Lecture Notes on Cloud Computing
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LECTURE NOTES ON CLOUD COMPUTING Unit-1 INTRODUCTION TO CLOUD COMPUTING CLOUD COMPUTING IN A NUTSHELL Computing itself, to be considered fully virtualized, must allow computers to be built from distributed components such as processing, storage, data, and software resources. Technologies such as cluster, grid, and now, cloud computing, have all aimed at allowing access to large amounts of computing power in a fully virtualized manner, by aggregating resources and offering a single system view. Utility computing describes a business model for on-demand delivery of computing power; consumers pay providers based on usage (“payas-you- go”), similar to the way in which we currently obtain services from traditional public utility services such as water, electricity, gas, and telephony. Cloud computing has been coined as an umbrella term to describe a category of sophisticated on-demand computing services initially offered by commercial providers, such as Amazon, Google, and Microsoft. It denotes a model on which a computing infrastructure is viewed as a “cloud,” from which businesses and individuals access applications from anywhere in the world on demand . The main principle behind this model is offering computing, storage, and software “as a service.” Many practitioners in the commercial and academic spheres have attempted to define exactly what “cloud computing” is and what unique characteristics it presents. Buyya et al. have defined it as follows: “Cloud is a parallel and distributed computing system consisting of a collection of inter-connected and virtualised computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements (SLA) established through negotiation between the service provider and consumers.” Vaquero et al. have stated “clouds are a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). These resources can be dynamically reconfigured to adjust to a variable load (scale), allowing also for an optimum resource utilization. This pool of resources is typically exploited by a pay-per-use model in which guarantees are offered by the Infrastructure Provider by means of customized Service Level Agreements.” A recent McKinsey and Co. report claims that “Clouds are hardwarebased services offering compute, network, and storage capacity where: Hardware management is highly abstracted from the buyer, buyers incur infrastructure costs as variable OPEX, and infrastructure capacity is highly elastic.” A report from the University of California Berkeley summarized the key characteristics of cloud computing as: “(1) the illusion of infinite computing resources; (2) the elimination of an up-front commitment by cloud users; and (3) the ability to pay for use ... as needed .. .” The National Institute of Standards and Technology (NIST) characterizes cloud computing as “... a pay-per-use model for enabling available, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” In a more generic definition, Armbrust et al. define cloud as the “data center hardware and software that provide services.” Similarly, Sotomayor et al. point out that “cloud” is more often used to refer to the IT infrastructure deployed on an Infrastructure as a Service provider data center. While there are countless other definitions, there seems to be common characteristics between the most notable ones listed above, which a cloud should have: (i) pay-per-use (no ongoing commitment, utility prices); (ii) elastic capacity and the illusion of infinite resources; (iii) self-service interface; and (iv) resources that are abstracted or virtualised. ROOTS OF CLOUD COMPUTING We can track the roots of clouds computing by observing the advancement of several technologies, especially in hardware (virtualization, multi-core chips), Internet technologies (Web services, service-oriented architectures, Web 2.0), distributed computing (clusters, grids), and systems management (autonomic computing, data center automation). Figure 1.1 shows the convergence of technology fields that significantly advanced and contributed to the advent of cloud computing. Some of these technologies have been tagged as hype in their early stages of development; however, they later received significant attention from academia and were sanctioned by major industry players. Consequently, a specification and standardization process followed, leading to maturity and wide adoption. The emergence of cloud computing itself is closely linked to the maturity of such technologies. We present a closer look at the technol ogies that form the base of cloud computing, with the aim of providing a clearer picture of the cloud ecosystem as a whole. From Mainframes to Clouds We are currently experiencing a switch in the IT world, from in-house generated computing power into utility-supplied computing resources delivered over the Internet as Web services. This trend is similar to what occurred about a century ago when factories, which used to generate their own electric power, realized that it is was cheaper just plugging their machines into the newly formed electric power grid . Computing delivered as a utility can be defined as “on demand delivery of infrastructure, applications, and business processes in a security-rich, shared, scalable, and based computer environment over the Internet for a fee” . Hardware Hardware Virtualization Multi-core chips I g nte n i t r u n p e t SOA m Utility & T Web 2.0 e Co Cloud c Grid h d Web Services no e Computing t Computing u l Mashups o b i g r i es ist D Autonomic Computing Data Center Automation Systems Management FIGURE 1.1. Convergence of various advances leading to the advent of cloud computing. This model brings benefits to both consumers and providers of IT services. Consumers can attain reduction on IT-related costs by choosing to obtain cheaper services from external providers as opposed to heavily investing on IT infrastructure and personnel hiring. The “on-demand” component of this model allows consumers to adapt their IT usage to rapidly increasing or unpredictable computing needs. Providers of IT services achieve better operational costs; hardware and software infrastructures are built to provide multiple solutions and serve many users, thus increasing efficiency and ultimately leading to faster return on investment (ROI) as well as lower total cost of ownership (TCO). The mainframe era collapsed with the advent of fast and inexpensive microprocessors and IT data centers moved to collections of commodity servers. The advent of increasingly fast fiber-optics networks has relit the fire, and new technologies for enabling sharing of computing power over great distances have appeared. SOA, Web Services, Web 2.0, and Mashups • Web Service • applications running on different messaging product platforms • enabling information from one application to be made available to others • enabling internal applications to be made available over the Internet • SOA • address requirements of loosely coupled, standards-based, and protocol-independent distributed computing • WS ,HTTP, XML • Common mechanism for delivering service • applications is a collection of services that together perform complex business logic • Building block in IaaS • User authentication, payroll management, calender Grid Computing Grid computing enables aggregation of distributed resources and transparently access to them. Most production grids such as TeraGrid and EGEE seek to share compute and storage resources distributed across different administrative domains, with their main focus being speeding up a broad range of scientific applications, such as climate modeling, drug design, and protein analysis. Globus Toolkit is a middleware that implements several standard Grid services and over the years has aided the deployment of several service-oriented Grid infrastructures and applications. An ecosystem of tools is available to interact with service grids, including grid brokers, which facilitate user interaction with multiple middleware and implement policies to meet QoS needs. Virtualization technology has been identified as the perfect fit to issues that have caused frustration when using grids, such as hosting many dissimilar software applications on a single physical platform. In this direction, some research projects. Utility Computing In utility computing environments, users assign a “utility” value to their jobs, where utility is a fixed or time-varying valuation that captures various QoS constraints (deadline, importance, satisfaction). The valuation is the amount they are willing to pay a service provider to satisfy their demands. The service providers then attempt to maximize their own utility, where said utility may directly correlate with their profit. Providers can choose to prioritize high yield (i.e., profit per unit of resource) user jobs, leading to a scenario where shared systems are viewed as a marketplace, where users compete for resources based on the perceived utility or value of their jobs. Hardware Virtualization The idea of virtualizing a computer system’s resources, including processors, memory, and I/O devices, has been well established for decades, aiming at improving sharing and utilization of computer systems . Hardware virtualization allows