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A Computer Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page M S BE aG F 48 Year (T) ΔNPV (thousands of dollars) –50 COVER FEATURE 0 –100 –150 –200 –250 –300 0 2 4 6 Operational years 8 10 12 Figure 1. The calculated NPV values for a medium-size enterprise deciding whether to buy or lease storage with life expectancy from 0 to 10 years. ΔNPV (thousands of dollars) 150 100 50 0 –50 –100 –150 –200 0 2 4 6 8 10 12 Operational years Figure 2. The calculated NPVs for a large-size enterprise deciding whether to buy or lease storage with life expectancy from 0 to 10 years. 500-Gbyte disk drives ( = 500), with a present cost of $150 per drive (K = $0.30), each specified to consume 0.01 kW of power. Finally, we assume the electric utility cost is $0.04 per kilowatt hour (kWh), and the end-of-life disk salvage depreciation factor is 0.1. Table 3 shows the worksheet for the buy-or-lease deci- COMPUTER Table 2. Hypothetical storage cloud pricing structure. Default Storage > 50 Tbytes Storage > 100 Tbytes Storage > 500 Tbytes $0.15/Gbyte/month $0.14/Gbyte/month $0.13/Gbyte/month $0.12/Gbyte/month Storage requirement in Gbytes (V T ) Purchased drives Table 3. Buy-or-lease decision model worksheet for a single-user computer. Operating cost (C T ) Capital cost (E T ) Lease payment (L T ) Recurring cost (CPV T ) sion model for an expected storage life expectancy of 0 to 6 years. We compute the values for the “purchased drive” column using V T 500 – V T–1 500 , and the NPVs using Equation 1. In addition, we calculate the recurring costs (CPV T ) and salvage values (SPV T ) representing terms in the formula: ΔNPV = CPV + SPV – C T T C – E + I T T T CPV = T ( 1 + I K ) T T ∑ T – 0 SPV . T = S ( 1 + I K ) T Salvage value (SPV T ) Incremental net present value (NPV) 0 100 1 ($178.70) $154.50 $98 ($236) $15.00 ($1,221) 1 200 0 ($178.70) $2.90 $278 ($141) $9.58 ($1,131) 2 300 0 ($178.70) $1.87 $458 $131 $6.12 ($863) 3 400 0 ($178.70) $1.21 $638 $575 $3.91 ($421) 4 500 0 ($178.70) $0.78 $818 $1,188 $2.50 $190 5 600 1 ($182.21) $17.79 $998 $1,947 $3.19 $950 6 700 0 ($182.21) $0.65 $1,178 $2,884 $2.04 $1,886 Notes: C = $1,000, = 500 Gbytes, Κ = $0.30 ($150 per 500 Gbytes), = 0, = 0.1, = $0.04, P = 0.5 kW, P = 0.01 kW, and I = 1 percent. C D K The worksheet shows that for an expected storage life expectancy of less than four years, leasing is always the preferred option. This is because of the high cost of the disk controller the user purchases at the outset. If the storage system will likely exist for more than four years, however, purchasing storage is the superior choice. Medium-size enterprises Medium-size enterprises—those with clusters of tens to hundreds of servers— made up approximately 50 percent of all data centers installed in the US from 2000 to 2007. 12 We assume our medium-size enterprise has a storage requirement that grows at 1 Tbyte a year to serve a moderate-size data center of tens or low hundreds of servers. For such an enterprise, the human operator burden in owning and operating a storage cluster will be larger A Computer Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page M S BE aG F
A Computer Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page M S BE aG F than that of leasing from the storage cloud vendor. We therefore assume the proportional difference in the operator’s level of effort required to manage the system/data (H T = $70,000 per year) is = 0.5. We assume that the firm must purchase an enterprise-class RAID disk controller (C = $2,000), specified to consume 0.7 kW of power. Also, as storage is needed, the enterprise will purchase 1-Tbyte disk drives ( = 1,000), with a present cost of $300 per drive (K = $0.30), each specified to consume 0.01 kW of power. Finally, we assume the electric utility cost is $0.04 per kWh, and the end-of-life disk salvage depreciation factor is 0.1. Figure 1 shows the calculated NPV values for a storage life expectancy of 0 to 10 years. In all the operational lifetimes examined, the model shows that leasing is always preferable to purchasing storage. In this case, the clear recommendation to the medium-size enterprise is to lease storage from the storage cloud vendor. Large-size enterprises Next, we look at the benefits of purchasing versus leasing storage for a large-size enterprise—for example, a data center with thousands of servers. In this scenario, we assume the large enterprise’s storage requirement grows at 10 Tbytes per year. In this case, the human operator burden in owning and operating a storage cluster is even larger than that of leasing from the storage cloud vendor. We therefore assume the proportional difference in the operator’s level of effort required to manage the system/data (H T = $70,000/year) is = 1.0. We also assume that the firm must purchase an enterprise-class RAID disk controller (C = $2,000), specified to consume 0.7 kW of power. Furthermore, we assume the controller has a peak capacity of 100 Tbytes, and the firm will purchase additional controllers as the storage need arises. For the actual storage, the enterprise will purchase 1-Tbyte disk drives ( = 1,000), with a present cost of $300 per drive (K = $0.30), each specified to consume 0.01 kW of power. Finally, as before, we assume the electric utility cost is $0.04 per kWh, and the end-of-life disk salvage depreciation factor is 0.1. Figure 2 shows the calculatedNPVs for a storage life expectancy of 0 to 10 years. As the graph shows, leasing storage is advantageous up to a nine-year storage life expectancy. After that, it becomes more advantageous for the enterprise to purchase and maintain a storage cluster. Thus, the final decision to buy or lease storage will depend on the expected use of the storage and data. For example, if the storage is destined for use by a server cluster with a five-year life expectancy, the enterprise should lease storage. However, if the storage is destined for a long-term archival system with an indefinite life expectancy, the enterprise should purchase storage instead. LATENCY IS NOT ZERO A common flawed assumption in designing distributed systems is the notion that latency is zero. In fact, latency isn’t zero for cloud services. Accessing storage from across the commodity Internet can incur a substantial cost in terms of I/O latency. Our model doesn’t account for this latency. However, future extensions can incorporate this factor by estimating the profit parameter, P T , which we assumed to be equal when deriving our current model. We can use this profit parameter to reward services with faster response times. For example, an enterprise might naïvely consider a service that’s two times faster to be more productive, and hence two times more profitable. For the moment, we leave this substantive extension to a future time. Our primary purpose in this article is to stimulate discussion, debate, and future work in the quantitative modeling of the cloud <strong>computing</strong> industry. To this end, we propose a model to assist consumers, researchers, and policy makers in estimating the benefit of leasing from storage clouds. Ultimately, an organization’s buy-or-lease decision will depend on their anticipated parameters in the analysis. Our model simply provides a first stepping-stone for rational decision making to prevail in the cloud <strong>computing</strong> market. Acknowledgments This article is based on work supported in part by US National Science Foundation grant 0721931. References 1. K. Chandar, “SSD & HDD Market Tracker,” iSuppli research report, 2009; www.isuppli.com/Abstract/ ________________________________ ABSTRACT - SSD_HDD Market Tracker 2009.pdf. 2. P. Lyman and H.R. Varian, “How Much Information?” Oct. 2003, School of Information Management and Systems, Univ. of Calif., Berkeley; ______________ www2.sims.berkeley. _____________________________ edu/research/projects/how-much-info-2003. 3. N. Leavitt, “Is Cloud Computing Really Ready for Prime Time?” Computer, Jan. 2009, pp. 15-20. 4. A. Henry, “Keynote Address: Cloud Storage FUD (Failure, Uncertainty, and Durability),” presented at the 7th Usenix Conf. File and Storage Technologies, 2009; www.usenix.org/media/events/fast09/tech/ ____________ videos/henry.mov. 5. R.W. Johnson and W.G. Lewellen, “Analysis of Leaseor-Buy Decision,” J. Finance, vol. 27, no. 4, 1972, pp. 815-823. 6. G.B. Harwood and R.H. Hermanson, “Lease-or-Buy Decisions,” J. Accountancy, vol. 142, no. 3, 1976, pp. 83-87. APRIL 2010 A Computer Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page M S BE aG F 49
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