L’un des principaux défis des fournisseurs de services cloud est d’offrir aux utilisateurs une performance acceptable, tout en minimisant les besoins en matériel et énergie. Dans cette thèse CIFRE menée avec Outscale, un fournisseur de cloud, nous visons à optimiser l’allocation des ressources en utilisant de nouvelles sources d’information. Nous caractérisons la charge de travail pour comprendre le stress résultant sur l’orchestrateur, et la compétition pour les ressources disponibles qui dégrade la qualité de service. Nous proposons un modèle pour prédire la durée d’exécution des VMs à partir de caractéristiques prédictives disponibles au démarrage. Enfin, nous évaluons la sensibilité aux erreurs d’un algorithme de placement des VMs de la littérature qui se base sur ces prédictions. Nous ne trouvons pas d’intérêt à coupler note système prédictif avec cet algorithme, mais nous proposons d’autres façons d’utiliser les prédictions pour optimiser le placement des VMs. / One of the main challenges for cloud computing providers remains to offer trustable performance for all users, while maintaining an efficient use of hardware and energy resources. In the context of this CIFRE thesis lead with Outscale, apublic cloud provider, we perform an in-depth study aimed at making management algorithms use new sources of information. We characterize Outscale’s workload to understand the resulting stress for the orchestrator, and the contention for hardware resources. We propose models to predict the runtime of VMs based on features which are available when they start. We evaluate the sensitivity with respect to prediction error of a VM placement algorithm from the literature that requires such predictions. We do not find any advantage in coupling our prediction model and the selected algorithm, but we propose alternative ways to use predictions to optimize the placement of VMs.

One of the most important attribute a cloud service provider (CSP) offers their customers through their cloud services is scalability. Scalability gives customers the ability to vary the amount of capacity when required. A cloud service can be divided in three service layers, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). Scalability of a certain service depends on software licenses on these layers. When a customer wants to increase the capacity it will be determined by the CSP's licenses bought from its suppliers in advance. If a CSP scales up more than what was agreed on, then there is a risk that the CSP needs to pay a penalty fee to the supplier. If the CSP invests in too many licenses that does not get utilized, then it will be an investment loss. A second challenge with software licensing is when a customer outsources their applications to the CSP’s platform. As each application comes with a set of licenses, there is a certain level of scalability that cannot be exceeded. If a customer wants the CSP scale up more than usual for an application then the customer need to inform the vendors. However, a common misunderstanding is that the customer expects the CSP to notify the vendor. Then there is a risk that the vendor never gets notified and the customer is in danger of paying a penalty fee. This in turn hurts the CSP’s relationship with the customer. The recommendation to the CSP under study is to create a successful customer relationship management (CRM) and a supplier relationship management (SRM). By creating a CRM with the customer will minimize the occurring misunderstandings and highlight the responsibilities when a customer outsources an application to the CSP. By creating a SRM with the supplier will help the CSP to maintain a flexible paying method that they have with a certain supplier. Furthermore, it will set an example to the remaining suppliers to change their inflexible paying method. By achieving a flexible payment method with the suppliers will make it easier for the CSP to find equilibrium between scalability and licenses. / Ett av de viktigaste attributen en molnleverantör (CSP) erbjuder sina kunder genom sina molntjänster är skalbarhet. Skalbarheten ger kunden möjligheten att variera mängden kapacitet efter behov. En molntjänst kan delas in i tre serviceskikter, Infrastruktur-as-a-Service (IaaS), Platform-as-a-Service (PaaS) och Software-as-a-Service (SaaS). Skalbarheten av en viss service beror på mjukvarulicenser på dessa skikt. När en kund vill öka kapaciteten bestäms detta av CSP:s förhandsköpta licenser från sina leverantörer. Om en CSP skalar upp mer än vad som avtalats, finns det en risk att CSP:n måste betala en straffavgift till leverantören. Investerar CSP i alltför många licenser som inte utnyttjas, blir det en investeringsförlust. En annan utmaning med mjukvarulicenser uppstår när en kund lägger ut sina applikationer på CSP:s plattform. Eftersom varje applikation levereras med en uppsättning av licenser, finns det en bestämd nivå av skalbarhet som inte kan överskridas. Om en kund vill att CSP:n ska skala upp mer än vanligt för en applikation, måste kunden informera applikationsförsäljaren själv. Dock sker det en missuppfattning då kunden förväntar sig att CSP:n ska meddela säljaren om detta. Det finns en risk att säljaren aldrig blir informerad och kunden hamnar i stor risk för att betala en straffavgift. Detta i sin tur skadar CSP:s relation med kunden. Rekommendationen till CSP:n är att skapa en framgångsrik kundrelationshantering (CRM) och leverantörrelationshantering (SRM). Genom att skapa en CRM med kunden kan förekommande missförstånd minimeras och belysa ansvarsområden som råder när en kund lägger ut sin applikation hos CSP:n. Genom att skapa en SRM med leverantörerna kommer det att hjälpa CSP:n att upprätthålla en flexibel betalmetod som de har med en viss leverantör. Dessutom kommer det att vara ett föredöme för de övriga leverantörerna att ändra sin icke flexibla betalningsmetod. Genom att uppnå en mer flexibel betalningsmetod med leverantörerna kommer det att bli enklare för en CSP att hitta en balans mellan skalbarhet och licenser.

This thesis deals with possible interconnections between two concepts playing a big role in contemporary business and IT world. These concepts are Business Continuity Management and Cloud Computing. In the scope of this thesis there are certain areas identified where both concepts are complement, where Cloud Computing brings new opportunities for Business Continuity Management and where could possible problems arise during particular implementation. From the BCM perspective the impact lies on IT services, from the Cloud Computing perspective the thesis deals especially with security aspects. The thesis is also aimed at the characteristics of higher education and basic differences from commercial sphere. Based on defined differences and identified interconnections between BCM and Cloud Computing, the thesis argues for usage of suitable Cloud Computing solution for higher education regarding Business Continuity improvement. The multi-criterion comparison of several Infrastructure-as-a-Service solutions stems from this analysis focusing on technical, financial, and Business Continuity aspects. The result from this comparison together with conclusions from previous chapters serve as an input for subsequent practical proposal of Cloud Computing solution and its verification against Business Continuity improvement in specific conditions on University of Economics in Prague. The proposal is also represented by strategic map.

Information technology (IT) in the recent decades has showcased immense evolution. This evolution has impacted not only the technological sector of the society but also the socio-political and economic sectors. One such archetype that has gained popular momentum in the recent years is cloud computing. Still in its developmental stages, cloud computing is the latest offspring of computing services and has invariably challenged the traditional aspects of computing services across the IT Organizations in the world. Thus, adopted by a number of developed as well as developing countries, cloud computing has played a crucial role in decreasing the overall cost pertinent to hardware, software and other services in organizations. However, issues such as data security and confidentiality, data availability, legal issues and unintelligent Service Level Agreements (SLAs) have questioned the reliability and credibility of cloud computing. This has resulted in lack of potential investments from major companies. The main aim of this research is to provide concrete reasons for the actively implementing cloud computing in IT Organizations. Qualitative approach and exploratory design are extensively used for data collection and analysis. Primary data is collected based on the views and opinions of IT experts and secondary data is collected from literary sources which invariably supports the primary data and maintains the credibility of the research.

This diploma thesis deals with Cloud computing, in particular its implementation of Microsoft's Windows Azure platform. The following objectives were defined for the thesis, which is analyzing and identifying the essential characteristics of Cloud computing with a focus on the Windows Azure platform, characterizing the key differences of applications in the cloud than on-premise applications, formulate recommendations for enabling transformation of existing applications into the Microsoft Cloud computing, which is delivered as a service. The main contribution of the work is to identify key differences between Windows Azure and on-premise solution. Creating recommendations to minimize the negative impact of these differences and maximize the benefits associated with the use of Windows Azure Cloud Computing platform.

This diploma thesis focuses on the problematics of providing ERP in Cloud. The main objectives of this thesis lie in either identification of the situation in the ERP market, provision of an outline of particular deployment models, evaluation of strong and weak parts of ERP in cloud in contractor's and customer's point of view and in charting of potential risks and chances for the area. Obtained theoretical findings are utilized in the practical part of the thesis. Its significant portion is dedicated to proposition of methodology for the selection of right ERP model supply. This is then used for the particular choice of Oracle JD Edwards implementation. To achieve these objectives author realizes an analysis of electronic and printed resources as well as consultations with experts and also uses his own practical experiencies from Algotech Corporation. The main contribution of this thesis is in the creation of comprehensive sum of problematics and in the formation of methodology for the right type of deployment selection. This is all considering the further application and verification on the example. Thesis is divided into five theme parts -- ERP, Cloud, ERP in Cloud, methodology and practical setting of the presented methodology.

This thesis focuses on the ubiquitous cloud computing trend and particularly Software as a Service. The main goal is to specify all pros and cons of this concept for its customers and also cloud providers. The global perspective is then specified to Software as a Service and deployment of SaaS ERP products. The next goal is to define an appropriate method to analyze a company's needs prior to switching to the cloud. Furthermore, recommendations as to whether the cloud is better than an on-premise version should result from the previous analysis. Another aim is to form a process for successful switch to the cloud and ensure all important aspects of using cloud services are covered. Theoretical findings and recommendations are used in a practical example of SaaS ERP deployment. To reach all goals, a theoretical analysis of all available monograph and electronic sources, covering cloud computing and consultations with experts from the company; Algotech BSC, were used. The main added value is in the dual point of view on the topic, from customer's and provider's viewpoints with a more detailed focus on ERP. The author suggests the right procedure for switching to the cloud, guidance with calculating ROI, TCO and preparing all necessary contracts and agreements.

This work is focused on the newly emerging field of Cloud computing and view of adoption of this technology in various industrial sectors. The first section explains the concept of cloud computing, its characteristics and individual models. The following part is a brief analysis of the size and development of the current market for cloud services. Next section focuses on the creation of industry categories acording to area of their business, together with their description. The actual categories are examined in part four, along with their expenditures on IT and state of Cloud's adoption, in addition with specific advantages and obstacles. Results from previous section are summarized in part five. In conclusion, the hypothesis is verified and the main findings summarized.

This diploma thesis deals with the issue of the development of mobile thin client application which is able to manage commercial infrastructure cloud service. The introduction section provides an overview of the necessary terminology and presents the problem domain that is a subject of this thesis. Besides the project itself, of which result is the main output of this work, is also the part dedicated to research of current theses, school information resources, guidelines, recommendations and methodologies that covers topics of design and implementation of graphical user interfaces for mobile devices, especially for those that have the Android operating system installed. Information obtained on basis of the research is after used as a default knowledge base for design and implementation of the presentation layer of the previously mentioned application. The next aim of this thesis is to make analysis of requirements for new application which is being developed. The requirements are then transformed into conceptual design, to which is bound the explanation of implementation of individual application layers. UML was mainly used for the modeling of the application parts design in environment of the Sybase PowerDesigner. The programming process was performed by usage of the development environment called Eclipse SDK. Benefit of this diploma thesis lies in the possibility of reusing its concentrated knowledge resulting from the research dealing with the problems of creating GUI for mobile devices. Programmer, who is untouched in this specialization, will be able to use this part of the thesis as a critical study material which has been verified in practice. Cloud infrastructure solution providers can find there an inspiration for their own mobile thin client application.

The examiner of cloud computing systems in the last few years observes that there is a trend of the emergence of new Big Data frameworks every single year. Since Hadoop was developed in 2007, new frameworks followed it such as Spark, Storm, Heron, Apex, Flink, Samza, Kafka ... etc. Each framework is developed in a certain way to target and achieve certain objectives better than other frameworks do. However, there are few common functionalities and aspects that are shared between these frameworks. One vital aspect all these frameworks strive to achieve is better reliability and faster recovery time in case of failures. Despite all the advances in making datacenters dependable, failures actually still happen. This is particularly onerous for long-running “big data” applications, where partial failures can lead to significant losses and lengthy recomputations. This is also crucial for streaming systems where events are processed and monitored online in real time, and any delay in data delivery will cause a major inconvenience to the users.<div>Another observation is that some reliability implementations are redundant between different frameworks. Big data processing frameworks like Hadoop MapReduce include fault tolerance mechanisms, but these are commonly targeted at specific system/failure models, and are often redundant between frameworks. Encapsulating these implementations into one layer and making it shared between different applications will benefit more than one frame-work without the burden of re-implementing the same reliability approach in each single framework.<br></div><div>These observations motivated us to solve the problem by presenting two systems: Guardian and Warden. Guardian is tailored towards batch processing big data systems while Warden is targeted towards stream processing systems. Both systems are robust, RMS based, generic, multi-framework, flexible, customizable, low overhead systems that allow their users to run their applications with individually configurable fault tolerance granularity and degree, with only minor changes to their implementation.<br></div><div>Most reliability approaches carry out one rigid fault tolerance technique targeted towards one system at a time. It is more challenging to provide a reliability approach that is pluggable in multiple Big Data frameworks at a time and can achieve low overheads comparable with single targeted framework approaches, yet is flexible and customizable by its users to make it tailored towards their objectives. The genericity is attained by providing an interface that can be used in different applications from different frameworks in any part of the application code. The low overhead is achieved by providing faster application finish times with and without failures. The customizability is fulfilled by providing the users the options to choose between two fault tolerance guarantees (Crash Failures / Byzantine Failures) and, in case of streaming systems; it is combined with two delivery semantics (Exactly Once / At Most Once).<br></div><div>In other words, this thesis proposes the paradigm of dependable resources: big data processing frameworks are typically built on top of resource management systems (RMSs),and proposing fault tolerance support at the level of such an RMS yields generic fault tolerance mechanisms, which can be provided with low overhead by leveraging constraints on resources.<br></div><div>To the best of our knowledge, such approach was never tried on multiple big data batch processing and streaming frameworks before.<br></div><div>We demonstrate the benefits of Guardian by evaluating some batch processing frame-works such as Hadoop, Tez, Spark and Pig on a prototype of Guardian running on Amazon-EC2, improving completion time by around 68% in the presence of failures, while maintaining around 6% overhead. We’ve also built a prototype of Warden on the Flink and Samza (with Kafka) streaming frameworks. Our evaluations on Warden highlight the effectiveness of our approach in the presence of failures and without failures compared to other fault tolerance techniques (such as checkpointing)<br></div>