computing lives - FTP Directory Listing
computing lives - FTP Directory Listing
computing lives - FTP Directory Listing
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than that of leasing from the storage cloud vendor. We<br />
therefore assume the proportional difference in the operator’s<br />
level of effort required to manage the system/data<br />
(H T = $70,000 per year) is = 0.5. We assume that<br />
the firm must purchase an enterprise-class RAID disk<br />
controller (C = $2,000), specified to consume 0.7 kW<br />
of power. Also, as storage is needed, the enterprise will<br />
purchase 1-Tbyte disk drives ( = 1,000), with a present<br />
cost of $300 per drive (K = $0.30), each specified<br />
to consume 0.01 kW of power. Finally, we assume the<br />
electric utility cost is $0.04 per kWh, and the end-of-life<br />
disk salvage depreciation factor is 0.1.<br />
Figure 1 shows the calculated NPV values for a storage<br />
life expectancy of 0 to 10 years. In all the operational lifetimes<br />
examined, the model shows that leasing is always<br />
preferable to purchasing storage. In this case, the clear<br />
recommendation to the medium-size enterprise is to lease<br />
storage from the storage cloud vendor.<br />
Large-size enterprises<br />
Next, we look at the benefits of purchasing versus leasing<br />
storage for a large-size enterprise—for example, a<br />
data center with thousands of servers. In this scenario, we<br />
assume the large enterprise’s storage requirement grows<br />
at 10 Tbytes per year.<br />
In this case, the human operator burden in owning and<br />
operating a storage cluster is even larger than that of leasing<br />
from the storage cloud vendor. We therefore assume<br />
the proportional difference in the operator’s level of effort<br />
required to manage the system/data (H T = $70,000/year)<br />
is = 1.0. We also assume that the firm must purchase an<br />
enterprise-class RAID disk controller (C = $2,000), specified<br />
to consume 0.7 kW of power. Furthermore, we assume<br />
the controller has a peak capacity of 100 Tbytes, and the<br />
firm will purchase additional controllers as the storage<br />
need arises.<br />
For the actual storage, the enterprise will purchase<br />
1-Tbyte disk drives ( = 1,000), with a present cost of<br />
$300 per drive (K = $0.30), each specified to consume 0.01<br />
kW of power. Finally, as before, we assume the electric utility<br />
cost is $0.04 per kWh, and the end-of-life disk salvage<br />
depreciation factor is 0.1.<br />
Figure 2 shows the calculatedNPVs for a storage life<br />
expectancy of 0 to 10 years. As the graph shows, leasing<br />
storage is advantageous up to a nine-year storage life<br />
expectancy. After that, it becomes more advantageous for<br />
the enterprise to purchase and maintain a storage cluster.<br />
Thus, the final decision to buy or lease storage will depend<br />
on the expected use of the storage and data. For example,<br />
if the storage is destined for use by a server cluster with<br />
a five-year life expectancy, the enterprise should lease<br />
storage. However, if the storage is destined for a long-term<br />
archival system with an indefinite life expectancy, the<br />
enterprise should purchase storage instead.<br />
LATENCY IS NOT ZERO<br />
A common flawed assumption in designing distributed<br />
systems is the notion that latency is zero. In fact, latency isn’t<br />
zero for cloud services. Accessing storage from across the<br />
commodity Internet can incur a substantial cost in terms<br />
of I/O latency. Our model doesn’t account for this latency.<br />
However, future extensions can incorporate this factor by<br />
estimating the profit parameter, P T , which we assumed to<br />
be equal when deriving our current model. We can use this<br />
profit parameter to reward services with faster response<br />
times. For example, an enterprise might naïvely consider a<br />
service that’s two times faster to be more productive, and<br />
hence two times more profitable. For the moment, we leave<br />
this substantive extension to a future time.<br />
Our primary purpose in this article is to stimulate<br />
discussion, debate, and future work in the quantitative<br />
modeling of the cloud <strong>computing</strong> industry.<br />
To this end, we propose a model to assist consumers,<br />
researchers, and policy makers in estimating the<br />
benefit of leasing from storage clouds.<br />
Ultimately, an organization’s buy-or-lease decision will<br />
depend on their anticipated parameters in the analysis.<br />
Our model simply provides a first stepping-stone for rational<br />
decision making to prevail in the cloud <strong>computing</strong><br />
market.<br />
Acknowledgments<br />
This article is based on work supported in part by US National<br />
Science Foundation grant 0721931.<br />
References<br />
1. K. Chandar, “SSD & HDD Market Tracker,” iSuppli<br />
research report, 2009; www.isuppli.com/Abstract/<br />
________________________________<br />
ABSTRACT - SSD_HDD Market Tracker 2009.pdf.<br />
2. P. Lyman and H.R. Varian, “How Much Information?”<br />
Oct. 2003, School of Information Management and Systems,<br />
Univ. of Calif., Berkeley; ______________<br />
www2.sims.berkeley.<br />
_____________________________<br />
edu/research/projects/how-much-info-2003.<br />
3. N. Leavitt, “Is Cloud Computing Really Ready for<br />
Prime Time?” Computer, Jan. 2009, pp. 15-20.<br />
4. A. Henry, “Keynote Address: Cloud Storage FUD<br />
(Failure, Uncertainty, and Durability),” presented at<br />
the 7th Usenix Conf. File and Storage Technologies,<br />
2009; www.usenix.org/media/events/fast09/tech/<br />
____________<br />
videos/henry.mov.<br />
5. R.W. Johnson and W.G. Lewellen, “Analysis of Leaseor-Buy<br />
Decision,” J. Finance, vol. 27, no. 4, 1972, pp.<br />
815-823.<br />
6. G.B. Harwood and R.H. Hermanson, “Lease-or-Buy<br />
Decisions,” J. Accountancy, vol. 142, no. 3, 1976, pp.<br />
83-87.<br />
APRIL 2010<br />
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