Measuring the Business Value of Cloud Computing

The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. Recent reviews of extant literature highlights the need for multi-disciplinary research. This research should explore and further develops the conceptualization of value in cloud computing research. In addition, there is a need for research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios. This open access book will review the state of the art from an IS, Computer Science and Accounting perspective, will introduce and discuss the main techniques for measuring business value for cloud computing in a variety of scenarios, and illustrate these with mini-case studies

Novel processes for smart grid information exchange and knowledge representation using the IEC common information model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.Dr. Martin Bradley on behalf of National Grid Plc. and the Engineering and Physical Sciences Research Council (EPSRC

Transiency-driven Resource Management for Cloud Computing Platforms

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient computing resources are increasingly important, and existing fault-tolerance and resource management techniques are inadequate for transient servers because applications typically assume continuous resource availability. This thesis presents research in distributed systems design that treats transiency as a first-class design principle. I show that combining transiency-specific fault-tolerance mechanisms with resource management policies to suit application characteristics and requirements, can yield significant cost and performance benefits. These mechanisms and policies have been implemented and prototyped as part of software systems, which allow a wide range of applications, such as interactive services and distributed data processing, to be deployed on transient servers, and can reduce cloud computing costs by up to 90\%. This thesis makes contributions to four areas of computer systems research: transiency-specific fault-tolerance, resource allocation, abstractions, and resource reclamation. For reducing the impact of transient server revocations, I develop two fault-tolerance techniques that are tailored to transient server characteristics and application requirements. For interactive applications, I build a derivative cloud platform that masks revocations by transparently moving application-state between servers of different types. Similarly, for distributed data processing applications, I investigate the use of application level periodic checkpointing to reduce the performance impact of server revocations. For managing and reducing the risk of server revocations, I investigate the use of server portfolios that allow transient resource allocation to be tailored to application requirements. Finally, I investigate how resource providers (such as cloud platforms) can provide transient resource availability without revocation, by looking into alternative resource reclamation techniques. I develop resource deflation, wherein a server\u27s resources are fractionally reclaimed, allowing the application to continue execution albeit with fewer resources. Resource deflation generalizes revocation, and the deflation mechanisms and cluster-wide policies can yield both high cluster utilization and low application performance degradation

Une architecture de cloud broker basée sur la sémantique pour l'optimisation de la satisfaction des utilisateurs

Cloud Computing is a dynamic new technology that has huge potentials in enterprises and markets. The dynamicity and the increasing complexity of Cloud architectures involve several management challenges. In this work, we are interested in Service Level Agreement (SLA) management. Actually, there is no standard to express Cloud SLA, so, providers describe their SLAs in different manner and different languages, which leaves the user puzzled about the choice of its Cloud provider. To overcome these problems, we introduce a Cloud Broker Architecture managing the SLA between providers and consumers. It aims to assist users in establishing and negotiating SLA contracts and to help them in finding the best provider that satisfies their service level expectations. Our broker SLA contracts are formalized as OWL ontologies as they allow hiding the heterogeneity in the distributed Cloud environment and enabling interoperability between Cloud actors. Besides, by combining our ontology with our proposed inference rules, we contribute to detect violations in the SLA contract assuring thereby the sustainability of the user satisfaction. Based on the requirements specified in the SLA contract, our Cloud Broker assists users in selecting the right provider using a multi attribute utility theory method. This method is based on utility functions representing the user satisfaction degree. To obtain accurate results, we have modelled both functional and non functional attributes utilities. We have used personalized utilities for each criterion under negotiation so that our cloud broker satisfies the best consumer requirements from functional and non functional point of viewLe Cloud Computing est un nouveau modèle économique hébergeant les applications de la technologie de l’information. Le passage au Cloud devient un enjeu important des entreprises pour des raisons économiques. La nature dynamique et la complexité croissante des architectures de Cloud impliquent plusieurs défis de gestion. Dans ce travail, nous nous intéressons à la gestion des contrats SLA. Vu le manque de standardisation, chaque fournisseur de service décrit les contrats SLA avec son propre langage, ce qui laisse l'utilisateur perplexe concernant le choix de son fournisseur de services. Dans ce travail, nous proposons une architecture de Cloud Broker permettant d’établir et de négocier les contrats SLA entre les fournisseurs et les consommateurs du Cloud. L’objectif de cette architecture est d’aider l’utilisateur à trouver le meilleur fournisseur en utilisant une méthode multi-critère. Cette méthode considère chaque critère comme une fonction d’utilité à intégrer dans une super-fonction d’utilité. Nous proposons d’illustrer chaque fonction d’utilité par une courbe spécifique à lui représentant bien le critère de choix. Nous essayons de cerner la plupart des critères qui contribuent dans le choix du meilleurs service et de les classer en critères fonctionnels et critères non fonctionnels. Les contrats SLA établit par notre broker sont formalisés sous forme d’ontologies qui permettent de masquer l'hétérogénéité et d’assurer l'interopérabilité entre les acteurs du Cloud. En outre, l’utilisation des règles d'inférence nous a permis de détecter les violations dans le contrat SLA établit et de garantir ainsi le respect de la satisfaction client dans le temp

Experimental Computational Simulation Environments for Algorithmic Trading

This thesis investigates experimental Computational Simulation Environments for Computational Finance that for the purpose of this study focused on Algorithmic Trading (AT) models and their risk. Within Computational Finance, AT combines different analytical techniques from statistics, machine learning and economics to create algorithms capable of taking, executing and administering investment decisions with optimal levels of profit and risk. Computational Simulation Environments are crucial for Big Data Analytics, and are increasingly being used by major financial institutions for researching algorithm models, evaluation of their stability, estimation of their optimal parameters and their expected risk and performance profiles. These large-scale Environments are predominantly designed for testing, optimisation and monitoring of algorithms running in virtual or real trading mode. The stateof-the-art Computational Simulation Environment described in this thesis is believed to be the first available for academic research in Computational Finance; specifically Financial Economics and AT. Consequently, the aim of the thesis was: 1) to set the operational expectations of the environment, and 2) to holistically evaluate the prototype software architecture of the system by providing access to it to the academic community via a series of trading competitions. Three key studies have been conducted as part of this thesis: a) an experiment investigating the design of Electronic Market Simulation Models; b) an experiment investigating the design of a Computational Simulation Environment for researching Algorithmic Trading; c) an experiment investigating algorithms and the design of a Portfolio Selection System, a key component of AT systems. Electronic Market Simulation Models (Experiment 1): this study investigates methods of simulating Electronic Markets (EMs) to enable computational finance experiments in trading. EMs are central hubs for bilateral exchange of securities in a well-defined, contracted and controlled manner. Such modern markets rely on electronic networks and are designed to replace Open Outcry Exchanges for the advantage of increased speed, reduced costs of transaction, and programmatic access. Study of simulation models of EMs is important from the point of view of testing trading paradigms, as it allows users to tailor the simulation to the needs of particular trading paradigms. This is a common practice amongst investment institutions to use EMs to fine-tune their algorithms before allowing the algorithms to trade with real funds. Simulations of EMs provide users with the ability to investigate the market micro-structure and to participate in a market, receive live data feeds and monitor their behaviour without bearing any of the risks associated with real-time market trading. Simulated EMs are used by risk managers to test risk characteristics and by quant developers to build and test quantitative financial systems against market behaviour. Computational Simulation Environments (Experiment 2): this study investigates the design, implementation and testing of an experimental Environment for Algorithmic Trading able to support a variety of AT strategies. The Environment consists of a set of distributed, multi-threaded, event-driven, real-time, Linux services communicating with each other via an asynchronous messaging system. The Environment allows multi-user real and virtual trading. It provides a proprietary application programming interface (API) to support research into algorithmic trading models and strategies. It supports advanced trading-signal generation and analysis in near real-time, with use of statistical and technical analysis as well as data mining methods. It provides data aggregation functionalities to process and store market data feeds. Portfolio Selection System (Experiment 3): this study investigates a key component of Computational Finance systems to discover exploitable relationships between financial time-series applicable amongst others to algorithmic trading; where the challenge lays in identification of similarities/dissimilarities in behaviour of elements within variable-size portfolios of tradable and non-tradable securities. Recognition of sets of securities characterized by a very similar/dissimilar behaviour over time, is beneficial from the perspective of risk management, recognition of statistical arbitrage and hedge opportunities, and can be also beneficial from the point of view of portfolio diversification. Consequently, a large-scale search algorithm enabling discovery of sets of securities with AT domain-specific similarity characteristics can be utilized in creation of better portfolio-based strategies, pairs-trading strategies, statistical arbitrage strategies, hedging and mean-reversion strategies. This thesis has the following contributions to science: Electronic Markets Simulation - identifies key features, modes of operation and software architecture of an electronic financial exchange for simulated (virtual) trading. It also identifies key exchange simulation models. These simulation models are crucial in the process of evaluation of trading algorithms and systemic risk. Majority of the proposed models are believed to be unique in the academia. Computational Simulation Environment - design, implementation and testing of a prototype experimental Computational Simulation Environment for Computational Finance research, currently supporting the design of trading algorithms and their associated risk. This is believed to be unique in the academia. Portfolio Selection System - defines what is believed to be a unique software system for portfolio selection containing a combinatorial framework for discovery of subsets of internally cointegrated time-series of financial securities and a graph-guided search algorithm for combinatorial selection of such time-series subsets

Pricing the Cloud: An Auction Approach

Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research. One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints. Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice

Exploring Firm-Level Cloud Adoption and Diffusion

Cloud computing innovation adoption literature has primarily focused on individuals, small businesses, and nonprofit organizations. The functional linkage between cloud adoption and diffusion is instrumental toward understanding enterprise firm-level adoption. The purpose of this qualitative collective case study was to explore strategies used by information technology (IT) executives to make advantageous enterprise cloud adoption and diffusion decisions. This study was guided by an integrated diffusion of innovation and technology, organization, and environment conceptual framework to capture and model this complex, multifaceted problem. The study’s population consisted of IT executives with cloud-centric roles in 3 large (revenues greater than $5 billion) telecom-related companies with a headquarters in the United States. Data collection included semistructured, individual interviews (n = 19) and the analysis of publicly available financial documents (n = 50) and organizational technical documents (n = 41). Data triangulation and interviewee member checking were used to increase study findings validity. Inter- and intracase analyses, using open and axial coding as well as constant comparative methods, were leveraged to identify 5 key themes namely top management support, information source bias, organizational change management, governance at scale, and service selection. An implication of this study for positive social change is that IT telecom executives might be able to optimize diffusion decisions to benefit downstream consumers in need of services