructures by their own. Performance usually specified through service level agreement performance commitment of clouds is one of key research challenges and draws great research interests. Thus, performance issues of cloud infrastructures have been receiving considerable interest by both researchers and practitioners as a prominent activity for improving cloud quality. This work develops an analytical approach to dynamic performance modeling and trend prediction of fault-prone Infrastructure-as-a-Service clouds. The proposed analytical approach is based on a time-series and stochastic-process-based model. It is capable of predicting the expected system responsiveness and request rejection rate under variable load intensities, fault frequencies, multiplexing abilities, and instantiation processing times. A comparative study between theoretical and measured performance results through a real-world campus cloud is carried out to prove the correctness and accuracy of the proposed prediction approach.

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Et. al., Pavithra G,. "A Novel Watermarking and Re-Encryption Approach to Avoid Illegal Content Sharing In Cloud." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2603–9. http://dx.doi.org/10.17762/turcomat.v12i2.2229.

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Cloud computing is a set of information stored in a cloud, it can be accessed by the user whenever there is a need to access. Cloud computing is a combination of large interconnected computers. Importance of clod computing are storage and processing power to run application. Information can be access from anywhere .Data management is also one importance of the cloud computing. It improves performance. And also a lower infrastructure cost. Lower bandwidth does not support. Another important disadvantage is security thread. Cloud computing requires a constant internet connection. By using watermarking and re-encryption technique we can overcome the problem of security thread in the cloud. In this article we briefly discuss about the above mentioned technique for implementing to overcome the security thread in the cloud computing.

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Hassan, Jameela Abdulla, and Fahad Al-Dosari. "Cloudlet Networks Performance Analysis and Improvement." International Research Journal of Electronics and Computer Engineering 3, no. 2 (June 29, 2017): 22. http://dx.doi.org/10.24178/irjece.2017.3.2.22.

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Abstract— Cloud computing is a Participation in the process and storage operations across distant servers that are shared by many organizations and users and thus be transferred from an application to a service. The organization can share data over the Internet and user can pay only for the resources that will be used only. While cloud computing has disadvantages, there are some advantages for cloudlets have over cloud computing which include: lower network latency and users having full ownership of the data shared. When the need of data to be stored in the servers grows quickly, the workload in every resource will grow too. So, we need a load balancing algorithm and the load balancing is important issue in the cloud environment. Load balancing defined as a technique that divides the extra load equally across all the resources to ensure that no one resource overloaded. . So the performance of the cloud can be improved by having an excellent load balancing strategy. For that we will discuss the existing load balancing algorithms in cloud computing and propose algorithm to improve round robin algorithm by CloudAnalyst simulator based on a factor of response time and processing time and the proposed algorithm was found to be best in response time and processing time when we compare it with round robin algorithms. Index Terms— Cloud Computing, CloudAnalyst, Load Balance, Mobile Cloud Computing, Cloudlet Networks.

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Somula, Rama Subbareddy, and Sasikala R. "A Survey on Mobile Cloud Computing: Mobile Computing + Cloud Computing (MCC = MC + CC)." Scalable Computing: Practice and Experience 19, no. 4 (December 29, 2018): 309–37. http://dx.doi.org/10.12694/scpe.v19i4.1411.

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In recent years, the mobile devices become popular for communication and running advanced real time applications such as face reorganization and online games. Although, mobile devices advanced for providing significant benefits for mobile users. But still, these devices suffers with limited recourses such as computation power, battery and storage space due to the portable size. However, The Cloud Technology overcome the limitations of mobile computing with better performance and recourses. The cloud technology provides enough computing recourses to run mobile applications as storage computing power on cloud platform. Therefore, the novel technology called mobile cloud computing (MCC) is introduced by integrating two technologies (Mobile Computing, Cloud Computing) in order to overcome the limitations(such as Battery life, Storage capacity, Processing capacity) of Mobile Devices by offloading application to recourse rich Remote server. This paper presents an overview of MCC, the advantages of MCC, the related concepts and the technology beyond various offloading frameworks, the architecture of the MCC, Cloudlet technology, security and privacy issues and limitations of mobile cloud computing. Finally, we conclude with feature research directions in MCC.

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Firdhous, Mohamed, Suhaidi Hassan, and Osman Ghazali. "Statistically Enhanced Multi-Dimensional Trust Computing Mechanism for Cloud Computing." International Journal of Mobile Computing and Multimedia Communications 5, no. 2 (April 2013): 1–17. http://dx.doi.org/10.4018/jmcmc.2013040101.

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Quality of service plays an important role in distributed systems. Cloud computing has been the recent entrant to the distributed system market and has revolutionized computing by changing the way computing resources are accessed and paid for. Users can access cloud services and pay only for the usage similar to utilities. Trust computing systems can be employed for identifying the service providers quality in terms of their adherence to the committed in SLAs. Several trust computing mechanisms have been proposed in literature based on various algorithms and functions. Almost all of them are based on a single performance parameter and modify the trust scores monotonously when performance deviations are reported. This paper proposes a trust computing mechanism that statistically validates the attribute monitored before modifying the trust scores. Hence the proposed mechanism is protected from momentary fluctuations in system performances. The experiments conducted show that the trust scores computed using the proposed mechanism are more representative of the long term system performance than the ones that were computed without the validation of the inputs.

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Zhang, Yue Ling, Gang Xu, and Jiang Tao Wang. "An Implementation of High Performance Cloud Data Management." Applied Mechanics and Materials 614 (September 2014): 468–71. http://dx.doi.org/10.4028/www.scientific.net/amm.614.468.

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This paper is aimed to maximize computing resources and improve performances of CMEP by using REST style cloud management interfaces to operate data, using open authentication constructor to validate requests, designing schedule algorithms and distributing cloud computing based on executing and user’s purchase situation. Experiments showed that, high performance cloud data management would significantly shorten response time, expand erupt simultaneously count and scalability.

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Ahuja, Sanjay P., and Suganya Sridharan. "Performance Evaluation of Hypervisors for Cloud Computing." International Journal of Cloud Applications and Computing 2, no. 3 (July 2012): 26–67. http://dx.doi.org/10.4018/ijcac.2012070102.

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The virtualization of IT infrastructure enables consolidation and pooling of IT resources so they are shared over diverse applications to offset the limitation of shrinking resources and growing business needs. Virtualization provides a logical abstraction of physical computing resources and creates computing environments that are not restricted by physical configuration or implementation. Virtualization is very important for cloud computing because the delivery of services simplified by providing a platform for optimizing complex IT resources in a scalable manner, which makes cloud computing more cost effective. Hypervisor plays an important role in the virtualization of hardware. This software provides a virtual hardware to support running multiple operating systems concurrently using one physical server. This research quantitatively and qualitatively compares the performance of VMware ESXi 4.1, Citrix Systems Xen Server 5.6 and KVM Hypervisors using standard benchmark SPECvirt_sc2010v1.01 formulated by Standard Performance Evaluation Committee (SPEC), which generate various Workloads simulating real life situations.

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Man, X., S. Usui, S. Jayanti, L. Teo, and T. D. Marusich. "A High Performance Computing Cloud Computing Environment for Machining Simulations." Procedia CIRP 8 (2013): 57–62. http://dx.doi.org/10.1016/j.procir.2013.06.065.

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Zhang, Ruidong. "The impacts of cloud computing architecture on cloud service performance." Journal of Computer Information Systems 60, no. 2 (March 7, 2018): 166–74. http://dx.doi.org/10.1080/08874417.2018.1429957.

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Honarvar, Ali Reza. "Developing an Elastic Cloud Computing Application through Multi-Agent Systems." International Journal of Cloud Applications and Computing 3, no. 1 (January 2013): 58–64. http://dx.doi.org/10.4018/ijcac.2013010106.

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the integration of a multi-agent environment and a cloud platform is to the best of our knowledge a new concept in terms of elastic application to run on cloud platforms. Currently in literature can be found some works focused on merging cloud computing and multi-agent systems (MAS). These works focused on the using of clouds in MAS because MASs often require high-performance computing systems and large data storage devices, or using agents on cloud i.e. Providing agent-based solutions founded on the design and development of software agents for improving Cloud resources and service management and discovery. Scalability is an important issue in the successful deployment of cloud computing software since performance requirements can change over time. Therefore, it is often expected that the software can be scaled up or down to ensure the desired performance at minimal costs. In this paper a framework was proposed which can be used as a foundation for developing an application that uses resources elastically on the cloud. This framework which provides elastic resource provision to applications is based on the MAS.

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Liu, Li Xia, Gang Hu, Zhen Huang, and Yu Xing Peng. "White Cloud or Black Cloud: Opportunity and Challenge of Spectrum Sharing on Cloud Computing." Advanced Materials Research 430-432 (January 2012): 1290–93. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1290.

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In order to fully utilize the spectrum resource, dynamic spectrum access becomes a promising approach to increase the opportunity of spectrum access with the rapid development of cognitive radio. However the performance of cognitive radio networks (CRN) is considerably constrained by its limited power, memory and computing ability. Fortunately cloud computing, which is the highlight of current research, has the potential to make up for the disadvantages because of its vast storage and computing capacity. In this paper we will discuss the convergence of spectrum sharing and cloud computing from several aspects including model, advantages and challenges. A spectrum sharing model based on cloud computing (SSC) will be introduced.

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Khanghahi, Niloofar, and Reza Ravanmehr. "Cloud Computing Performance Evaluation: Issues and Challenges." International Journal on Cloud Computing: Services and Architecture 3, no. 5 (October 31, 2013): 29–41. http://dx.doi.org/10.5121/ijccsa.2013.3503.

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Krupa, B., T. Lesiak, T. Wojtoń, and L. Zawiejski. "Increased ILC Software Performance Using Cloud Computing." Acta Physica Polonica B 46, no. 7 (2015): 1337. http://dx.doi.org/10.5506/aphyspolb.46.1337.

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Kannadasan, R., N. Prabakaran, P. Boominathan, A. Krishnamoorthy, K. Naresh, and G. Sivashanmugam. "High Performance Parallel Computing with Cloud Technologies." Procedia Computer Science 132 (2018): 518–24. http://dx.doi.org/10.1016/j.procs.2018.05.004.

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Kumar Abhishek, Manish. "High Performance Computing using Containers in Cloud." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (August 25, 2020): 5686–90. http://dx.doi.org/10.30534/ijatcse/2020/220942020.

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Shengji, Yu, and Xiang Yanping. "Multi-Dimensional Performance Optimization in Cloud Computing." Journal of Computational and Theoretical Nanoscience 14, no. 1 (January 1, 2017): 335–40. http://dx.doi.org/10.1166/jctn.2017.6325.

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The challenge of multi-dimensional performance optimization has been extensively addressed in the literature based on deterministic parameters. Since resources in Cloud Computing platforms are geographically separated and heterogeneous, it is rather difficult to apply a uniform distribution algorithm for achieving various optimization goals. Based on the analysis of cloud service performance measures, this paper proposes an approach for optimal network resource distribution managed by the multi-agent system (MAS), which is aimed to satisfy both the users’ and the service providers’ requirements. Moreover, a communication algorithm that uses the universal generating function technique is proposed to obtain the service time distribution efficiently.

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Bu, Kai, Bin Xiao, and Yi Qian. "High performance and security in cloud computing." Concurrency and Computation: Practice and Experience 29, no. 19 (July 12, 2017): e4241. http://dx.doi.org/10.1002/cpe.4241.

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Bruque Cámara, Sebastián, José Moyano Fuentes, and Juan Manuel Maqueira Marín. "Cloud computing, Web 2.0, and operational performance." International Journal of Logistics Management 26, no. 3 (November 9, 2015): 426–58. http://dx.doi.org/10.1108/ijlm-07-2013-0085.

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Barakat, Osamah L., Shaiful J. Hashim, Raja Syamsul Azmir B. Raja Abdullah, Abdul Rahman Ramli, Fazirulhisyam Hashim, Khairulmizam Samsudin, and Mahmud Ab Rahman. "Malware analysis performance enhancement using cloud computing." Journal of Computer Virology and Hacking Techniques 10, no. 1 (June 19, 2013): 1–10. http://dx.doi.org/10.1007/s11416-013-0187-2.

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Mdhaffar, Afef, Riadh Ben Halima, Mohamed Jmaiel, and Bernd Freisleben. "Reactive performance monitoring of Cloud computing environments." Cluster Computing 20, no. 3 (November 10, 2016): 2465–77. http://dx.doi.org/10.1007/s10586-016-0676-4.

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Thandeeswaran, R., S. Subhashini, N. Jeyanthi, and M. A. Saleem Durai. "Secured Multi-Cloud Virtual Infrastructure with Improved Performance." Cybernetics and Information Technologies 12, no. 2 (June 1, 2012): 11–22. http://dx.doi.org/10.2478/cait-2012-0009.

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Abstract Cloud computing is a model where software applications and computing resources are accessed over Internet with minimal cost and effort by interacting with the service provider. Along with these benefits there are also some significant security concerns that need to be addressed for handling sensitive data and critical applications. The simultaneous use of multiple clouds can provide several potential benefits, such as high availability, fault tolerance and reduced infrastructural cost. The model proposed which is the implementation of a secured multi-cloud virtual infrastructure consists of a grid engine on top of the multi-cloud infrastructure to distribute the task among the worker nodes that are supplied with various resources from different clouds to enhance cost efficiency of the infrastructure set up and also to implement high availability feature. The Oracle grid engine is used to schedule the jobs to the worker nodes (in-house and cloud). Worker nodes will be acting like listeners to receive the job from the oracle grid engine master node. High security is provided at this point for data using AES algorithm and also a password protection key for privileged user’s access. Performance analysis, cost analysis and cost-performance ratio analysis are done by comparing different cluster configurations.

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Al-nsour, Sana'a, Haroun Alryalat, and Samer Alhawari. "Integration between Cloud Computing Benefits and Customer Relationship Management (CRM) Processes to Improve Organization's Performance." International Journal of Cloud Applications and Computing 4, no. 2 (April 2014): 73–86. http://dx.doi.org/10.4018/ijcac.2014040105.

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Due to the rapid growth in the electronic business, information technology has become very necessary for building up efficiency and effective transactions among organizations. Consequently, Cloud Computing (CC) becomes a very important area to achieve the organization's performance. The process of integration between Cloud Computing (CC) and the Customer Relationship Management (CRM) is a new research area. Therefore, scientific research and literature review related to Cloud Computing and CRM processes are limited. Yet, the impact of Cloud Computing on CRM processes remains under exploration and investigation. To this effect, this paper takes on a holistic approach that examines the combined effects of Cloud Computing Benefits (cost and ease of use) and CRM processes (customer acquisition, customer retention and customer expansion) on organization's performance. It also presents a conceptual framework for Cloud Computing and CRM processes, based on the analysis of various models presented in Cloud Computing and CRM processes. The main emphasis is focused on the concept of Cloud Computing Benefits (cost and ease of use) and CRM processes (customer acquisition, customer retention and customer expansion) by examining some reasons that make the organization interested in using this modern technology from one hand and how Cloud Computing can encourage organizations to implement CRM to improve their performance on the other. Therefore, this paper contributes to develop three models: Cloud Computing Benefits, CRM process, and integration between the Cloud Computing and CRM process to improve organization performance model. Also, it describes more valid factors to identify how an organization deals with its performance in the future by providing an advice for the Information Technology (IT) industry as how an analytical integration between Cloud Computing Benefits and CRM process improves organization's performance – something that should be taken into account in developing countries to attain a competitive advantage. This study is divided in three sections: Literature Review, Research Model, and Conclusion.

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Thaman, Jyoti, and Kamal Kumar. "Performance Aware Planning Algorithms for Cloud Environments." International Journal of Distributed Systems and Technologies 9, no. 1 (January 2018): 1–15. http://dx.doi.org/10.4018/ijdst.2018010101.

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For the last decade, cloud computing has been spreading its application base from the small enterprises to the large, from the domestic user to the professional, from buyers to sellers and from research to implementation. Subscribers submit their jobs or workflows for executions on clouds. Workflow scheduling is a very important aspect in cloud computing and it imitates industrial operations, constraints and dependencies. Several approaches such as Greedy, Heuristic, Meta-heuristic and Hybrid have been tried to reschedule workflows. This article proposes Modified HEFT (MHEFT) and Cluster Based Modified HEFT (C-MHEFT). MHEFT modifies the mapping of ranked tasks to the VMs. C-MHEFT is the cluster based extension of MHEFT. The simulations were performed in WorkflowSim and were compared with existing benchmarks in planning algorithms like HEFT and DHEFT. The proposed schemes will help industries, enterprises to model and sequence the Industrial process which will be faster and efficient.

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Al-nsour, Sana'a, Haroun Alryalat, and Samer Alhawari. "Integration between Cloud Computing Benefits and Customer Relationship Management (CRM) Processes to Improve Organization's Performance." International Journal of Cloud Applications and Computing 4, no. 1 (January 2014): 1–14. http://dx.doi.org/10.4018/ijcac.2014010101.

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Abstract:

Due to the rapid growth in the electronic business, information technology has become necessary for building efficiency and effective transactions among organizations. Consequently, Cloud Computing (CC) becomes a very important area to achieve the organization's performance. The process of integration between Cloud Computing (CC) and the Customer Relationship Management (CRM) is a new research area. Therefore, scientific research and literature review related to Cloud Computing and CRM processes are limited. This paper takes a holistic approach that examines the combined effects of Cloud Computing Benefits (cost and ease of use) and CRM processes (customer acquisition, customer retention and customer expansion) on organization's performance. It also presents a conceptual framework for Cloud Computing and CRM processes, based on the analysis of various models presented in Cloud Computing and CRM processes. The main emphasis is focused on the concept of Cloud Computing Benefits and CRM processes by examining some reasons that make the organization interested in using this modern technology from one hand and how Cloud Computing can encourage organizations to implement CRM to improve their performance on the other. This paper contributes to develop three models: Cloud Computing Benefits, CRM process, and integration between the Cloud Computing and CRM process to improve organization performance model. Also, it describes more valid factors to identify how an organization deals with its performance in the future by providing an advice for the Information Technology (IT) industry as how an analytical integration between Cloud Computing Benefits and CRM process improves organization's performance – something that should be taken into account in developing countries to attain a competitive advantage. This study is divided in three sections: Literature Review, Research Model, and Conclusion.

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Shrestha, Sujan, and Subarna Shakya. "A Comparative Performance Analysis of Fog-Based Smart Surveillance System." Journal of Trends in Computer Science and Smart Technology 2, no. 2 (May 13, 2020): 78–88. http://dx.doi.org/10.36548/jtcsst.2020.2.002.

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There has been increasing demand of security and safety in public as well as private places and hence surveillance system using IoT sensors, cameras has become the most important part in our daily life. This system has to operate all times 24/7/365 and thus produces huge amounts of data. Cloud computing offers storage, processing, and analytical services for handling of such massive amounts of data. For real time applications like smart surveillance system, increased latency from centralized Cloud computing is not acceptable. Fog Computing is an extension of Cloud computing, evolved to minimize latency. A Fog-Based Smart Surveillance System has been modelled and simulated in two environments as Cloud Only Network and Fog-Based Cloud Network using iFogsim. Various performance metrics like Application Loop Delay, Energy Consumption, Execution Cost, and Network Usage has been compared between Fog-Based Cloud Network and Cloud Only Network. Results showed that Fog-Based Cloud Network performs better than Cloud Only Network.

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Habbal, Adib, Siti Aminah Abdullah, Emmanuel O. C. Mkpojiogu, Suhaidi Hassan, and Nabil Benamar. "Assessing Experimental Private Cloud Using Web of System Performance Model." International Journal of Grid and High Performance Computing 9, no. 2 (April 2017): 21–35. http://dx.doi.org/10.4018/ijghpc.2017040102.

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Cloud computing has attracted the attention of educational and research institutions as a way to support modern trends in teaching and learning. This article describes the performance assessment of a private cloud within a university environment using the Web of System Performance (WOSP) model. A survey was carried out to measure the respondents' attitude towards the use of private cloud in which students and experts serve as sample. Testing was conducted by designing a virtual lab consisting of a number of virtual machines operated by a selected sample. The results showed that the usage of cloud computing in university has good perceived system performance judging from how it fares in the constituent parts of the WOSP model. Furthermore, the study revealed that usability and flexibility outperformed criterion like security. Moreover, several non-functional criteria outperformed functionality. In short, the knowledge and results presented from assessing a private cloud using WOSP model could be beneficial for users, designers and managers of private clouds especially in universities.

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Zhang, Hong Xia, and Xiao Dan Zhang. "Research on Cloud Computing-Based Virtual Logistics Enterprise Performance Management." Advanced Materials Research 989-994 (July 2014): 5357–60. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5357.

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Virtual Logistics Enterprise is a joint collaboration between multiple companies to adapt to changing market demands. These member companies have complementary resources and technologies. Cloud computing can provide dynamic resource for each member of virtual logistics enterprises mainly based on Internet-related services. Virtual Logistics Enterprise performance management can efficient and timely acquire the implementation of various companies performance objectives relying on cloud computing platform. Through cloud computing, corporate members can obtain real-time information on the implementation of the overall performance objectives virtual logistics enterprise. Virtual Logistics Enterprise Performance Management can also help member companies to improve their internal performance management. Cloud computing is efficient contact these dynamic virtual logistics enterprise members together. Virtual Logistics Enterprise member companies will be integrated in accordance with pre-set performance management objectives with the cloud computing.

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Chandana Kumari, P. ILA, S. Swarajya Laxmi, B. Rekha, B. Pravallika, G. Chaitanya, Syed Umar, and Azeem Mohammed Abdul. "VMM based Secured Virtualization in Cloud-Computing." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 18, 2016): 643. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp643-648.

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Cloud computing, utility computing, the future will be the main IT field. The government and companies are realizing that foreigners can clearly increase the cloud with minimal costs and maximum flexibility in the plant or existing equipment. But the clouds to ensure user privacy and digital asset management challenge in cloud client. Protection must always contain a performance. The complex is a sure way to end the occupation of the problem, the study suggests two Ebionitism security architecture. The movement and different approaches to health and safety and the level of the best things for their security costs hyper-visor layer to a reduction in order to avoid false alarms

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Chen, Bin, Zhi Jian Wang, Rong Zhi Qi, and Xin Lv. "Key Performance Information Collection Architecture Based on Cloud Computing." Applied Mechanics and Materials 509 (February 2014): 182–88. http://dx.doi.org/10.4028/www.scientific.net/amm.509.182.

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Cloud Computing has become another buzzword in recent years. Follow the popular research and use of the cloud system the performance become the bottleneck of the Newborn. More and more researches are turning their attention to analyze the performance of the cloud services. However, it is hard to extract accurate information from the different type of the cloud components such as datacenter, host, Virtual Machines (VM) in the cloud. Thus, it is significant to collect sufficient row data of the Cloud systems for the performance analysis. In this paper, we described an analysis framework to evaluate comprehensive performance guideline of cloud computing center. The analysis architecture is built based on the performance agent and server interface method (PASI), which consists of performance client (PMC), performance agent (PMA) and performance server (PMS), and we put forward a mathematical model based on the PASI information and queuing theory to forecast the idle rate and availability of the cloud environment. It is proved that the PASI architecture is correctly and effectively evaluates the performance of the cloud component and whole cloud environment.

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Shatil, Anwar S., Sohail Younas, Hossein Pourreza, and Chase R. Figley. "Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing." Magnetic Resonance Insights 8s1 (January 2015): MRI.S23558. http://dx.doi.org/10.4137/mri.s23558.

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With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.

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Hudaib, Amjad, and Layla Albdour. "Fog Computing to Serve the Internet of Things Applications." International Journal of Fog Computing 2, no. 2 (July 2019): 44–56. http://dx.doi.org/10.4018/ijfc.2019070103.

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Due to centralized nature for cloud computing and some other reasons, high mobility cannot be supported and low latency requirements for some applications such as Internet of Things (IoT) that require real time and mobility support. To satisfy such requirements new technologies, fog computing is a good solution, where we use edges of network for service provisioning instead of far datacenters allocated in clouds. Low latency response is the most attractive property for fog computing, which is very suitable for IoT multi-billion devices, sensors and actuators generates huge amount of data that need processing and analysis for smart decision generation. The main objective of this article is to show the super ability of fog computing over cloud-only computing. The authors present a patient monitoring system as a case study for simulation; they evaluated the performance of the system using: latency, network usage, power consumption, cost of execution and simulation execution time performance metrics. The results show that the Fog computing is superior over Cloud-only paradigm in all performance measurements.

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Redishettywar, Kavita, and Prof Rafik Juber Thekiya. "A REVIEW ON TASK ALLOCATION PERFORMANCE ISSUES IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 3 (May 23, 2017): 6225–32. http://dx.doi.org/10.24297/ijct.v16i3.6158.

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Cloud computing is an emerging paradigm in the computer industry where the computing is moved to a cloud of computers. It has become one of the buzz words of the industry. The core concept of cloud computing is, quite simply, that the vast computing resources that we need will reside somewhere out there in the cloud of computers and weâ€™ll connect to them and use them as and when needed. Computing can be described as any activity of using and/or developing computer hardware and software. It includes everything that sits in the bottom layer, i.e. everything from raw compute power to storage capabilities. Cloud computing ties together all these entities and delivers them as a single integrated entity under its own sophisticated management. Load balancing is a mechanism that distributes the dynamic workload equally across over the nodes or virtual machines within the whole cloud server to avoid a state of conflict wherever some virtual machines are measured as heavily loaded whereas others nodes or hosts are measured as idle or doing very little work. It helps to realize a high client satisfaction and resource utilization magnitude relation, consequently increasing the performance and resource utility of the system. It additionally makes sure that each computing resource in the cloud server is distributed with efficiently and fairly among all the requests of the client. It additionally prevents bottlenecks of the system which can occur because of load imbalance.

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Journal articles on the topic 'Cloud Computing Performance'

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