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The book, while introducing readers to the auction mechanism for resource provisioning in cloud computing, also endeavors to provide structured literature on the subject. Since various models have been proposed, it will help readers to formulate the cloud resource provisioning problem using the auction approach. The book also discusses challenges for resource provisioning in detail, helping to shape future research. The target audience for this book includes computer scientists, economists, industry professionals, research scholars, and postgraduate students. Computer science readers of this book will come to see that economics-based method are quite helpful in computer science, especially for resource provisioning. Readers with a cloud computing background will come to recognize the importance of dynamic pricing, the specific benefits of auctions, and how to formulate auctions for cloud computing. Lastly, readers from the economics community will come to understand their role in cloud computing, as well as where and how they can contribute.
This SpringerBrief reviews the existing market-oriented strategies for economically managing resource allocation in distributed systems. It describes three new schemes that address cost-efficiency, user incentives, and allocation fairness with regard to different scheduling contexts. The first scheme, taking the Amazon EC2TM market as a case of study, investigates the optimal resource rental planning models based on linear integer programming and stochastic optimization techniques. This model is useful to explore the interaction between the cloud infrastructure provider and the cloud resource customers. The second scheme targets a free-trade resource market, studying the interactions amongst multiple rational resource traders. Leveraging an optimization framework from AI, this scheme examines the spontaneous exchange of resources among multiple resource owners. Finally, the third scheme describes an experimental market-oriented resource sharing platform inspired by eBay's transaction model. The study presented in this book sheds light on economic models and their implication to the utility-oriented scheduling problems.
Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on.
This edited book presents contributions from three different areas: cloud computing, digital mess and business algorithms on a single platform, i.e. Digital Business. The book is divided into four sections: (i) Digital Business Transformation, (ii) Cloud Computing, (iii) IOT & Mobility, and (iv) Information Management & Social Media, which are part of a holistic approach to information management and connecting the value chains of businesses to derive more throughput in the entire business ecosystem. Digital business is a niche area of computer science and business management, and its dimension is vast - it includes technologies such as cloud computing, Internet of Things, mobile platforms, big data applied in areas like ERP, data mining and business intelligence. Digital technologies have also challenged existing business models and will continue to do so. One of the key driving forces is the capacity of innovation and the commercialization of information and communication technologies.
Present trends in cloud providers (CPs) capabilities have given rise to the interest in federating or collaborating clouds, thus allowing providers to revel on an increased scale and reach more than that is achievable individually. Current research efforts in this context mainly focus on building supply chain collaboration (SCC) models, in which CPs leverage cloud services from other CPs for seamless provisioning. Nevertheless, in the near future, we can expect that hundreds of CPs will compete to offer services and thousands of users will also compete to receive the services to run their complex heterogeneous applications on a cloud computing environment. In this open federation scenario, existing collaboration models (i.e. SCC) are not applicable since they are designed for static environments where a-priori agreements among the parties are needed to establish the federation. To move beyond these shortcomings, Dynamic Cloud Collaboration Platform establishes the basis for developing dynamic, advanced and efficient collaborative cloud service solutions that are scalable, high performance, and cost effective. We term the technology for inter-connection and inter-operation of CPs in open cloud federation as Dynamic Cloud Collaboration (DCC), in which various CPs (small, medium, and large) of complementary service requirements will collaborate dynamically to gain economies of scale and enlargements of their capabilities to meet quality of service (QoS) requirements of consumers. In this context, this book addresses four key issues - when to collaborate (triggering circumstances), whom to collaborate with (suitable partners), how to collaborate (architectural model), and how to demonstrate collaboration applicability (simulation study). It also provides solutions, which are effective in real environments.
This book includes high-quality, peer-reviewed papers from the International Conference on Recent Advancement in Computer, Communication and Computational Sciences (RACCCS-2017), held at Aryabhatta College of Engineering & Research Center, Ajmer, India on September 2–3, 2017, presenting the latest developments and technical solutions in computational sciences. Data science, data- and knowledge engineering require networking and communication as a backbone and have a wide scope of implementation in engineering sciences. Keeping this ideology in mind, the book offers insights that reflect the advances in these fields from upcoming researchers and leading academicians across the globe. Covering a variety of topics, such as intelligent hardware and software design, advanced communications, intelligent computing technologies, advanced software engineering, the web and informatics, and intelligent image processing, it helps those in the computer industry and academia use the advances of next-generation communication and computational technology to shape real-world applications.
Abstract: The paradigm of cloud computing has started a new era of service computing. While there are many research efforts on developing enabling technologies for cloud computing, few focuses on how to strategically set price and capacity and what key components are leading to success in this emerging market. In this thesis, we present quantitative modeling and optimization approaches for assisting such decisions in cloud computing services. We first show that learning curve models help in understanding the potential market of cloud services and explain quantitatively why cloud computing is most attractive to small and medium businesses. We then present Single Instance model to depict a particular type of cloud networks and aid in resource provisioning for the cloud service providers. We further present Multiple Instance model to depict any generic cloud network. We map the resource provisioning problem to Kelly's Loss Network and propose Genetic Algorithm to solve it. The approach provides the cloud service provider a quantitative framework to obtain management solutions and to learn and react to the critical parameters in the operation management process by gaining useful business insights.