German Retana,Chris Forman, Sridhar Narasimhan,Marius Florin Niculescu, D.J. Wu

2012 Atlanta-Athens Conference on Research in Information Systems

March 31, 2012

Retana et al. | AACRIS 2012 | March 31, 2012. 2

“even the smallest company can now afford a communications and computational infrastructurethat would have been the envy of large corporation 15 years ago”

Varian, FP 2010 “The illusion of infinite computing resources available on demand” eliminates the need “to plan far ahead for provisioning”

“The elimination of an up-front commitment by Cloud users” allows firms to increase resources only when needed .

Armbrust et al., Berkeley 2009

“The real strength of cloud computing is that it is a catalyst for more innovation .”

Brynjolfsson et al., CACM 2010

IaaS forecasted to grow from an estimated $3.7 billion in 2011 to $10.5 billion in 2014

Gartner, 2011

“A key economic advantage of the cloud is its ability to address variability in resource utilization ”

Harms & Yamartino, Microsoft, 2010

Retana et al. | AACRIS 2012 | March 31, 2012. 3

Only 29% of SMBs were paying for one or more cloud services in Dec 2010. Expected 39% in 2011-2013 .

Microsoft, SMB Cloud Adoption Study, March 2011

Can the provider influence customers’ adoption and usage

of cloud services?

1 in 4 IT staff across 5,300 global organizations have cloud experience . 50% of organizations claim staff is “less than somewhat prepared” to handle cloud .

Symantec, State of the Cloud Survey, March 2011.

4% of 1,361 IT professionals have implemented publi c cloud (for production applications). 64% are not even considering it .

SearchDataCenter.com, Data Center Decisions 2011 Survey, July 2011.

What is the

of

a cloud infrastructure service?

Retana et al. | AACRIS 2012 | March 31, 2012. 4

Does the provider’s support influence the

by the customers?

Does the provider’s support influence the

by the customers?

Retana et al. | AACRIS 2012 | March 31, 2012. 5

Customers who use a consume, on average,

than those who don’t.

Even , customers consume than those who never used it.

IT Capacity

Customers who use a are

than those who don’t.

, customers to less complex deployments.

Architecture Complexity

Detailed cloud infrastructure services usage data from major public cloud infrastructure services provider.

Customers can choose between two different levels of support:

• Basic: only issues with service offered• Managed: assistance in adaptation(co-invention) process of cloud service

Data

Fixed effects panel data models with difference-in-difference identification strategy to test effect of upgrades or downgrades between chosen support levels.

Approach

Retana et al. | AACRIS 2012 | March 31, 2012. 6

Downgrade

�

Downgrade

Basic

Managed

0 �

Retana et al. | AACRIS 2012 | March 31, 2012. 7

No switching

�

Basic

�

Managed

0 �0 �

Upgrade

� �

0 �

ManagedManaged

Basic

Upgrade Upgrade

Basic

0 �

�

DowngradeUpgrade

Basic

Managed

0 �

���� = �� + ������������ + ��������� + �� ����������+�� + �� + �� + ���

���� = �� + ������������ + ��������� + �� ����������+�� + �� + �� + ���

Retana et al. | AACRIS 2012 | March 31, 2012. 8

Dummy variables indicating if adoption of managed support will occur in near future (in � weeks), if managed support has been adopted, or if customer stopped using managed support. �

Basic

Managed

0 �

������������ = 1

��������� = 1

����������� = 1

��

�

��

�� = Customer fixed effect

�� = Time fixed effect, period (week) in customer’s lifetime

�� = Time fixed effect, calendar period (week)

�����������������= Logged average GB RAM/hour consumed during period (week).

� �� �����= Binary variable equal to 1 if customer is using a horizontally scalable (complex) architecture, 0 otherwise.

Dependent variable is ln(GB/h+1). All regressions include lifetime and calendar time dummies. Robust standard errors, clustered on customers, in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.0001.

Retana et al. | AACRIS 2012 | March 31, 2012. 9

(1) (2) (3a) (3b)

������� 0.7061*** 0.7414*** 0.7774*** 0.8076***

(0.0416) (0.0421) (0.0452) (0.0480)

���������� -0.2906*** -0.2943*** -0.2971***

(0.0576) (0.0573) (0.0572)

�������4�� 0.1908***

(0.0326)

�������8�� 0.1999***

(0.0357)

Constant -0.5523*** -0.5497*** -0.5529*** -0.5557***

(0.0049) (0.0049) (0.0051) (0.0053)

N 775767 775767 775767 775767

R2 0.208 0.209 0.209 0.209

Dependent variable is horizontalit. All regressions include lifetime and calendar time dummies. Robust standard errors, clustered on customers, in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.0001.

Retana et al. | AACRIS 2012 | March 31, 2012. 10

(1) (2) (3a) (3b)

������� 0.1526*** 0.1485*** 0.1538*** 0.1569***(0.0200) (0.0200) (0.0216) (0.0228)

���������� 0.0340 0.0335 0.0332(0.0296) (0.0295) (0.0295)

�������4�� 0.0282+(0.0151)

�������8�� 0.0256+(0.0153)

Constant -0.5593*** -0.5596*** -0.5601*** -0.5604***(0.0025) (0.0026) (0.0027) (0.0027)

N 775767 775767 775767 775767

R2 0.036 0.036 0.036 0.036

German Retana

german.retana@mgt.gatech.edu@germanretana

2012 Atlanta-Athens Conference on Research in Information Systems

March 31, 2012

Variable Obs MeanStd.Dev.

Min Max

Non

-Tim

e-V

aryi

ng A

ttri

bute

s Service Adoption (calendar day) 15,582 546.15 236.48 4 949

Tenure (days) 15,582 351.08 236.70 1 946

Basic Accts (#) 15,582 0.99 0.88 0 35

Managed Accts (#) 15,582 0.20 0.48 0 28

Managed Adoption (day in lifetime) 3,139 41.47 119.22 1 913

Managed Churn (day in lifetime) 3,139 173.61 171.83 1 941

Days before Managed. Adoption 3,139 40.47 119.22 0 912

Days using Managed 3,139 132.14 113.56 0 538

Days before Managed Churn 3,139 172.61 171.83 0 940

Wee

kly

Pane

l IT Capacity (Avg. GBh/day) 775,767 6.67 25.20 0.00 1598.55

Max. Servers Running/day 775,767 3.80 8.16 0 518

Horizontal? (indicator/day) 775,767 0.23 0.42 0 1

Max. Parallel/day 775,767 0.83 3.42 0 236

Max Num. Sets/day 775,767 0.40 1.36 0 202

Retana et al. | AACRIS 2012 | March 31, 2012. 13

Web Web

DB

LB

Web Web

DB

LB

WebWeb

DB

• Several nodes perform same function in parallel(e.g., several web servers)

• Scale IT capacity by adding or removing parallel nodes� truly leverages on cloud’s scalability

• Involves complex synchronization of data and user sessions across nodes, specially in case of node failure (common in cloud)

• Much harder to deploy than having a single server per function.

Retana et al. | AACRIS 2012 | March 31, 2012. 14

���� = � +� ��������+������ + ���� ��� ���� �����

+� ���������� +���� ������ × ����������+�� + �� + �� + ���

���� = � +� ��������+�������� + ����� ��� ����� ×�������+� ���������� +����� ������ × ����������+�� + �� + �� + ���

����������� ��� �������

(4) (5a) (5b) (4) (5a) (5b)��������� 0.6851*** 0.6882*** 0.6836*** 0.1063** 0.1063** 0.1116**

(0.0738) (0.0739) (0.0738) (0.0355) (0.0356) (0.0355)��������������

���������

0.0004 0.0004+ 0.0004 0.0002+ 0.0002+ 0.0002(0.0002) (0.0002) (0.0002) (0.0001) (0.0001) (0.0001)

������������� -0.3113*** -0.3949*** -0.2842** 0.0307 0.0327 -0.0626+

(0.0585) (0.0768) (0.0832) (0.0299) (0.0447) (0.0344)����������

× �������������

0.0010* -0.0000(0.0004) (0.0002)

����������

× �������������

-0.0001 0.0004*

(0.0003) (0.0002)

���������8�� 0.1498*** 0.1504*** 0.1500*** 0.0146 0.0146 0.0142(0.0332) (0.0332) (0.0332) (0.0156) (0.0156) (0.0155)

Constant -0.5635 -0.4798 -0.5821 0.0322 0.0301 0.0962(0.3883) (0.3512) (0.3973) (0.2124) (0.2139) (0.1964)

N 79870 79870 79870 79870 79870 79870R2 0.344 0.344 0.344 0.093 0.093 0.094