5 5 Problem: Uncertainty Various pricing mechanisms are changing Source: Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., and Tsafrir, D. (2011). Deconstructing Amazon EC2 spot instance pricing.
6 6 Problem: Uncertainty Various service specifications
7 7 Consequence Misalignment between business goals and use of cloud computing services
8 8 Research Questions 1) What are the major types of pricing methods currently used by cloud services vendors? 2) What are the key factors that should be considered in pricing cloud computing services? 3) Why should a cloud services vendor be interested in multiple pricing approaches? Related to this, how should Amazon s EC2 mixed pricing strategy, with both fixed reserve pricing and dynamic spot pricing, be evaluated? 4) What are the key variables that will affect clients valuation of cloud services? How will they affect clients willingness-to-pay for customized cloud services?
9 9 Research Model Standard Cloud Services Essay 1 General Price Formula for Cloud Services Analysis of Different Pricing Channels Examining Customers Willingness-to- Pay for the Customized Cloud Service Essay 3 Market Survey of Pricing Practices Customized Cloud Services Essay 2
10 10 Essays 1) Modeling reserved and spot cloud services 2) A pricing experiment for cloud services 3) Pricing practices in the cloud computing services market
11 11 Modeling reserved and spot cloud services What I do Provide new knowledge Analysis of fixed price reserved cloud computing services versus spot price services How I do Analytical modeling and simulation
12 12 Modeling reserved and spot cloud services model settings One vendor Many clients with job arrivals (λ 1, λ 2, λ 3 ) Job value uniformly distributed in the range of [v L, v H ] Two services: reserved and spot Fixed price for reserved service Reserved contract (T, N) Dynamic price for spot service Spot price Two price levels: low price and high price (p L, p H ) Each price is associated with a probability (θ L, θ H ) k stages, spot price refreshes in each stage Clients sensitivity to service interruption: γ
13 Modeling reserved and spot cloud services k stage timeline Buy reserved contract (T, N) or not λ i λ i λ i λ i λ i λ i λ i λ i λ i λ i λ i λ i k-2 k-1 k Time p s changes In-between stages: task submission On Spot Reserved
14 14 Modeling reserved and spot cloud services k stage timeline After introducing spot service: Reserved contract price decreases The reduction of price decreases in γ Market share of reserved service decreases The reduction of market share is convex in θ L The reduction of market share decreases in γ The reduction of market share is larger when the price ratio p H /p L is larger. Vendor s total profit increases in γ For the vendor, optimal θ L increases in γ Condition for a higher vendor profit: p H /p L is small and p H is close to the expected job value.
15 15 A pricing experiment for cloud services What I do Investigate factors affecting clients willingness-to-pay for a customized cloud computing services Customization of cloud computing services is related to the level of risk of service interruption How I do Experimental work and econometric modeling
16 16 A pricing experiment for cloud services Basic empirical model: WTP i,j = β 1 Price Reserved + β 2 Price Spot + β 3 RiskScore i + β 4 RiskInfo i + β 5 TaskDuration j + ε
17 17 A pricing experiment for cloud services Risk analysis support On-demand Spot (Statistics of Historical Purchases) Occurre nce Total Payment Expected Profit Job completed 100% $240.0 ($0.80 X 300) $760.0 Job not completed 0% N/A N/A Job completed 98.9% $87.74 $ Job not completed 1.1% N/A $ *finish the task: $1000 rewards *failed to finish the task: $200 penalty
18 18 A pricing experiment for cloud services
19 19 A pricing experiment for cloud services - Experiment Workflow Login Read Informed Consent Form Quiz Task 3 Task 2 Task 1 Questionnaire Task 1: 3 hours, 100 instances Task 2: 5 hours, 100 instances Task 3: 10 hours, 100 instances One week later Risk Propensity Measurement
20 20 A pricing experiment for cloud services Preliminary findings Risk analysis support significantly increases participants willingness-to-pay. Interaction effects (risk analysis support and job risk, risk analysis support and participants risk propensity) are uncertain.
21 21 Pricing practices in the cloud computing services market What I do Study the macro-structure of pricing practices in the cloud computing services market A general pricing framework for cloud computing services market How I do Market survey of pricing mechanisms implemented by representative cloud computing services vendors
22 22 Pricing practices in the cloud computing services market Criteria for selection of cloud computing services vendors The vendor must make pricing information on all its services available on its official web site The vendor must have been selected at least once for review in Gartner s Magic Quadrant Report 18 cloud services vendors, 29 different types of services
23 23 Pricing practices in the cloud computing services market Category Factor Definition Unit Service Type Services specifications (OS, size, location, etc.) Categorical Usage Unit Price Unit price of usage $ / Unit Total Usage In units of usage Units Tength of period that the service is exclusively Reservation Period Reservation reserved for the client s use Hours Reservation Fee One-time advance fee for reserved services $ Technical Support Type Characteristics of technical support Categorical Support Support Charge Periodic payment for technical support $ Total Outage Length of service down time Hours Penalty Monetary penalty for vendor not fulfilling the Compensation promised level of service quality $
25 25 Variable Name Definition T Fixed price for reserved services contract N Resource capacity of reserved services contract v Value of a single job v L v H θ L Lower bound of job value Upper bound of job value Probability of low spot price θ H p L p H γ λ i λ Probability of high spot price, θ H = 1 - θ L Low spot price High spot price Clients sensitivity coefficient to services interruption Job arrival rate of client i Maximum job arrival rate of a client
26 26 N Range Minimum Maximum Mean Std. Deviation Statistic Statistic Statistic Statistic Statistic Statistic Risk Propensity Age Working Experience Decision Making Experience Cloud Services Usage Experience Negotiation Experience Analytics Experience
27 27 Pricing information goods Etzion, H., Pinker, E., and Seidmann, A. (2006). Analyzing the simultaneous use of auctions and posted prices for online selling. Manufacturing and Service Operations Management, 8(1), Fishburn, Peter C., Andrew M. O., and Ryan C. S. (2000). Fixed fee versus unit pricing for information goods: competition, equilibria, and price wars. In D. Hurley, B. Kahin, and H. Varian (eds.), Internet Publishing and Beyond: The Economics of Digital Information and Intellectual Property, , MIT Press, Boston, MA. Sridhar, B., Bhattacharya, S., and Krishnan, V. (2009). Pricing information goods: A strategic analysis of the selling and on-demand pricing mechanisms. Working paper, Smeal College of Business, Pennsylvania State University, College Park, PA. Sundararajan, A. (2004). Nonlinear pricing of information goods. Mgmt. Sci., 50(12), Varian, H. R. (1995). Pricing information goods. In Proceedings of Scholarship in the New Information Environment Symposium, Harvard Law School, Boston, MA.
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