How Auto-Scaling Techniques Make Public Cloud Deployments More Cost-Effective By moving to public cloud infrastructure, IT organizations can apauto-scaling techniques in which they pay only for the resources are used. This can eliminate unused infrastructure during seasonbusiness slowdowns, resulting in significant cost savings.

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Executive SummaryPublic cloud technologies have been widely adopted by start-ups and small businesses due to their low cost, flexibility and brief set-up time required for the infrastructure. For larger estab-lished enterprises, however, the infrastructure set-up time is less of a concern, particularly where private cloud hosting and virtualization have reduced provisioning time considerably. In such organizations, the smaller number of benefits and concerns over data security have meant that public cloud adoption has been less forthcoming. This trend has been particularly noted in the banking sector.

This white paper focuses on a key benefit of public cloud hosting for larger and more established enterprises that has largely gone unnoticed: scalability.

The auto-scaling feature in Amazon Web Services (AWS), in particular, allows elastic compute cloud (EC2) instances to be scaled out automatically following set rules around CPU, memory and I/O utilization. For example, if a website needs only two Web servers for 11 months of the year,

but 15 servers during the Christmas month, the auto-scaling feature makes public cloud hosting far less expensive than private cloud implemen-tations. Traditional on-premises hosting would have required the capacity of all 15 servers throughout the year even when most of the servers are not used. With auto-scaling in public cloud, however, companies pay only for the two servers for 11 months, which automatically increases to 15 during the month of December based on usage.

An analysis of U.S. Census retail data from 2015 shows a clear distinction in spending habits at various times of the year. Retail customers who shop in categories such as fuel dealers, and hobby/toy and game stores spend over 220% during the peak month of the year compared with the lowest spending month. Traditional on-premises hosting would result in tremendous amounts of wasted IT resources, by reserving capacity throughout the year to address peak demand that corresponds with heavy usage periods such as Thanksgiving, Black Friday, Christmas and New Year’s.

cognizant 20-20 insights | november 2016

• Cognizant 20-20 Insights

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The waste of computing resources is particu-larly prevalent among those systems that handle e-commerce, payment and back-end real-time processing, such as stock tracking and accounts. This paper also offers a snapshot of some such systems in both the retail and retail banking sectors.

By using the auto-scaling features of public cloud, up to 57% in cost savings can be achieved for such systems, on the assumption that there is a linear correlation between spending amounts and IT resource requirements. Analyzing the savings trends also shows that the minimum required server count for such applications should be kept between four and six load-bal-anced servers rather than a smaller number of large servers.

The Capacity Management ProblemThe capacity management problem has long been a challenge for many enterprises. First, there is a need to forecast the demand for new IT resources based on future projects, and second, there’s the need to optimize available resources for systems that are already live. Much time and resources are spent trying to forecast these two requirements, and ensure that the optimum con-figuration is ready to provision services quickly and to reduce infrastructure resource waste.

Despite these efforts, the traditional on-premis-es hosted IT service typically results in wasted resources.

For the live systems, the peak workload is estimated and resources set aside to meet this demand at the most critical time frames. The Christmas rush, for instance, is a key example where retailers set aside large amounts of IT in advance and in preparation for the big event.

In fact, research from Net Retail Federation suggests that additional peaks during Black Friday and Cyber Monday are starting to emerge, and are likely to grow in the coming years.1

Similarly, in organizations where there is a large amount of change, physical server capacity is kept aside (e.g., for private cloud) to provision virtual machines quickly and on demand when required. Therein also lies potential waste where it is practically impossible to have precisely the right amount of physical hardware ready for virtual machine provisioning.

This section explores the first of the two problems noted above — which is the requirement for live systems to predict utilization throughout the year and set aside sufficient capacity to meet peak demand.

The key data set used for carrying out this analysis is 2015 U.S. Census retail data, which in turn is based on the Monthly Retail Trade Survey, Annual Retail Trade Survey and adminis-trative records.2 The data reveals the estimated monthly sales for various retail categories.

With this data as a starting point, the monthly estimates were used to calculate the data presented in Figure 1 (next page).

Our analysis shows that in some categories, there was a 230% increase in monthly sales volumes compared with the lowest month.

Translating this to IT, we assume that the capacity of IT resources required to manage sales volumes is linearly proportional to the revenue recognized. Fuel dealers, for example, will therefore need 230% more in IT resources during peak months compared with lean sales months.

By using the auto-scaling features of public cloud, up to 57% in cost savings

can be achieved for such systems, on the assumption that there is a linear

correlation between spending amounts and IT resource requirements.

Fuel dealers, for example, will therefore need 230% more in IT resources during peak months compared with lean sales months.

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Therein lies the problem with traditional IT infra-structure. Since IT infrastructure is dependent on physical hardware which is time-consuming to purchase and provision, huge amounts of wasted resources are typically deployed to accommo-date peak demand, which means much of the infrastructure may lie fallow at other times of the year.

IT Systems Related to Sale VolumesVarious core systems are likely to have a direct or indirect impact on the fluctuating sale volumes presented in Figure 1.

Figure 2 (next page) offers a cross-sectional view of the systems used by retailers and banks that are likely to have an impact on sales figures.

Front End

In retail, consumers start the buying process through one of three channels — mobile, Web or retail shops (either in a brick-and-mortar setting or via telephone). These channels are typically backed by a content management system and portal, which includes Web, application and database servers that can all benefit from auto-scaling features. In the banking sector, consumers interact and transact through their

Top 20 Estimates of Monthly 2015 Retail and Food Services Sales by Business Type

Rank

Category

Max Month Sale

Min Month Sale

Max-Min

% Diff

1 Fuel dealers 4,476 1,358 3,118 230%

2 Hobby, toy, and game stores 3,723 1,154 2,569 223%

3 Jewelry stores 5,807 1,841 3,966 215%

4 Department stores (excl. discount department stores) 9,257 3,493 5,764 165%

5 Department stores (excl. discount department stores) 9,623 3,639 5,984 164%

6 Book stores 1,633 690 943 137%

7 Gift, novelty, and souvenir stores 2,613 1,130 1,483 131%

8 Sporting goods, hobby, book, and music stores 12,897 5,716 7,181 126%

9 All other home furnishings stores 4,016 1,841 2,175 118%

10 Department stores (incl. L.D.)(3) 23,820 10,957 12,863 117%

11 Department stores (excl. L.D.) 23,425 10,797 12,628 117%

12 Sporting goods stores 6,209 2,870 3,339 116%

13 Clothing and clothing access. stores 33,123 15,733 17,390 111%

14 Family clothing stores 12,565 6,040 6,525 108%

15 Women’s clothing stores 4,984 2,537 2,447 96%

16 Clothing stores 22,884 11,649 11,235 96%

17 Discount dept. stores 14,168 7,297 6,871 94%

18 Discount dept. stores 14,197 7,312 6,885 94%

19 Shoe stores 3,938 2,029 1,909 94%

20 Electronic shopping and mail-order houses 55,765 29,879 25,886 87%

Category: These are the categories as provided in the U.S. Census retail data. Max Month Sale: This is the sales volume, in millions of dollars, for the month that gave the highest sales in 2015. Min Month Sale: This is the sales volume, in millions of dollars, for the month that gave the lowest sales in 2015. Max-Min: This column provides the difference between the maximum and minimum sale months. % Diff: This column presents the Max–Min as a percentage of the minimum sale month. This column essentially provides an estimate of monthly sales figure disparities.

Note: Data for the top 20 categories was sorted based on the “% Diff” column.Source: 2015 U.S. Retail Sales and Inventory DataFigure 1

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mobile, Web or internal core systems. These systems are also likely to be utilized more heavily during peak sales volumes.

Middle Tier

Following the front end, there is typically a tier that handles the payments themselves. This is usually shared between retailers (providing the front end) and banks (providing the back end) for processing the actual payments and trans-actions. Such systems tend to work in real time3 that varies in utilization based on sales trends.

Back End

At the back end of retail organizations, numerous real-time systems keep track of inventory and the product purchase process. In banking, similarly, there are back-end systems that keep track of accounts and manage the transactions.

Savings Through Auto-ScalingUsing U.S. retail data, sales figures can be mapped to the actual server capacity required for each application tier to estimate the savings achievable through auto-scaling.

With the monthly sales figures available for each category, it is possible to extrapolate the server count required for each month. Using fuel dealers as an example, the month of August saw the lowest sales figures. If we assume that N servers are required to meet this demand in August, then the server counts for the remaining months can be extrapolated as a ratio of the August sales using the following formula:

Server count for Month A = (N x Sales for Month A) / Lowest Month Sales

Figure 3 (next page) provides the estimated server count based on the above formula for each of the 12 months in 2015 when N is assumed to be six servers. Using these figures, the table also calculates the total number of server months required in 2015, based on auto-scaling, and compares it with the total without auto-scaling. The server count without auto-scaling is estimated using the peak server month x 12 months (e.g., the maximum capacity required in any month is assumed to be in place for all months in the year).

Where Auto-Scaling Benefits Retail and Banking Systems

Retail

Mobile Store Web Mobile Bank Web

Banking

Shared Payment Processing Systems

Data Tier Back Office

Stock Control Accounts

Presentation Presentation

Figure 2Figure 2

With these two numbers, we are able to calculate the saving possible as total (no auto-scaling) — total (auto-scaling) / total (auto-scaling).

The results show that for the hobby/toy and game stores category, there is a 57% saving possible by using the auto-scaling feature when compared with no auto-scaling — as is the tradi-tional case with on-premises deployments.

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The results show that for the hobby/toy and game stores category, there is a 57% saving

possible by using the auto-scaling feature.

Savings with Auto-Scaling: Top 20

No.

Category J F M A M J J A S O N D

Total(Auto-

Scale)

Total(No Auto-

Scale)

Saving

1 Fuel dealers 20 20 16 10 7 7 7 6 7 9 9 11 129 240 46%

2Hobby, toy, and game stores

6 7 8 7 7 7 7 7 7 8 12 20 103 240 57%

3 Jewelry stores 6 9 7 8 9 8 7 8 7 7 8 19 104 228 54%

4Department stores (excl. discount department stores)

6 7 8 8 9 8 8 8 8 8 11 16 105 192 45%

5Department stores (excl. discount department stores)

6 7 8 8 9 8 8 8 8 8 11 16 105 192 45%

6 Book stores 13 7 7 6 7 7 7 15 10 7 7 13 106 180 41%

7Gift, novelty, and souvenir stores

6 7 7 8 9 9 9 9 8 10 9 14 105 168 38%

8Sporting goods, hobby, book, and music stores

7 6 8 7 8 8 8 9 8 8 9 14 100 168 40%

9All other home furnishings stores

7 6 8 7 8 8 8 8 8 8 10 14 100 168 40%

10Department stores (incl. L.D.)(3)

6 7 8 7 8 7 7 8 7 8 10 14 97 168 42%

11Department stores (excl. L.D)

6 7 8 7 8 7 7 8 7 8 10 14 97 168 42%

12 Sporting goods stores 7 6 8 8 9 9 9 10 8 8 9 13 104 156 33%

13Clothing and clothing access. Stores

6 7 8 8 9 8 8 9 8 8 9 13 101 156 35%

14 Family clothing stores 6 7 8 8 8 7 8 8 7 8 18 13 106 156 32%

15Women’s clothing stores

6 7 9 9 9 8 8 9 8 9 9 12 103 144 28%

16 Clothing stores 6 7 9 8 9 8 8 9 8 9 10 12 103 144 28%

17 Discount dept. stores 7 6 8 7 8 7 7 8 7 7 9 12 93 144 35%

18 Discount dept. stores 7 6 8 7 8 7 7 8 7 7 9 12 93 144 35%

19 Shoe stores 6 8 9 8 9 8 9 11 8 8 9 12 105 144 27%

20Electronic shopping and mail-order houses

7 6 7 7 7 7 7 7 7 8 9 12 91 144 37%

Source: 2015 U.S. Retail Sales and Inventory DataFigure 3

Optimal Base ArchitectureBased on the formula for calculating the server count required in each month (as shown above), we can now explore the impact of modifying the value of N. As a reminder, N is the minimum number of servers that make up the IT infra-structure required for the application at a time when the system is least utilized.

By varying the value of N, it was observed that the percentage savings figure also changes. Figure 4 plots the average cost savings achieved across every category in the U.S. retail data against the value of N.

The results show that the cost savings are low when the value of N is low. However, as N increases, there comes a point when the cost savings stabilizes. Based on this data set, the optimum value of N is seen as six servers.

This result shows that the most cost-effective architecture for an application when utilizing auto-scaling is between four and six servers as a minimum for the lowest utilized periods. A smaller number of servers results in a lower cost saving. A higher number of base servers does not provide any further cost savings.

Moving ForwardGiven the above analysis, we recommend the following:

• Utilize public cloud services for the right reasons. Auto-scaling certainly will bring businesses big cost savings when applied to consumer-facing systems.

• Ensure that IT teams include DevOps skills that are required to exploit auto-scaling features to its fullest.

• Perform deeper analysis on individual businesses to identify the true return on investment (ROI) in exploiting auto-scaling features.

The underlying premise behind the data presented in this paper is that there exists a linear relationship between sales volumes and IT resource utilization. Further analysis should be carried out to measure utilization trends on a monthly basis to refine the cost saving figures.

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18%

16%

14%

12%

10%

8%

6%

4%

2%

0%1 2 3 4 5 6 7 8 9 10 11 12

Number of Servers

Cost Savings vs. Base Server Count

Figure 4

About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 100 development and delivery centers worldwide and approximately 255,800 employees as of September 30, 2016, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top per-forming and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.

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Footnotes1 Matthew Shay, “Black Friday: Changing, but far from gone,” Stores, National Retail Federation, December

16, 2015, https://nrf.com/news/black-friday-changing-far-gone.

2 “Annual Revision of Monthly Retail and Food Services: Sales and Inventories—January 1992 Through March 2016,” U.S. Census, http://www.census.gov/retail/mrts/www/benchmark/2016/html/annrev16.html.

3 “The Global Adoption of Real-Time Retail Payment Systems (RT-PS),” SWIFT 2015, https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiv68qerInNAhUlIcAKHXPuAlUQFggjMAA&url=https%3A%2F%2Fwww.swift.com%2Fnode%2F4716&usg=AFQjCNFG-0RiwEfZGpdOrajx7kX_0sHzPw&sig2=uqZg9WKS6Sy_LCYTl8gmmg.

About the AuthorsNaveen Thomas is a Senior Manager with Cognizant Infrastructure Services. He has over 12 years of consulting experience at several leading consulting firms, focusing predominantly on innovation, cloud technologies and infrastructure consulting. He holds a Ph.D. in computer vision and a master’s degree in computer systems engineering from the University of Bristol. Naveen can be reached at Naveen.Thomas@cognizant.com.

Alec Selvon-Bruce is a Director and Practice Lead for Enterprise Computing and Cloud Practice at Cognizant Infrastructure Services in EMEA. He has over 22 years of professional services experience across leading system integrations and technology providers. Alec drives the innovation councils that provide guidance on infrastructure and cloud strategy. He holds a master’s degree in political studies from the University Of Aberdeen. Alec can be reached at Alec.Selvon-Bruce@cognizant.com.

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