Relevant bibliographies by topics / Cloud Computing Performance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cloud Computing Performance'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Cloud Computing Performance"

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Al-Refai, Ali, and Srinivasreddy Pandiri. "Cloud Computing : Trends and Performance Issues." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3672.

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Context: Cloud Computing is a very fascinating concept these days, it is attracting so many organiza-tions to move their utilities and applications into a dedicated data centers, and so it can be accessed from the Internet. This allows the users to focus solely on their businesses while Cloud Computing providers handle the technology. Choosing a best provider is a challenge for organizations that are willing to step into the Cloud Computing world. A single cloud center generally could not deliver large scale of resources for the cloud tenants; therefore, multiple cloud centers need to collaborate to achieve some business goals and to provide the best possible services at lowest possible costs. How-ever a number of aspects, legal issues, challenges, and policies should be taken into consideration when moving our service into the Cloud environment. Objectives: The aim of this research is to identify and elaborate the major technical and strategy differences between the cloud-computing providers in order to enable the organizations managements, system designers and decision makers to have better insight into the strategies of the different Cloud Computing providers. It is also to understand the risks and challenges due to implementing Cloud Computing, and “how” those issues can be moderated. This study will try to define Multi-Cloud Computing by studying the pros and cons of this new domain. It is also aiming to study the concept of load balancing in the cloud in order to examine the performance over multiple cloud environments. Methods: In this master thesis a number of research methods are used, including the systematic litera-ture review, contacting experts from the relevant field (Interviews) and performing a quantitative methodology (Experiment). Results: Based on the findings of the Literature Review, Interviews and Experiment, we got out the results for the research questions as, 1) A comprehensive study for identifying and comparing the major Cloud Computing providers, 2) Addressing a list of impacts of Cloud Computing (legal aspects, trust and privacy). 3) Creating a definition for Multi-Cloud Computing and identifying the benefits and drawbacks, 4) Finding the performance results on the cloud environment by performing an expe-riment on a load balancing solution. Conclusions: Cloud Computing becomes a central interest for many organizations nowadays. More and more companies start to step into the Cloud Computing service technologies, Amazon, Google, Microsoft, SalesForce, and Rackspace are the top five major providers in the market today. However, there is no Cloud that is perfect for all services. The legal framework is very important for the protection of the user’s private data; it is an important key factor for the safety of the user’s personal and sensitive information. The privacy threats vary according to the nature of the cloud scenario, since some clouds and services might face a very low privacy threats compare to the others, the public cloud that is accessed through the Internet is one of the most means when it comes the increasing threats of the privacy concerns. Lack of visibility of the provider supply chain will lead to suspicion and ultimately distrust. The evolution of Cloud Computing shows that it is likely, in a near future, the so-called Cloud will be in fact a Multi-cloud environment composed of a mixture of private and public Clouds to form an adaptive environment. Load balancing in the Cloud Computing environment is different from the typical load balancing. The architecture of cloud load balancing is using a number of commodity servers to perform the load balancing. The performance of the cloud differs depending on the cloud’s location even for the same provider. HAProxy load balancer is showing positive effect on the cloud’s performance at high amount of load, the effect is unnoticed at lower amounts of load. These effects can vary depending on the location of the cloud.
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Mani, Sindhu. "Empirical Performance Analysis of High Performance Computing Benchmarks Across Variations in Cloud Computing." UNF Digital Commons, 2012. http://digitalcommons.unf.edu/etd/418.

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High Performance Computing (HPC) applications are data-intensive scientific software requiring significant CPU and data storage capabilities. Researchers have examined the performance of Amazon Elastic Compute Cloud (EC2) environment across several HPC benchmarks; however, an extensive HPC benchmark study and a comparison between Amazon EC2 and Windows Azure (Microsoft’s cloud computing platform), with metrics such as memory bandwidth, Input/Output (I/O) performance, and communication computational performance, are largely absent. The purpose of this study is to perform an exhaustive HPC benchmark comparison on EC2 and Windows Azure platforms. We implement existing benchmarks to evaluate and analyze performance of two public clouds spanning both IaaS and PaaS types. We use Amazon EC2 and Windows Azure as platforms for hosting HPC benchmarks with variations such as instance types, number of nodes, hardware and software. This is accomplished by running benchmarks including STREAM, IOR and NPB benchmarks on these platforms on varied number of nodes for small and medium instance types. These benchmarks measure the memory bandwidth, I/O performance, communication and computational performance. Benchmarking cloud platforms provides useful objective measures of their worthiness for HPC applications in addition to assessing their consistency and predictability in supporting them.
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Pelletingeas, Christophe. "Performance evaluation of virtualization with cloud computing." Thesis, Edinburgh Napier University, 2010. http://researchrepository.napier.ac.uk/Output/4010.

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Cloud computing has been the subject of many researches. Researches shows that cloud computing permit to reduce hardware cost, reduce the energy consumption and allow a more efficient use of servers. Nowadays lot of servers are used inefficiently because they are underutilized. The uses of cloud computing associate to virtualization have been a solution to the underutilisation of those servers. However the virtualization performances with cloud computing cannot offers performances equal to the native performances. The aim of this project was to study the performances of the virtualization with cloud computing. To be able to meet this aim it has been review at first the previous researches on this area. It has been outline the different types of cloud toolkit as well as the different ways available to virtualize machines. In addition to that it has been examined open source solutions available to implement a private cloud. The findings of the literature review have been used to realize the design of the different experiments and also in the choice the tools used to implement a private cloud. In the design and the implementation it has been setup experiment to evaluate the performances of public and private cloud. The results obtains through those experiments have outline the performances of public cloud and shows that the virtualization of Linux gives better performances than the virtualization of Windows. This is explained by the fact that Linux is using paravitualization while Windows is using HVM. The evaluation of performances on the private cloud has permitted the comparison of native performance with paravirtualization and HVM. It has been seen that paravirtualization hasperformances really close to the native performances contrary to HVM. Finally it hasbeen presented the cost of the different solutions and their advantages.
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Noureddine, Moustafa. "Enterprise adoption oriented cloud computing performance optimization." Thesis, University of East London, 2014. http://roar.uel.ac.uk/4026/.

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Cloud computing in the Enterprise has emerged as a new paradigm that brings both business opportunities and software engineering challenges. In Cloud computing, business participants such as service providers, enterprise solutions, and marketplace applications are required to adopt a Cloud architecture engineered for security and performance. One of the major hurdles of formal adoption of Cloud solutions in the enterprise is performance. Enterprise applications (e.g., SAP, SharePoint, Yammer, Lync Server, and Exchange Server) require a mechanism to predict and manage performance expectations in a secure way. This research addresses two areas of performance challenges: Capacity planning to ensure resources are provisioned in a way that meets requirements while minimizing total cost of ownership; and optimization to authentication protocols that enable enterprise applications to authenticate among each other and meet the performance requirements for enterprise servers, including third party marketplace applications. For the first set of optimizations, the theory was formulated using a stochastic process where multiple experiments were monitored and data collected over time. The results were then validated using a real-life enterprise product called Lync Server. The second set of optimizations was achieved by introducing provisioning steps to pre-establish trust among enterprise applications servers, the associated authorisation server, and the clients interested in access to protected resources. In this architecture, trust is provisioned and synchronized as a pre-requisite step 3 to authentication among all communicating entities in the authentication protocol and referral tokens are used to establish trust federation for marketplace applications across organizations. Various case studies and validation on commercially available products were used throughout the research to illustrate the concepts. Such performance optimizations have proved to help enterprise organizations meet their scalability requirements. Some of the work produced has been adopted by Microsoft and made available as a downloadable tool that was used by customers around the globe assisting them with Cloud adoption.
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Penmetsa, Jyothi Spandana. "AUTOMATION OF A CLOUD HOSTED APPLICATION : Performance, Automated Testing, Cloud Computing." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12849.

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Context: Software testing is the process of assessing quality of a software product to determine whether it matches with the existing requirements of the customer or not. Software testing is one of the “Verification and Validation,” or V&V, software practices. The two basic techniques of software testing are Black-box testing and White box testing. Black-box testing focuses solely on the outputs generated in response to the inputs supplied neglecting the internal components of the software. Whereas, White-box testing focuses on the internal mechanism of the software of any application. To explore the feasibility of black-box and white-box testing under a given set of conditions, a proper test automation framework needs to be deployed. Automation is deployed in order to reduce the manual effort and to perform testing continuously, thereby increasing the quality of the product. Objectives: In this research, cloud hosted application is automated using TestComplete tool. The objective of this thesis is to verify the functionality of cloud application such as test appliance library through automation and to measure the impact of the automation on release cycles of the organisation. Methods: Here automation is implemented using scrum methodology which is an agile development software process. Using scrum methodology, the product with working software can be delivered to the customers incrementally and empirically with updating functionalities in it. Test appliance library functionality is verified deploying testing device thereby keeping track of automatic software downloads into the testing device and licenses updating in the testing device. Results: Automation of test appliance functionality of cloud hosted application is made using TestComplete tool and impact of automation on release cycles is found reduced. Through automation of cloud hosted application, nearly 24% of reduction in level of release cycles can be observed thereby reducing the manual effort and increasing the quality of delivery. Conclusion: Automation of a cloud hosted application provides no manual effort thereby utilisation of time can be made effectively and application can be tested continuously increasing the efficiency and
AUTOMATION OF A CLOUD HOSTED APPLICATION
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Roloff, Eduardo. "Viability and performance of high-performance computing in the cloud." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/79594.

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Computação em nuvem é um novo paradigma, onde recursos computacionais são disponibilizados como serviços. Neste cenário, o usuário não tem a necessidade de adquirir infraestrutura, ele pode alugar os recursos de um provedor e usá-los durante um certo período de tempo. Além disso, o usuário pode facilmente alocar e desalocar quantos recursos ele desejar, num ambiente totalmente elástico. O usuário só é cobrado pelo efetivo uso que for feito dos recursos alocados, isso significa que ele somente pagará pelo que for utilizado. Por outro lado, usuários de processamento de alto desempenho (PAD) tem a necessidade de utilizar grande poder computacional como uma ferramenta de trabalho. Para se ter acesso a estes recursos, são necessários investimentos financeiros adequados para aquisição de sistemas para PAD. Mas, neste caso, duas situações podem incorrer em problemas. O usuário necessita ter acesso aos recursos financeiros totais para adquirir e manter um sistema para PAD, e esses recusros são limitados. O propósito dessa dissertação é avaliar se o paradigma de computação em nuvem é um ambiente viável para PAD, verificando se este modelo de computação tem a capaciodade de prover acesso a ambientes que podem ser utilizados para a execução de aplicações de alto desempenho, e também, se o custo benefício apresentado é melhor do que o de sistemas tradicionais. Para isso, todo o modelo de computação em nuvem foi avaliado para se identificar quais partes dele tem o potencial para ser usado para PAD. Os componentes identificados foram avaliados utilizando-se proeminentes provedores de computação em nuvem. Foram analisadas as capacidades de criação de ambientes de PAD, e tais ambientes tiveram seu desempenho analisado através da utilização de técnicas tradicionais. Para a avaliação do custo benefício, foi criado e aplicado um modelo de custo. Os resultados mostraram que todos os provedores analisados possuem a capacidade de criação de ambientes de PAD. Em termos de desempenho, houveram alguns casos em que os provedores de computação em nuvem foram melhores do que um sistema tradicional. Na perspectiva de custo, a nuvem apresenta uma alternativa bastante interessante devido ao seu modelo de cobrança de acordo com o uso. Como conclusão dessa dissertação, foi mostrado que a computação em nuvem pode ser utilizada como uma alternativa real para ambientes de PAD.
Cloud computing is a new paradigm, where computational resources are offered as services. In this context, the user does not need to buy infrastructure, the resources can be rented from a provider and used for a period of time. Furthermore the user can easily allocate as many resources as needed, and deallocate them as well, in a totally elastic environment. The resources need to be paid only for the effective usage time. On the other hand, High-Performance Computing (HPC) requires a large amount of computational power. To acquire systems capable for HPC, large financial investments are necessary. Apart from the initial investment, the user must pay the maintenance costs, and has only limited computational resources. To overcome these issues, this thesis aims to evaluate the cloud computing paradigm as a candidate environment for HPC. We analyze the efforts and challenges for porting and deploy HPC applications to the cloud. We evaluate if this computing model can provide sufficient capacities for running HPC applications, and compare its cost efficiency to traditional HPC systems, such as clusters. The cloud computing paradigm was analyzed to identify which models have the potential to be used for HPC purposes. The identified models were then evaluated using major cloud providers, Microsoft Windows Azure, Amazon EC2 and Rackspace and compare them to a traditional HPC system. We analyzed the capabilities to create HPC environments, and evaluated their performance. For the evaluation of the cost efficiency, we developed an economic model. The results show that all the evaluated providers have the capability to create HPC environments. In terms of performance, there are some cases where cloud providers present a better performance than the traditional system. From the cost perspective, the cloud presents an interesting alternative due to the pay-per-use model. Summarizing the results, this dissertation shows that cloud computing can be used as a realistic alternative for HPC environments.
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Sridharan, Suganya. "A Performance Comparison of Hypervisors for Cloud Computing." UNF Digital Commons, 2012. http://digitalcommons.unf.edu/etd/269.

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The virtualization of IT infrastructure enables the consolidation and pooling of IT resources so that they can be 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 is 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. It is a piece of software that provides a virtualized hardware environment to support running multiple operating systems concurrently using one physical server. Cloud computing has to support multiple operating environments and Hypervisor is the ideal delivery mechanism. The intent of this thesis is to quantitatively and qualitatively compare the performance of VMware ESXi 4.1, Citrix Systems Xen Server 5.6 and Ubuntu 11.04 Server KVM Hypervisors using standard benchmark SPECvirt_sc2010v1.01 formulated by Standard Performance Evaluation Corporation (SPEC) under various workloads simulating real life situations.
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Danielsson, Simon, and Staffan Johansson. "Cloud Computing - A Study of Performance and Security." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20326.

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Cloud Computing är det stora modeordet i IT-världen just nu. Det har blivit mer och mer populärt på senare år men frågor har uppstått om dess prestanda och säkerhet. Hur säkert är det egentligen och är det någon större skillnad i prestanda mellan en lokal server och en molnbaserad server? Detta examensarbete tar upp dessa frågor. En serie prestandatester kombinerat med en litteraturstudie genomfördes för att få fram ett resultatet för detta examensarbete.Denna rapport kan komma att vara till nytta för de som har ett intresse av Cloud Computing men som saknar någon större kunskap om ämnet. Resultaten kan användas som exempel för hur framtida forskning inom Cloud Computing kan genomföras.
Cloud Computing - the big buzz word of the IT world. It has become more and more popular in recent years but questions has arisen about it’s performance and security. How safe is it and is there any real difference in performance between a locally based server and a cloud based server? This thesis will examine these questions. A series of performance tests combined with a literature study were performed to achieve the results of this thesis.This thesis could be of use for those who have an interest in Cloud Computing and do not have much knowledge of it. The results can be used as an example for how future research in Cloud Computing can be done.
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Calatrava, Arroyo Amanda. "High Performance Scientific Computing over Hybrid Cloud Platforms." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/75265.

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Scientific applications generally require large computational requirements, memory and data management for their execution. Such applications have traditionally used high-performance resources, such as shared memory supercomputers, clusters of PCs with distributed memory, or resources from Grid infrastructures on which the application needs to be adapted to run successfully. In recent years, the advent of virtualization techniques, together with the emergence of Cloud Computing, has caused a major shift in the way these applications are executed. However, the execution management of scientific applications on high performance elastic platforms is not a trivial task. In this doctoral thesis, Elastic Cloud Computing Cluster (EC3) has been developed. EC3 is an open-source tool able to execute high performance scientific applications by creating self-managed cost-efficient virtual hybrid elastic clusters on top of IaaS Clouds. These self-managed clusters have the capability to adapt the size of the cluster, i.e. the number of nodes, to the workload, thus creating the illusion of a real cluster without requiring an investment beyond the actual usage. They can be fully customized and migrated from one provider to another, in an automatically and transparent process for the users and jobs running in the cluster. EC3 can also deploy hybrid clusters across on-premises and public Cloud resources, where on-premises resources are supplemented with public Cloud resources to accelerate the execution process. Different instance types and the use of spot instances combined with on-demand resources are also cluster configurations supported by EC3. Moreover, using spot instances, together with checkpointing techniques, the tool can significantly reduce the total cost of executions while introducing automatic fault tolerance. EC3 is conceived to facilitate the use of virtual clusters to users, that might not have an extensive knowledge about these technologies, but they can benefit from them. Thus, the tool offers two different interfaces for its users, a web interface where EC3 is exposed as a service for non-experienced users and a powerful command line interface. Moreover, this thesis explores the field of light-weight virtualization using containers as an alternative to the traditional virtualization solution based on virtual machines. This study analyzes the suitable scenario for the use of containers and proposes an architecture for the deployment of elastic virtual clusters based on this technology. Finally, to demonstrate the functionality and advantages of the tools developed during this thesis, this document includes several use cases covering different scenarios and fields of knowledge, such as structural analysis of buildings, astrophysics or biodiversity.
Las aplicaciones científicas generalmente precisan grandes requisitos de cómputo, memoria y gestión de datos para su ejecución. Este tipo de aplicaciones tradicionalmente ha empleado recursos de altas prestaciones, como supercomputadores de memoria compartida, clústers de PCs de memoria distribuida, o recursos provenientes de infraestructuras Grid, sobre los que se adaptaba la aplicación para que se ejecutara satisfactoriamente. El auge que han tenido las técnicas de virtualización en los últimos años, propiciando la aparición de la computación en la nube (Cloud Computing), ha provocado un importante cambio en la forma de ejecutar este tipo de aplicaciones. Sin embargo, la gestión de la ejecución de aplicaciones científicas sobre plataformas de computación elásticas de altas prestaciones no es una tarea trivial. En esta tesis doctoral se ha desarrollado Elastic Cloud Computing Cluster (EC3), una herramienta de código abierto capaz de llevar a cabo la ejecución de aplicaciones científicas de altas prestaciones creando para ello clústers virtuales, híbridos y elásticos, autogestionados y eficientes en cuanto a costes, sobre plataformas Cloud de tipo Infraestructura como Servicio (IaaS). Estos clústers autogestionados tienen la capacidad de adaptar su tamaño, es decir, el número de nodos, a la carga de trabajo, creando así la ilusión de un clúster real sin requerir una inversión por encima del uso actual. Además, son completamente configurables y pueden ser migrados de un proveedor a otro de manera automática y transparente a los usuarios y trabajos en ejecución en el cluster. EC3 también permite desplegar clústers híbridos sobre recursos Cloud públicos y privados, donde los recursos privados son complementados con recursos Cloud públicos para acelerar el proceso de ejecución. Otras configuraciones híbridas, como el empleo de diferentes tipos de instancias y el uso de instancias puntuales combinado con instancias bajo demanda son también soportadas por EC3. Además, el uso de instancias puntuales junto con técnicas de checkpointing permite a EC3 reducir significantemente el coste total de las ejecuciones a la vez que proporciona tolerancia a fallos. EC3 está concebido para facilitar el uso de clústers virtuales a los usuarios, que, aunque no tengan un conocimiento extenso sobre este tipo de tecnologías, pueden beneficiarse fácilmente de ellas. Por ello, la herramienta ofrece dos interfaces diferentes a sus usuarios, una interfaz web donde se expone EC3 como servicio para usuarios no experimentados y una potente interfaz de línea de comandos. Además, esta tesis doctoral se adentra en el campo de la virtualización ligera, mediante el uso de contenedores como alternativa a la solución tradicional de virtualización basada en máquinas virtuales. Este estudio analiza el escenario propicio para el uso de contenedores y propone una arquitectura para el despliegue de clusters virtuales elásticos basados en esta tecnología. Finalmente, para demostrar la funcionalidad y ventajas de las herramientas desarrolladas durante esta tesis, esta memoria recoge varios casos de uso que abarcan diferentes escenarios y campos de conocimiento, como estudios estructurales de edificios, astrofísica o biodiversidad.
Les aplicacions científiques generalment precisen grans requisits de còmput, de memòria i de gestió de dades per a la seua execució. Este tipus d'aplicacions tradicionalment hi ha empleat recursos d'altes prestacions, com supercomputadors de memòria compartida, clústers de PCs de memòria distribuïda, o recursos provinents d'infraestructures Grid, sobre els quals s'adaptava l'aplicació perquè s'executara satisfactòriament. L'auge que han tingut les tècniques de virtualitzaciò en els últims anys, propiciant l'aparició de la computació en el núvol (Cloud Computing), ha provocat un important canvi en la forma d'executar este tipus d'aplicacions. No obstant això, la gestió de l'execució d'aplicacions científiques sobre plataformes de computació elàstiques d'altes prestacions no és una tasca trivial. En esta tesi doctoral s'ha desenvolupat Elastic Cloud Computing Cluster (EC3), una ferramenta de codi lliure capaç de dur a terme l'execució d'aplicacions científiques d'altes prestacions creant per a això clústers virtuals, híbrids i elàstics, autogestionats i eficients quant a costos, sobre plataformes Cloud de tipus Infraestructura com a Servici (IaaS). Estos clústers autogestionats tenen la capacitat d'adaptar la seua grandària, es dir, el nombre de nodes, a la càrrega de treball, creant així la il·lusió d'un cluster real sense requerir una inversió per damunt de l'ús actual. A més, són completament configurables i poden ser migrats d'un proveïdor a un altre de forma automàtica i transparent als usuaris i treballs en execució en el cluster. EC3 també permet desplegar clústers híbrids sobre recursos Cloud públics i privats, on els recursos privats són complementats amb recursos Cloud públics per a accelerar el procés d'execució. Altres configuracions híbrides, com l'us de diferents tipus d'instàncies i l'ús d'instàncies puntuals combinat amb instàncies baix demanda són també suportades per EC3. A més, l'ús d'instàncies puntuals junt amb tècniques de checkpointing permet a EC3 reduir significantment el cost total de les execucions al mateix temps que proporciona tolerància a fallades. EC3e stà concebut per a facilitar l'ús de clústers virtuals als usuaris, que, encara que no tinguen un coneixement extensiu sobre este tipus de tecnologies, poden beneficiar-se fàcilment d'elles. Per això, la ferramenta oferix dos interfícies diferents dels seus usuaris, una interfície web on s'exposa EC3 com a servici per a usuaris no experimentats i una potent interfície de línia d'ordres. A més, esta tesi doctoral s'endinsa en el camp de la virtualitzaciò lleugera, per mitjà de l'ús de contenidors com a alternativa a la solució tradicional de virtualitzaciò basada en màquines virtuals. Este estudi analitza l'escenari propici per a l'ús de contenidors i proposa una arquitectura per al desplegament de clusters virtuals elàstics basats en esta tecnologia. Finalment, per a demostrar la funcionalitat i avantatges de les ferramentes desenrotllades durant esta tesi, esta memòria arreplega diversos casos d'ús que comprenen diferents escenaris i camps de coneixement, com a estudis estructurals d'edificis, astrofísica o biodiversitat.
Calatrava Arroyo, A. (2016). High Performance Scientific Computing over Hybrid Cloud Platforms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/75265
TESIS
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Hutchins, Richard Chad. "Feasibility of virtual machine and cloud computing technologies for high performance computing." Thesis, Monterey, California. Naval Postgraduate School, 2013. http://hdl.handle.net/10945/42447.

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Approved for public release; distribution is unlimited
Reissued May 2014 with additions to the acknowledgments
Knowing the future weather on the battlefield with high certainty can result in a higher advantage over the adversary. To create this advantage for the United States, the U.S. Navy utilizes the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to create high spatial resolution, regional, numerical weather prediction (NWP) forecasts. To compute a forecast, COAMPS runs on high performance computing (HPC) systems. These HPC systems are large, dedicated supercomputers with little ability to scale or move. This makes these systems vulnerable to outages without a costly, equally powerful secondary system. Recent advancements in cloud computing and virtualization technologies provide a method for high mobility and scalability without sacrificing performance. This research used standard benchmarks in order to quantitatively compare a virtual machine (VM) to a native HPC cluster. The benchmark tests showed that the VM was feasible platform for executing HPC applications. Then we ran the COAMPS NWP on a VM within a cloud infrastructure to prove the ability to run a HPC application in a virtualized environment. The VM COAMPS model run performed better than the native HPC machine model run. These results show that VM and cloud computing technologies can be used to run HPC applications for the Department of Defense
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Books on the topic "Cloud Computing Performance"

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Pearson, Siani. Privacy and Security for Cloud Computing. London: Springer London, 2013.

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Milutinović, Veljko, Marijana Despotović-Zrakić, and Aleksandar Belić. Handbook of research on high performance and cloud computing in scientific research and education. Hershey, PA: Information Science Reference, 2014.

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Udoh, Emmanuel. Cloud, grid and high performance computing: Emerging applications. Hershey PA: Information Science Reference, 2011.

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Lynn, Theo. Heterogeneity, High Performance Computing, Self-Organization and the Cloud. Basingstoke: Springer Nature, 2018.

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Lynn, Theo, John P. Morrison, and David Kenny, eds. Heterogeneity, High Performance Computing, Self-Organization and the Cloud. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76038-4.

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Udoh, Emmanuel. Applications and developments in grid, cloud, and high performance computing. Hershey, PA: Information Science Reference, 2013.

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K, Kokula Krishna Hari, ed. Cloud Technology and Performance Improvement with Intserv Over Diffserv for Cloud Computing: ICCCEG 2014. Vietnam: Association of Scientists, Developers and Faculties, 2014.

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Evolving developments in grid and cloud computing: Advancing research. Hershey, PA: Information Science Reference, 2012.

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Gentzsch, Wolfgang. High speed and large scale scientific computing. Amsterdam: IOS Press, 2009.

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Gentzsch, Wolfgang. High speed and large scale scientific computing. Amsterdam: IOS Press, 2009.

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Book chapters on the topic "Cloud Computing Performance"

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Aversa, Rocco, Beniamino Di Martino, Massimiliano Rak, Salvatore Venticinque, and Umberto Villano. "Performance Prediction for HPC on Clouds." In Cloud Computing, 437–56. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940105.ch17.

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Casola, Valentina, Massimiliano Rak, and Umberto Villano. "PerfCloud: Performance-Oriented Integration of Cloud and GRID." In Cloud Computing, 93–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12636-9_7.

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Könsgen, Raoul, and Mario Schaarschmidt. "Key Performance Indicators für Software as a Service." In Cloud Computing, 31–42. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20967-4_3.

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Ekanayake, Jaliya, and Geoffrey Fox. "High Performance Parallel Computing with Clouds and Cloud Technologies." In Cloud Computing, 20–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12636-9_2.

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Ostermann, Simon, Alexandria Iosup, Nezih Yigitbasi, Radu Prodan, Thomas Fahringer, and Dick Epema. "A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing." In Cloud Computing, 115–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12636-9_9.

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Lima, Rodrigo Alves, Joshua Kimball, João E. Ferreira, and Calton Pu. "Systematic Construction, Execution, and Reproduction of Complex Performance Benchmarks." In Cloud Computing – CLOUD 2019, 26–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23502-4_3.

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Murphy, John. "Performance Engineering for Cloud Computing." In Computer Performance Engineering, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24749-1_1.

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Joshi, Pramod Kumar, and Sadhana Rana. "Era of Cloud Computing." In High Performance Architecture and Grid Computing, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22577-2_1.

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Chawla, Vinay, and Prenul Sogani. "Cloud Computing – The Future." In High Performance Architecture and Grid Computing, 113–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22577-2_15.

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Kimball, Joshua, and Calton Pu. "A Method and Tool for Automated Induction of Relations from Quantitative Performance Logs." In Cloud Computing – CLOUD 2019, 11–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23502-4_2.

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Conference papers on the topic "Cloud Computing Performance"

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Wu, Dazhong, Xi Liu, Steve Hebert, Wolfgang Gentzsch, and Janis Terpenny. "Performance Evaluation of Cloud-Based High Performance Computing for Finite Element Analysis." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46381.

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Cloud computing is an innovative computing paradigm that can potentially bridge the gap between increasing computing demands in computer aided engineering (CAE) applications and limited scalability, flexibility, and agility in traditional computing paradigms. In light of the benefits of cloud computing, high performance computing (HPC) in the cloud has the potential to enable users to not only accelerate computationally expensive CAE simulations (e.g., finite element analysis), but also to reduce costs by utilizing on-demand and scalable cloud computing resources. The objective of this research is to evaluate the performance of running a large finite element simulation in a public cloud. Specifically, an experiment is performed to identify individual and interactive effects of several factors (e.g., CPU core count, memory size, solver computational rate, and input/output rate) on run time using statistical methods. Our experimental results have shown that the performance of HPC in the cloud is sufficient for the application of a large finite element analysis, and that run time can be optimized by properly selecting a configuration of CPU, memory, and interconnect.
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Batra, Amit, and Arvind Kumar. "High Performance Computing into Cloud Computing Services." In 2012 International Conference on Computing Sciences (ICCS). IEEE, 2012. http://dx.doi.org/10.1109/iccs.2012.39.

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Barbosa Vianna, Rodrigo, and Luiz Fernando Bittencourt. "Performance Evaluation of Cloud Computing." In XXV Congresso de Iniciação Cientifica da Unicamp. Campinas - SP, Brazil: Galoa, 2017. http://dx.doi.org/10.19146/pibic-2017-78629.

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Loulergue, Frederic, Frederic Gava, Nikolai Kosmatov, and Matthieu Lemerre. "Towards verified cloud computing environments." In 2012 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2012. http://dx.doi.org/10.1109/hpcsim.2012.6266896.

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Kumaresan, M., and G. K. D. Prasanna Venkatesan. "Enabling high performance computing in cloud computing environments." In 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE). IEEE, 2017. http://dx.doi.org/10.1109/iceice.2017.8191887.

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Olson, Michael, and K. Mani Chandy. "Performance Issues in Cloud Computing for Cyber-physical Applications." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.118.

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Younge, Andrew J., Robert Henschel, James T. Brown, Gregor von Laszewski, Judy Qiu, and Geoffrey C. Fox. "Analysis of Virtualization Technologies for High Performance Computing Environments." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.29.

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Roloff, Eduardo, Francis Birck, Matthias Diener, Alexandre Carissimi, and Philippe O. A. Navaux. "Evaluating High Performance Computing on the Windows Azure Platform." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.47.

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Ellens, Wendy, Miroslav ivkovic, Jacob Akkerboom, Remco Litjens, and Hans van den Berg. "Performance of Cloud Computing Centers with Multiple Priority Classes." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.96.

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Mfula, Harrison, and Jukka K. Nurminen. "Self-Healing Cloud Services in Private Multi-Clouds." In 2018 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2018. http://dx.doi.org/10.1109/hpcs.2018.00041.

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Reports on the topic "Cloud Computing Performance"

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Hochstein, Lorin. High Performance Computing (HPC) Innovation Service Portal Pilots Cloud Computing (HPC-ISP Pilot Cloud Computing). Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada549202.

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Appel, Gordon John, Teklu Hadgu, Brandon Thorin Klein, and John Gifford Miner. Cloud Computing for Complex Performance Codes. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1343253.

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