Social networking applications that are developed using traditional software and architecture have scalability issues. One way to overcome the high cost of scaling social applications is to use Cloud Computing (CC). There are various cloud computing platforms available. One very interesting CC platform is Google App Engine (GAE). This research focuses on using the free GAE as a way to re-implement existing social networking applications.<p> The research focuses on how to move social applications into the cloud and on the evaluation of their performance. The thesis investigates the GAE platform, and its features. The study shows how to re-implement a social networking application using GAE cloud with limited code approximately 600 lines and evaluates the scalability of the applications.

<p>With Cloud Computing becoming a popular term on the Information Technology (IT) market, security and accountability has become important issues to highlight. In our research we review these concepts by focusing on security risks with Cloud Computing and the associated services; Software, Platform and Infrastructure (SPI) and connecting them with a social study of trust.</p><p>The method that was conducted during our research was reviewing secondary literature, interviewing different experts regarding Cloud Computing and relating standards already established by ENISA, NIST, and CSA to the interviews.</p><p>The result of this study shows connections between the specific SPIs, both how they compare, but also how they differ. In the end we were also able to rank the top security risks from interviews with experts and see which SPI could be the most insecure one and  what countermeasures could be applied.</p><p>This was further related to trust and Service Level Agreement (SLA) in Cloud Computing to show how the security risks we discuss are related to these two specific areas. By highlighting this we wanted to present useable information for both clients and providers in how to create a better Cloud Computing environment.</p>

Cloud computing is a technology that uses the internet and central remote servers to maintain data and applications. There are different cloud services of which cloud Infrastructure as a service enables a company to grow very fast. All small and large-scale companies are shifting their applications towards cloud. As usage of the internet all over the world, the number of video applications are increasing more and becoming popular. Smart phones use wireless networks to transfer large amount of data. Users access the video application from the cloud through web browsers in laptop and smart phone. First, a systematic literature review is conducted on the performance issues of cloud infrastructure as a service. Second, the performance metrics jitter, round trip time and page loading time are analyzed while accessing a video application from the cloud. Finally, results are analyzed for various browsers in Smartphone and laptop. From the obtained results it helps the users to choose better browser for accessing cloud applications.

Context In the fields of communication systems and computer science, Infrastructure as a Service consists of building blocks for cloud computing and to provide robust network features. AWS is one such infrastructure as a service which provides several services out of which Elastic Cloud Compute (EC2) is used to deploy virtual machines across several data centers and provides fault tolerant storage for applications across the cloud. Apache Cassandra is one of the many NoSQL databases which provides fault tolerance and elasticity across the servers. It has a ring structure which helps the communication effective between the nodes in a cluster. Cassandra is robust which means that there will not be a down-time when adding new Cassandra nodes to the existing cluster.  Objectives. In this study quantifying the latency in adding Cassandra nodes to the Amazon EC2 instances and assessing the impact of Replication factors (RF) and Consistency Levels (CL) on autoscaling have been put forth. Methods. Primarily a literature review is conducted on how the experiment with the above-mentioned constraints can be carried out. Further an experimentation is conducted to address the latency and the effects of autoscaling. A 3-node Cassandra cluster runs on Amazon EC2 with Ubuntu 14.04 LTS as the operating system. A threshold value is identified for each Cassandra specific configuration and is scaled over to five nodes on AWS utilizing the benchmarking tool, Cassandra stress tool. This procedure is repeated for a 5-node Cassandra cluster and each of the configurations with a mixed workload of equal reads and writes. Results. Latency has been identified in adding Cassandra nodes on Amazon EC2 instances and the impacts of replication factors and consistency levels on autoscaling have been quantified. Conclusions. It is concluded that there is a decrease in latency after autoscaling for all the configurations of Cassandra and changing the replication factors and consistency levels have also resulted in performance change of Cassandra.

Varje år går stora mängder resurser förlorade på misslyckade IT-system vilket bidrar till ett stort intresse för kostnadseffektiva tekniker. En sådan teknik kallas Cloud Computing och har funnits i ett flertal år. Cloud Computing kan potentiellt sänka kostnader relaterade till ITprojekt, såsom exempelvis kostnader rörande underhåll av serverhårdvara. Function as a Service (FaaS) är ett av de senaste tillskotten till Cloud Computing. Något som blir alltmer problematiskt är att identifiera vilken typ av Cloud Computing som bäst lämpar sig för ett företag eller projekt. Denna studie ämnar därför svara på följande frågor; vilket värde tillför FaaS till utvecklare vid utvecklande av applikationer, hur skiljer sig FaaS från IaaS rörande implementation och vilka är potentiella motiv bakom nyttjande av FaaS. Genom att svara på dessa frågor ämnar studien agera vägledande vid val av Cloud Computing-tjänst. Vid analys av FaaS har LEAN Software Development (LSD) applicerats för att identifiera var FaaS reducerar och potentiellt adderar slöseri vid mjukvaruutveckling nyttjande tekniken. En fallstudie genomfördes vid ett litet företag, mindre än 50 anställda, som experimenterar med Amazon Web Services implementation av FaaS, Lambda. Slutsatsen av studien är att trots att samtliga aspekter av LSD inte är applicerbara på alla företag och projekt motiverar Lambdas fördelaktiga betalmodell företag att själva utforska tekniken. / Every year a tremendous amount of resources is lost on failed IT-Systems. It is therefore of interest to explore potential cost-saving technologies. One such technology that has been around for many years is Cloud Computing. Cloud Computing can potentially lower costs of IT-projects by, for example, eliminating the need to maintain server hardware. One of the more recent additions to the Cloud Computing assortment is Function as a Service (FaaS). What is becoming increasingly problematic about the assortment of Cloud Computing services is to know which service is best suitable for a company or project. This study therefore aims to examine FaaS to answer the questions; what value does FaaS add to the developers when developing applications, what differs in implementing FaaS from IaaS, and what are potential motives behind the usage of FaaS, thereby provide guidance when choosing Cloud Computing service. To analyze the results the LEAN Software Development (LSD) model has been used to identify where FaaS reduces and potentially adds waste in software development. A casestudy of a small organization, less than 50 employees, that are experimenting with the usage of Amazon Web Services implementation of FaaS, Lambda, has been made. The conclusion of the study is that even though all the aspects of LSD is not applicable to all companies or projects, the payment model of Lambda makes it advantageous for organizations to try it out for themselves.

Cloud Computing is an emerging paradigm in the field of computing where scalable IT enabled capabilities are delivered ‘as-a-service’ using Internet technology. The Cloud industry adopted three basic types of computing service models based on software level abstraction: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Infrastructure-as-a-Service allows customers to outsource fundamental computing resources such as servers, networking, storage, as well as services where the provider owns and manages the entire infrastructure. This allows customers to only pay for the resources they consume. In a fast-growing IaaS market with multiple cloud platforms offering IaaS services, the user's decision on the selection of the best IaaS platform is quite challenging. Therefore, it is very important for organizations to evaluate and compare the performance of different IaaS cloud platforms in order to minimize cost and maximize performance. Using a vendor-neutral approach, this research focused on four of the top IaaS cloud platforms- Amazon EC2, Microsoft Azure, Google Compute Engine, and Rackspace cloud services. This research compared the performance of IaaS cloud platforms using system-level parameters including server, file I/O, and network. System-level benchmarking provides an objective comparison of the IaaS cloud platforms from performance perspective. Unixbench, Dbench, and Iperf are the system-level benchmarks chosen to test the performance of the server, file I/O, and network respectively. In order to capture the performance variability, the benchmark tests were performed at different time periods on weekdays and weekends. Each IaaS platform's performance was also tested using various parameters. The benchmark tests conducted on different virtual machine (VM) configurations should help cloud users select the best IaaS platform for their needs. Also, based on their applications' requirements, cloud users should get a clearer picture of which VM configuration they should choose. In addition to the performance evaluation, the price-per-performance value of all the IaaS cloud platforms was also examined.

Cloud computing has become an established paradigm for running services on external infrastructure that dynamically allocates virtually unlimited capacity. This paradigm creates a new scenario for the deployment of applications and information technology (IT) services. In this model, complete applications and machine infrastructure are offered to users, who are charged only for the resources they consume. Thus, cloud resources are offered through service abstractions classified into three main categories: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). In IaaS, computing resources are offered as virtual machines to the end user. The aim to offer such unlimited resources necessitates distributing these virtual machines through multiple data centers. This distribution makes harder to fulfill a number of requirements including security, reliability, availability, and accounting. Accounting refers to how resources are recorded, accounted for, and charged. Even for a single cloud provider this task is hard, and it becomes more difficult for a federation of cloud computing, or federated cloud, in which a cloud provider dynamically outsources resources to other providers in response to demand variation. Thus, a cluster of clouds shares heterogeneous resources, requiring greater effort to manage and accurately account for the distributed resources. Some earlier research has addressed the development of platforms for federated clouds but without considering the accounting requirement. This dissertation presents a framework for charging IaaS with a focus on federated cloud. In order to gather information about this topic area and to generate guidelines for building the framework, we applied a systematic mapping study. This dissertation also presents an initial validation of the tool through a case study, showing evidence that the requirements generated through the mapping study were fulfilled by the framework and presenting indications of its feasibility in a real cloud service scenario / Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-10T18:37:17Z No. of bitstreams: 2 Dissertação Francisco Airton da Silva.pdf: 5605679 bytes, checksum: 61aa2b6df102174ff2e190ab47678cbf (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-11T17:45:06Z (GMT). No. of bitstreams: 2 Dissertação Francisco Airton da Silva.pdf: 5605679 bytes, checksum: 61aa2b6df102174ff2e190ab47678cbf (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-27 / A Computação na Nuvem se tornou um paradigma consumado para executar serviços em infraestruturas externas, onde de uma forma virtual a capacidade praticamente ilimitada pode ser alocada dinamicamente. Este paradigma estabelece um novo cenário para a implantação de aplicações e serviços de TI. Neste modelo, desde aplicações completas até infraestrutura de máquinas são ofertadas a usuários, que são cobrados apenas pelo uso dos recursos consumidos. Desta forma, os bens de consumo da nuvem são ofertados através de abstrações de serviços, onde atualmente são citadas três principais categorias: Software como Serviço (SaaS), Plataforma como Serviço (PaaS) e Infraestrutura como Serviço (IaaS). No caso do IaaS são oferecidos recursos computacionais na forma de Máquinas Virtuais para o cliente final. Para atingir o aspecto ilimitado de recursos é necessário distribuir estas Máquinas Virtuais por vários Data Centers. Esta distribuição dificulta atender uma série de requisitos como Segurança, Confiabilidade, Disponibilidade e a Tarifação pelos recursos consumidos. A Tarifação refere-se a como os recursos são registrados, contabilizados e cobrados. Mesmo no caso de um único provedor esta tarefa não é fácil e existe um contexto em que esta dificuldade se torna ainda maior, conhecida como Federação de Computação na Nuvem ou também chamadas de Nuvens Federadas. Nuvens Federadas ocorrem quando um provedor de Computação na Nuvem terceiriza recursos dinamicamente para outros provedores em resposta à variação da demanda. Desta forma ocorre um aglomerado de nuvens, porém seus recursos são heterogêneos, acarretando num maior esforço para gerenciar os recursos distribuídos e por consequência para a Tarifação. Neste contexto foram identificadas pesquisas nesta área sobre plataformas para Nuvens Federadas, que não abordam o requisito de Tarifação. Esta dissertação apresenta um framework voltado à tarifação de Infraestrutura como Serviço com foco em Nuvens Federadas. Objetivando colher informações sobre esta área e consequentemente gerar insumos para fundamentar as decisões na construção do framework, foi aplicado um Estudo de Mapeamento Sistemático. Esta dissertação também apresenta uma validação inicial da ferramenta, através de um estudo experimental, mostrando indícios de que os requisitos gerados pelo Mapeamento Sistemático foram atendidos, bem como será viável a aplicação da solução por provedores de serviços de nuvem em um cenário real.

With Cloud Computing becoming a popular term on the Information Technology (IT) market, security and accountability has become important issues to highlight. In our research we review these concepts by focusing on security risks with Cloud Computing and the associated services; Software, Platform and Infrastructure (SPI) and connecting them with a social study of trust. The method that was conducted during our research was reviewing secondary literature, interviewing different experts regarding Cloud Computing and relating standards already established by ENISA, NIST, and CSA to the interviews. The result of this study shows connections between the specific SPIs, both how they compare, but also how they differ. In the end we were also able to rank the top security risks from interviews with experts and see which SPI could be the most insecure one and  what countermeasures could be applied. This was further related to trust and Service Level Agreement (SLA) in Cloud Computing to show how the security risks we discuss are related to these two specific areas. By highlighting this we wanted to present useable information for both clients and providers in how to create a better Cloud Computing environment.

Infrastructure-as-a-Service(IaaS), one of the service models of cloud computing, provides resources in the form of Virtual Machines(VMs). Many applications hosted on the IaaS cloud have time varying workloads. These kind of applications benefit from the on-demand provision ing characteristic of cloud platforms. Applications with time varying workloads demand time varying resources in IaaS, which requires elastic resource provisioning in IaaS, such that their performance is intact. In current IaaS cloud systems, VMs are static in nature as their configurations do not change once they are instantiated. Therefore, fluctuation in resource demand is handled in two ways: allocating more VMs to the application(horizontal scaling) or migrating the application to another VM with a different configuration (vertical scaling). This forces the customers to characterize their workloads at a coarse grained level which potentially leads to under-utilized VM resources or under performing application. Furthermore, the current IaaS architecture does not provide performance guarantees to applications, because of two major factors: 1)Performance metrics of the application are not used for resource allocation mechanisms by the IaaS, 2) Current resource allocation mechanisms do not consider virtualization overheads, can significantly impact the application’s performance, especially for I/O workloads. In this work, we develop an Elastic Resource Framework for IaaS, which provides flexible resource provisioning mechanism and at the same time preserves performance of applications specified by the Service Level Agreement(SLA). For identification of workloads which needs elastic resource allocation, variability has been defined as a metric and is associated with the definition of elasticity of a resource allocation system. We introduce new components Forecasting Engine based on a Cost Model and Resource manager in Open Nebula IaaS cloud, which compute a n optimal resource requirement for the next scheduling cycle based on prediction. Scheduler takes this as an input and enables fine grained resource allocation by dynamically adjusting the size of the VM. Since the prediction may not always be entirely correct, there might be under-allocation or over-allocation of resources based on forecast errors. The design of the cost model accounts for both over-allocation of resources and SLA violations caused by under-allocation of resources. Also, proper resource allocation requires consideration of the virtualization overhead, which is not captured by current monitoring frameworks. We modify existing monitoring frameworks to monitor virtualization over head and provide fine-grained monitoring information in the Virtual Machine Monitor (VMM) as well as VMs. In our approach, the performance of the application is preserved by 1)binding the application level performance SLA store source allocation, and 2) accounting for virtualization over-head while allocating resources. The proposed framework is implemented using the forecasting strategies like Seasonal Auto Regressive and Moving Average model (Seasonal ARIMA), and Gaussian Process model. However, this framework is generic enough to use any other forecasting strategy as well. It is applied to the real workloads, namely web server and mail server workloads, obtained through Supercomputer Education and Research Centre, Indian Institute of Science. The results show that significant reduction in the resource requirements can be obtained while preserving the performance of application by restricting the SLA violations. We further show that more intelligent scaling decisions can be taken using the monitoring information derived by the modification in monitoring framework.

Práce se zabývá podrobnou analýzou požadavků na moderní aplikační rámec pro prostředí cloud. Za pomoci standardních návrhových vzorů a technik připravuje teoretický základ a pravidla, která musí uvnitř rámce platit. V práci je realizována referenční implementace a připravena demonstrační aplikace středního rozsahu, aby představila výhody plynoucí z užití frameworku.