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Economic analysis of_cloud_computing

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Cloud computing is receiving an increasing level of attention, as evidenced by the rapidly growing number of qualitative surveys and analysis that has been published over the past few years. …

Cloud computing is receiving an increasing level of attention, as evidenced by the rapidly growing number of qualitative surveys and analysis that has been published over the past few years.
Cloud computing is a paradigm shift organizations use the computing resources to conduct their business. Cloud computing is a new general purpose Internet-based technology through which information is stored in servers and provided as a service and on-demand to clients. The computing resources are accessed by mainstream businesses as a pooled or leased resource over networks. Hence traditional IT investment decisions models are not directly suitable to perform the cost-benefit and investment decisions for cloud computing resources.
This paper presents research on the return-on-investment and pricing models and seeks to build a model for quantitative assessment of cloud computing.
The results of this analysis model are intended to facilitate a more informed decision making for cloud computing resources.

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  • 1. ECONOMIC ANALYSIS OF CLOUD COMPUTING ECONOMICS OF CLOUD COMPUTING Pravin K Asar PRAVIN K ASAR pravinasar.com 1
  • 2. ECONOMIC ANALYSIS OF CLOUD COMPUTING Table of Contents Abstract..................................................................................................................................................................... 3 Introduction ............................................................................................................................................................ 4 Qualitative Analysis ................................................................................................................................... 5 Productivity improvements .................................................................................................................. 7 Innovation ............................................................................................................................................. 8 Barriers to Adoption ................................................................................................................................. 9 Common challenges .................................................................................................................................. 9 Quantitative Analysis-Financial Analysis:................................................................................................ 10 Construction of Calculation Model: ........................................................................................................ 15 Closing Remarks ................................................................................................................................................. 15 Appendix A: Traditional and Cloud Computing Architecture ........................................................... 17 Cloud deployment models .................................................................................................................. 18 References ............................................................................................................................................................ 20 PRAVIN K ASAR pravinasar.com 2
  • 3. ECONOMIC ANALYSIS OF CLOUD COMPUTING Abstract Cloud computing is receiving an increasing level of attention, as evidenced by the rapidly growing number of qualitative surveys and analysis that has been published over the past few years. Cloud computing is a paradigm shift organizations use the computing resources to conduct their business. Cloud computing is a new general purpose Internet-based technology through which information is stored in servers and provided as a service and on-demand to clients. The computing resources are accessed by mainstream businesses as a pooled or leased resource over networks. Hence traditional IT investment decisions models are not directly suitable to perform the cost-benefit and investment decisions for cloud computing resources. This paperpresents research on the return-on-investment and pricing models and seeks to build a model for quantitative assessment of cloud computing. The results of this analysis model are intended to facilitate a more informed decision making for cloud computing resources. PRAVIN K ASAR pravinasar.com 3
  • 4. ECONOMIC ANALYSIS OF CLOUD COMPUTING Introduction “Today IT is a major financial, operational, and organizational component of any strategy” Mische (2001) In today’s high-tech world, Business relies heavily on Information Communication Technology (ICT) and Application softwareas a strategic tool for success and survival. As a result many IT and Software systems are either built or bought leading to exponential growth in data center. Over the years the underlying technologies (hardware platforms, software deployment methods) has evolved. Latest trend is to deploy the application on distributed systems (n-tier deployment) and application to be accessible from anywhere. The increasing use of Information Technology (IT) has brought with it overheads in the implementation and maintenance of in-house computing systems. The amount of time and finances invested in managing IT has increased exponentially; each decade since the 1970s has seen the evolution of IT into a new phenomenon, starting with mainframes in the 1970s, the rise of the personal computer in the 1980s and client server architecture from the 1990s. The next phase emerging in IT evolution is cloud computing. The concept of cloud computing has been around for some time (salesforce.com, Steve Jobs); however it has only recently become feasible from both a supplier and consumer perspective. The term cloud computing has become widespread amongst the business community, government and the media, but there is still some level of confusion outside of the technology industry about what cloud computing actually is, not the least because the language around cloud is constantly evolving. Indeed, cloud computing is a catchall termdescribing a range of related activities, but which is identified by each of the following corecriteria: Accessing computing resources as external services, instead of as products that are purchased, installed and managed within an organization. The ability to rapidly scale the allocation of computing resources to match fluctuations in business demand. Utility-based pricing, so that user’s only pay for computing resources actually used (rather than for full load capacity) as they do, for example, with electricity. Appendix A discusses in details the enterprise data center and cloud data center deployment architectures.At the heart of cloud computing lays the ability of computing resources to be reliably and efficiently accessed by mainstream businesses as a pooled resource over networks. PRAVIN K ASAR pravinasar.com 4
  • 5. ECONOMIC ANALYSIS OF CLOUD COMPUTING Qualitative Analysis A number of recent international surveys by CDW (2011) shows that businesses are increasingly becoming aware of the potential benefits of cloud computing and moving along the ‘cloud journey’ and its potential benefits Oracle (2010) whitepaper summarized the business benefitscommon to both public and private cloud as follows: Improved efficiency: Because both public and private cloud are based on grid computing13 and virtualization, both offer high efficiency and high utilization due to sharing pooled resources, enabling better workload balance across multiple applications. Increased availability: Another benefit of being based on grid computing is that applications can take advantage of a high availability of architecture that minimizes or eliminates planned and unplanned downtime, improving user service levels and business continuity. Elastic scalability: Grid computing also provides public and private cloud with elastic scalability; that is, the ability to add and remove computing capacity on demand. This is a significant advantage for applications with a highly variable workload or unpredictable growth, or for temporary applications. Fast deployment: Because both public and private cloud can provide self-service access to a shared pool of computing resources, and because the software and hardware components are standard, re-usable and shared, application deployment is greatly accelerated. Additional benefitsthat are unique to public cloud computing includes: Low upfront costs: Public clouds are faster and cheaper to get started, providing users with a low barrier to entry because there is no need to procure, install and configure hardware. Economies of scale: Large public clouds enjoy economies of scale in equipment purchasing power and management efficiencies. Savings may be passed on to consumers, and will increase as competition in the sector increases over time. Simpler to manage: Public clouds may require fewer IT personnel to manage and administer, update, patch, etc. Users rely on the public cloud service provider instead of an internal IT department. Operating expense: Public clouds are paid out of the operating expense budget, often by the users’ line of business, not the IT department. Capital expense is avoided, which can be an advantage in some organizations. PRAVIN K ASAR pravinasar.com 5
  • 6. ECONOMIC ANALYSIS OF CLOUD COMPUTING The emergence of cloud computing brings many benefits which are shifting the economics of IT. Cloud technology standardizes and pools IT resources and automates many of the maintenance tasks performed manually today. Cloud architectures facilitate elastic consumption, self-service, and pay-as-you-go pricing. Cloud also allows core IT infrastructure to be brought into large data centers that take advantage of significant economies of scale. The economics of cloud computing can be grouped into three broad categories: Direct cost savings Productivity improvements innovation Direct cost savings The largest and most identifiable economic benefit of cloud computing is the direct cost savings. Direct cost savings for organizations occur from changes both within the organization, and also the large data centers housing the IT infrastructure. Direct cost savings(Microsoft 2010) occurs at the data centers through significant economies of scale in three areas. Supply-side savings: Large-scale data centers potentially lower costs per server due to superior buying power and expertise. Demand-side aggregation: Aggregating demand for computing can smooth overall variability, allowing multiple users across varying industries, regions and time zones allowing average server utilization rates to increase. Multi-user efficiency: Increasing the number of users often lowers the application management and server cost per tenant.With large data centers housing the IT infrastructure, cloud computing activities remove many IT operational considerations from an organization altogether. This can not only reduce overheads associated with day-today operations of computer hardware and software, but can also simplify procurement, the need to plan for upgrades and patches to software, management of software licensing and facilities management. Removing this complexity from an organization frees personnel who are otherwise occupied with daily technology PRAVIN K ASAR pravinasar.com 6
  • 7. ECONOMIC ANALYSIS OF CLOUD COMPUTING operations. This may not translate into a reduction in overall headcount, but rather a reallocation of people (and tasks) within an organization. With these changes, an individual organization’s costs can change from mainly capital expenditure to predominantly operating expenditure. This can be achieved through lower upfront IT costs, as discussed earlier, and because cloud computing follows a utility basedpricing model in which service costs are based on consumption. That is, a company onlypays for those services that it uses rather than a fixed price for a potential level of services that may not suit actual demand. Another direct cost saving may come with lower electricity consumption (including for cooling apparatus) and accommodation costs for IT infrastructure, which are often among the largest components of overall IT costs. Specifically, several key factors(Accenture 2010)enable cloud computing to lower energy use and carbon emissions, including: Table 1 How Cloud Computing Leads to Green IT Factor Dynamic provisioning Multi-tenancy Server utilization Data center efficiency Rationale Reducing wasted computing resources through better matching of server capacity with actual demand. Flattening relative peak loads by serving large numbers of organizations and users on shared infrastructure and reducing costs through sharing of applications. Operating servers at higher utilization rates. Utilizing advanced data center infrastructure designs that reduce power loss through improved cooling, power conditioning, etc. Additionally, large data centers may be able to take advantage of geographical variability in electricity rates and choose to be situated in locations with either less expensive electricity supply or to negotiate bulk purchase agreements to lower the cost of electricity. Productivity improvements With the implementation of cloud computing, changes to business can be achieved without the need for detailed capacity planning, changes to installed technology or new technology purchases. Translated into business outcomes, this allows for the ability to open offices, geographically move staff and operations without compromising access to business systems, put new ideas into practice, and to meet new business requirements faster than before. PRAVIN K ASAR pravinasar.com 7
  • 8. ECONOMIC ANALYSIS OF CLOUD COMPUTING Cloud also enables organizations to scale up or down to the level of service required, allowing optimization of required capacity and reduced costs. The on-demand up/down elasticity of cloud-based computing services allows the ability to quickly scale computing resources to match business growth while minimizing downside risk, that is, preserving the ability to release resources if a new project fails to get traction. Additionally, cloud computing allows staff to access files and data when they are working remotely or outside of office hours. E-commuting has widespread potential benefits to both business — via a reduction in overheads (i.e. smaller office space may be required) — and to consumers (e.g. through a reduction in commuting time). Innovation The cost and efficiency benefits that initially drive interest in cloud computing may be augmented by other benefits. For example, organizations may gain further increased business flexibility and agility, collaboration, and an ability to take new products and services to market. An example of this is an online DVD hiring company that is transferring to cloud services to enable streamed delivery; the customer is able to receive, and the company to distribute, the product with significant time and cost savings. Cloud services may be particularly beneficial to small businesses that might lack the capital to acquire the in-house ICT solutions required in the absence of cloud services. The benefits of cloud computing may also translate into a faster ‘time to market’ for customer-facing activity. New services can potentially be built, and existing services adapted, more rapidly in response to feedback or changing customer requirements. In some cases, this could mean that improvements are significant, coming down from months to weeks or from weeks to days. Organizations may also progress to building entirely new services and products on cloud platforms taking full advantage of centralized data, easy scalability and web accessibility. Furthermore, many companies spend a significant portion of their IT budget on maintaining existing services and infrastructure, leaving few resources available for innovation. Cloud computing has the potential to free up significant resources that can be redirected to innovation. In spite the potential benefits described earlier, the all businesses are not adopting the new technology. PRAVIN K ASAR pravinasar.com 8
  • 9. ECONOMIC ANALYSIS OF CLOUD COMPUTING Barriers to Adoption While there is often a strong case for the adoption of cloud services, there are several constraints that need to be overcome. The natural barriers to full adoption include, but are not limited to: speed/latency issues and reliance on telecommunications services providers consistency of current processes and applications with cloud offerings (for example, ‘off the shelf’ cloud services may not integrate well with a business’ existing operations) location of data and related security and data sovereignty issues (including implications of the US Patriot Act17) business continuity/disaster recovery and integrations Limited knowledge of product offerings and lack of familiarity of businesses with opportunities. Based on the survey by various organization(KPMG, 2012) organizations identified as ‘pushing the boundaries’ of cloud computing came up with several findings. Although cloud computing makes it possible to access services located anywhere in the world, there is a strong desire for services located within certain geographical borders. For government/defense data, research data location is important in conjunction with security. A significant barrier to take-up is the wide variation in maturity and quality of cloud services and service providers — a particular problem is the inability to get enterprise grade service level agreements. Common challenges Common challenges include uncontrolled adoption of cloud applications in large organizations, non-compliance with local regulations (especially those that relate to the handling of customer information), and concerns about regulations applying to services in other jurisdictions, preparing ‘apples to oranges’ business cases for cloud computing, and measuring the performance of cloud service providers. Over time the above challenges to adoption will substantially overcome, as seen in early adopter countries such as the US (TCS, 2012)and Europe (European Commission, 2012) PRAVIN K ASAR pravinasar.com 9
  • 10. ECONOMIC ANALYSIS OF CLOUD COMPUTING Quantitative Analysis-Financial Analysis: Various financial and economic models have been derived used to financial analysis to evaluate the return on investment, total cost of ownership. Zhipeng Wu and Aiping Gan (2011) also discussed the intangible benefits (in addition to return of investment) from migration of the enterprise data center to cloud environment. Sharma et al(2012) has looked at cloud computing economic analysis from pricing the cloud computing from service provider point of view. Their model is based on the BlackScholes-Merton model for option pricing. Their model could be useful to the pricing for the cloud computing services. Andrzejak, Knoda and Yi (2011) have discussed the real instance price traces and workload model (referred as spot pricing model by authors) and its use to consumer to optimize the cost of peak time cloud computing needs. Mach and Schikuta (2011) developed an analytical model that supports the decisionmaking process to be applied with business cases and enables cloud consumers and cloud providers to determine their own business strategies and to analyze the respective impact on their business, including the energy costs. Li et al (2009) has proposed a cost analysis method based on the Total cost of ownership. Their modelis based the Total cost of ownership approach. To compare the TCO for enterprise and cloud computing data center, it is important to calculate the total investment over the period of desired time, for this Net Present Value (NPV) can could applied. Net Present Value (NPV) concept is commonly used in financial analysis to evaluate an investment considering the time value of the investment over a fixed duration. NPV has been used to compare purchasing with leasing /renting CPU cycles from the cloud taking CPU performance depreciation into account (Walker, 2009). It has also been used to compare the cost of hosting application workloads considering additional cost factors such as software licenses, electricity, workload growth, and multiple models of cloud usage. Where NPV is defined as the total investment cost over the course of Y years into the future, where c(t) is the cost invested at year t. k is the cost of capital where the money invested this year is worth more than money invested next year by a factor of (1+k). PRAVIN K ASAR pravinasar.com 10
  • 11. ECONOMIC ANALYSIS OF CLOUD COMPUTING Table 1 (Ellström,2011)below summarizes the various cost models in use for procurement of good and services by business. Table 2 Different cost perspectives and corresponding Cost Modeling methods Purchasing perspective Supply chain perspective Supplier perspective Method TCO TCR SCC Focus Supplier selection Logistics outsourcing Including logistics costs and information costs Traditional ABC Product cost focusing production SCC+value The same as SCC, value is also calculated Supplier costing Not focus on one product but on supplier In context with Information and Communications Technology investments, TCO is best suited(ITIL, 2012) since TCO philosophy aimed at understanding the relevant cost of buying a particular good or service from a particular supplier. TCO looks at an entire lifecycle cost analysis. In addition to the price paid for the product, TCO includes the costs incurred by purchasing for order placement research and qualification of suppliers, transportation, receiving, inspection, rejection, storage and disposal. Although it may not possible to quantify the cost breakdown for each lifecycle phase, TCO is an important tool to support strategic cost management. It is a complex approach that requires the decision makers to determine which costs it considers most relevant or significant in the acquisition, possession, use, and subsequent disposition of a good or service. The Information Technology Infrastructure Library (ITIL) provides a good reference to various topics including the cost breakdown structure for Information Communication Technology (ICT) infrastructure to calculate all IT costs (ITIL, 2012) While ITIL indicates no preference for either cost centered accounting or service based costing, the logical preference would be service for the simple reason that the philosophy of service management is more closely aligned with service based costing. The name Service Based costing suggests an end-to-end view of the costs of delivering an IT service. Practically this means that a costing methodology and set of cost centers need to be defined using the service definitions provided by the Service Level Management process and as published in the service catalog. In principle this means that the line items appearing on the client bill are synchronized with the services as they are defined within the Service Level Agreements and how Configuration Items (CIs) are captured and defined. PRAVIN K ASAR pravinasar.com 11
  • 12. ECONOMIC ANALYSIS OF CLOUD COMPUTING The NPV computed over Y years of the annual cost of a delivery model based on in-house /enterprise data centers are presented below. The cost of enterprise data center includes the cost of hardware, software procurement and licensing costs, utilities costs and salaries of IT personnel (data administrators, IT support staff, etc.) With reference to earlier work done in the area of IT financial management (ITIL, 2012), the following equations can be formulated for the costs associated with enterprise data center (edc). Hardware cost (Costhw) and Software cost(Costsw) are based on the number of physical servers (these servers are the data crunching severs) and data storage servers needed to run application workloads. Their cost is accounted for only in the year in which they were purchased. However, for applications that require software licenses and upgrades, this cost should be accounted for accordingly. Utilities cost consists of electricity and network bandwidth. The first term in equation represents the electricity costs, which is modeled based on the power (P) required to run and cool the physical compute and storage servers. The number of units needed to run a server is based on its power supply unit, PSU. The number of units needed for cooling is a [0.5, 1] factor of the number of units needed for running the servers (Walker,2009). We model electricity cost as a monthly function based on total units of kilowatt-hours (kWh) consumed. For example, for less than t1 units consumed, r1 is applied. Many utilities, including those offered by cloud providers commonly use this type of cost function. The PRAVIN K ASAR pravinasar.com 12
  • 13. ECONOMIC ANALYSIS OF CLOUD COMPUTING total cost depends on the total units consumed. If u, greater than t2, is consumed for a cost function based on r1 ($/kWh), r2, and r3, then the total cost is c. Both electricity and network are computed monthly and then scaled by 12 to obtain the annual amount. The salary that is accounted for as part of operational expenses and is the total amount needed to support the work related to administrating physical compute and storage servers in the enterprise data center. We note that administration of virtual machines is also required but we do not represent them in this term as virtual machine administrators are needed for both the enterprise data center delivery model and the cloud delivery model. They end up being the same amount, so we drop them from this term for simplicity. The FTERatio (Clarke, 2010), n:1 is defined as the number of physical compute or storage servers n that one system administrator can manage. A “lower” FTE Ratio such as 10:1 means that workers are less efficient than a “higher” FTE Ratio such as 100:1 Cloud Cost Model: Similarly cost model for ICT service delivery based on Cloud computing infrastructure can be defined. Amazon EC2Cloud (Amazon, 2012) is one of early cloud service provider. Amazon provides various virtual server configurations to suit the needs of computing tasks (speed and scale). Hence Amazon EC2 is used as a reference service provider. Infrastructure-as-a-Service (IaaS) is a very popular cloud model, as it mimics the setup of enterprise data center; in terms of hardware and software configuration. Biggest difference in this model is business do not have higher upfront cost, but have recurring usage costs. PRAVIN K ASAR pravinasar.com 13
  • 14. ECONOMIC ANALYSIS OF CLOUD COMPUTING Cost per instance of cloud depends on the configuration of cloud virtual machine, and normally termed as “Small”, “Large”, etc. The reservation fee (generally one-time fee for instance setup is charged and varies based on the period of contract) and usage fee is charged hourly (a pay per use pricing concept). The cloud charges for both the total amount of data stored and the total number of I/O requests (rate of IO). Depending on the cloud storage used, such as EBS or S3, the storage fees and the usage fees vary. The fees are often modeled using a similar rate function to the one depicted. Network: The cloud charges for both the total amount of data transferred out of an availability zone (cloud data center). In some cases, data transferred (data upload as well as data download) into a cloud data center is also charged. Many cloud providers (such Amazon, Microsoft) have recently removed data upload charges. Many cloud systems management service offerings such as instance monitoring, backups, load balancing, elasticity, etc. at nominal cost. Depending on application requirements, a cloud user could employ one or more of these services in addition to its compute, storage and network needs. Our application in Section III-D requires snapshots for backup purposes, so we represent the cost of storing the snapshot based on the size of the snapshot and using the frequency of snapshot service (i.e., total I/O writes to disk) in this term. One may exclude other services for simplicity Putting all these components together, we can built both the cost models for using an enterprise data center vs. using the cloud to deliver the ICT services. PRAVIN K ASAR pravinasar.com 14
  • 15. ECONOMIC ANALYSIS OF CLOUD COMPUTING Construction of Calculation Model: Using the research and with reference to Amazon’s online cloud computing cost calculator (Amazon, 2012) and literature published by Varia (2012),a model is developed using Microsoft Excel 2010. The model organized in different spreadsheets. The model is selfexplanatory and well documented. Assumptions used for the calculations can be edited. 1. Main: When user key-in the requirements (numbers of servers for each desired configuration) for standardized (day-to-day usage) and peak time usage. 2. Cloud RDS (Cloud Remote Data Service) 3. Co-location: Standard demands are met on-site and peak demands are met by cloud servers. 4. On-site: All infrastructure is located, operated on-site 5. Definitions: Explanation of terms and definitions used in the calculation. Closing Remarks The TCO model could cost estimation and financial planning to decision maker for a period of time. The Figure 1 below gives overall Annual Total Cost of Ownership (TCO) summary. Figure 2, 3 and 4 give the cost breakdown for cloud, co-location and on-site IT setup. The calculations are done for 20 Servers (10 small, 10 large) and based on the cost factors and values. Cloud RDS (On-Demand Instances Only) Cloud RDS (w/ 1 Year Reserved Instances) Cloud RDS (w/ 3 Year Reserved Instances) $74,847 $59,516 $55,805 Co-Location On-Site $162,768 $195,659 Figure 1: Annual Total Cost of Ownership (TCO) Summary PRAVIN K ASAR pravinasar.com 15
  • 16. ECONOMIC ANALYSIS OF CLOUD COMPUTING Figure 2Cost Breakdown for Cloud data center Figure 3 Cost Breakdown for Co-located data center Figure 4 Cost Breakdown for Onsite data center This model could be customized to suit the need of individual organization. PRAVIN K ASAR pravinasar.com 16
  • 17. ECONOMIC ANALYSIS OF CLOUD COMPUTING Appendix A: Traditional and Cloud Computing Architecture Figure 5 Traditional computing, owned, installed and operated on the premises by individual organizations Figure 6 Cloud computing, rented and accessed as external, shared services over networks PRAVIN K ASAR pravinasar.com 17
  • 18. ECONOMIC ANALYSIS OF CLOUD COMPUTING Cloud deployment models (Oracle, 2010 and Oracle, 2011) Cloud computing generally has four deployment models, as briefly explained below: Figure 7 Cloud Deployment Models Private cloud: For exclusive use by a single organization and typically controlled, managed and hosted in private data centers. The hosting and operation of private clouds may also be outsourced to a third party service provider, but a private cloud remains for the exclusive use of one organization. This is typically the first step in a company’s cloud journey. The computing resources shared by user groups. Public cloud: For use by multiple organizations (multi-tenants) on a shared basis and hosted and managed by a third party service provider (examples include Amazon EC2, Google Apps and Microsoft Azure). Community cloud: For use by a group of related organizations that wish to make use of a common cloud computing environment. This deployment is ideally suitable for organizations who share data on regular basis. Hybrid cloud: When a single organization adopts both private and public cloud for a single application in order to take advantage of the benefits of both. PRAVIN K ASAR pravinasar.com 18
  • 19. ECONOMIC ANALYSIS OF CLOUD COMPUTING Cloud computing can be categorized into three service models: Figure 8 Cloud Service Models Software as a service (SaaS): Renting access to software as Web-accessed services instead of installing it on the premises (example services include Salesforce.com, SAP Business-By Design, Google Apps). Platform as a service (PaaS): Developing and hosting bespoke software in cloud environments (platforms) that provide all required tools, languages, databases and resources (example services include Force.com, NetSuite Business Operating System, Microsoft Azure and Office 365 and Google App Engine). Infrastructure as a service (IaaS): Renting access to computer processing power and storage over networks (example services include Amazon EC2 and Amazon S3). PRAVIN K ASAR pravinasar.com 19
  • 20. ECONOMIC ANALYSIS OF CLOUD COMPUTING References Accenture, “Cloud Computing and Sustainability: The Environmental Benefits of Moving to the Cloud”, 2010, Online, http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture_Sustainability_Clou d_Computing_TheEnvironmentalBenefitsofMovingtotheCloud.pdf, Retrieved on Nov 20, 2012. Amazon, Web Services Websites, 2012, Online, http://aws.amazon.com/ec2/pricing/ , http://calculator.s3.amazonaws.com/calc5.html, retrieved on Nov 01, 2012 Andrzejak, A.; Kondo, D.; Sangho Yi; , "Decision Model for Cloud Computing under SLA Constraints," Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2010 IEEE International Symposium on , vol., no., pp.257-266, 17-19 Aug. 2010 Beaty, K. A.; Naik, V. K.; Perng, C.S.; "Economics of cloud computing for enterprise IT," IBM Journal of Research and Development, vol.55, no.6, pp.12:1-12:13, Nov.-Dec. 2011 CDW, “From Tactic to Strategy: The CDW 2011 Cloud Computing Tracking Poll”, 2011, Online, http://webobjects.cdw.com/webobjects/media/pdf/Newsroom/CDW-CloudTracking-Poll-Report-0511.pdf, Retrieved on Nov 20, 2012 Clarke, T., “Is there best practice for a server to system administrator ratio?” July, 2010, Online, http://www.computerworld.com.au/article/352635/there_best_practice_server_system_a dministrator_ratio/, Retrieved Nov 01, 2012 European Commission Report, “Unleashing the Potential of Cloud Computing in Europe”, September 2012, Online, http://ec.europa.eu/information_society/activities/cloudcomputing/docs/com/swd_com_ cloud.pdf , retrieved on Nov 18, 2012 Ellström D., “Logistics cost management models and their usability for purchasing”, 2011, Online, http://www.davidpublishing.org/DownLoad/?id=7825, retrieved Nov 1, 2012 Information Technology Infrastructure Library (ITIL), 2012, Online, http://www.itilofficialsite.com/, http://www.itlibrary.org/index.php, Retrieved Nov 01, 2012 KPMG, “Cloud Computing – Australian lessons and experiences”, 2012, Online, http://www.kpmg.com/AU/en/IssuesAndInsights/ArticlesPublications/Documents/Cloud -computing-Australian-lessons-and-experiences.pdf, Retrieved Nov 02, 2012 PRAVIN K ASAR pravinasar.com 20
  • 21. ECONOMIC ANALYSIS OF CLOUD COMPUTING Mach, W.; Schikuta, E.; , "A Consumer-Provider Cloud Cost Model Considering Variable Cost," Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on , vol., no., pp.628-635, 12-14 Dec. 2011 Microsoft, “The Economics of the Cloud”, November 2010, Online, http://www.microsoft.com/en-us/download/details.aspx?id=5166, Retrieved on September 05, 2012 Mische, Michael, “Strategic Renewal: Becoming a High-Performance Organization”, Prentice Hall, 2001 Oracle, “Oracle Cloud Computing”, 2010, Online, http://www.oracle.com/us/technologies/cloud/oracle-cloud-computing-wp-076373.pdf Oracle, “Oracle JD Edwards Cloud Computing: Choosing a deployment strategy that fits”, October 2012, Online, http://www.oracle.com/us/products/applications/jd-edwardsenterpriseone/jde-cloud-computing-wp-1851596.pdf, Retrieved on Nov 21, 2012 Sharma, B.; Thulasiram, R.K.; Thulasiraman, P.; Garg, S.K.; Buyya, R.; "Pricing Cloud Compute Commodities: A Novel Financial Economic Model," Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium, vol., no., pp.451-457, 13-16 May 2012 Tata Consultancy Services, “The State of Adoption of Cloud Applications”, October 2012, Online,http://sites.tcs.com/cloudstudy/the-state-of-adoption-of-cloudapplications#.ULgBQ-Q8B8G, Retrieved on Nov 15, 2012 Walker, E.; "The Real Cost of a CPU Hour," Computer, vol.42, no.4, pp.35-41, April 2009 Xinhui Li; Ying Li; Tiancheng Liu; Jie Qiu; Fengchun Wang; , "The Method and Tool of Cost Analysis for Cloud Computing," Cloud Computing, 2009. CLOUD'09. IEEE International Conference on, vol., no., pp.93-100, 21-25 Sept. 2009 Zhipeng Wu; Aiping Gan; , "Qualitative and Quantitative Analysis the Value of Cloud Computing," Information Management, Innovation Management and Industrial Engineering (ICIII), 2011 International Conference on , vol.2, no., pp.518-521, 26-27 Nov. 2011 Varia, Jinesh, “The Total Cost of (Non) Ownership of Web Applications in the Cloud”, August 2012, Online, http://media.amazonwebservices.com/AWS_TCO_Web_Applications.pdf, retrieved on Nov 01, 2012 PRAVIN K ASAR pravinasar.com 21

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