Overprovisioned Workloads Can Add 30% to the Cost of Cloud ... · IDC has projected that cloud computing spending will grow at 6 times the rate of IT spending from 2015 to 2020 2

© 2017 TSO Logictsologic.com

Overprovisioned Workloads Can Add 30% to the Cost of Cloud Migration

December 2017

© 2017 TSO Logic tsologic.com 2TSO Logic 2017 Economics of Cloud Migration Report

Inaccurate Cost Models Obscure Cloud ValueGlobal adoption of public cloud resources continues to accelerate. Gartner forecasted that the worldwide public cloud market would grow 17 percent in 2018, reaching $287,820 billion1. IDC has projected that cloud computing spending will grow at 6 times the rate of IT spending from 2015 to 2020 2.

Clearly, more organizations believe that public cloud can deliver real value. When attempting to develop a business case for migrating their own environments, however, many organizations calculate that their projected cloud savings will not be as large as they hoped. In some cases, cloud is even more expensive.

The disconnect here is not that organizations have unrealistic expectations of what cloud can offer, but that their cost models are often based on inaccurate assumptions.

These assumptions span multiple parameters:

• That a cloud provider’s hardware is comparable to hardware currently deployed on-premise, when public cloud platforms may be several generations newer and offer significantly better price/performance.

• That hardware pricing is basically the same for on-premise and cloud platforms, when in reality, public cloud providers benefit from massive economies of scale, and often create custom hardware and software configurations.

• That current on-premise resources are properly balanced, that data center’s are efficiently managing power consumption, and many others.

Among the largest oversights is the use of incomplete “direct match” methodologies when projecting cloud costs. Baked into many cost models is the assumption that current on-premise resources are sized appropriately, and that cloud instances should be provisioned exactly as they are provisioned on-premise. New research from TSO Logic, however, reveals that most on-premise workloads—more than 80 percent— are currently overprovisioned.

When organizations employ a “rightsized match” methodology—provisioning only the cloud resources those workloads need, based on historical utilization patterns, cloud becomes less expensive—yielding average annual cost savings of 30 percent or more.

1. Gartner. “Gartner Says Worldwide Public Cloud Services Market to Grow 18 Percent in 2017.” February 22, 2017. https://www.gartner.com/newsroom/id/3616417 2. IDC. “The Salesforce Economy: Enabling 1.9 Million New Jobs and $389 Billion in New Revenue Over the Next Five Years.” September, 2016. http://www.salesforce.com/assets/pdf/misc/IDC-salesforce-economy-study-2016.pdf


Data Reveals that Most OS Instances Are Over ProvisionedLast year, TSO Logic conducted an anonymized statistical analysis of 25,000 virtual OS instances across its North American customer base.

Now, TSO Logic has expanded this research to create one of the largest data sets assembled for this type of analysis. TSO Logic statistically analyzed 104,823 on-premise OS instances deployed across 20 companies evaluating cloud migration. The organizations range in size from a few hundred to thousands of employees, and span multiple industries. The analysis captured hundreds of millions of data points over six months to develop a fine-grained model of their real-world OS utilization, usage and provisioning levels.

The results:

• Just 16% of OS instances were sized appropriately for their workloads. 84% could run on a smaller footprint.

• Directly porting those OS instances to the same sized resources in the public cloud would yield an annual cost of $172,827,367—$30 million more than current on-premise costs of $145,578,171.

• By rightsizing those instances—porting them to optimally sized public cloud resources, based on historical analysis of real-world utilization—they could run in the cloud for just $90,092,019. That’s a savings of more than $55 million annually—a 36% cost reduction.

Key Findings

Source: TSO Logic 2017 - Economics of Cloud Migration Report

Instance Count: 104,823; GB Storage: 20,663,049; Annual On-Premise Cost: $141,578,171; Direct Match to Cloud:$172,827,367; Rightsized Match to Cloud: $90,092,019; Savings: 36%

Direct Match Vs. Rightsized Cloud Match On-Premise Workloads Are Over Provisioned

OVERPROVISIONED

ACCURATELY PROVISIONED

$0

$50,000,000

ANNUALON-PREM

COST

DIRECT MATCH TO

CLOUD

RIGHT SIZED TO

CLOUD

$100,000,000

$150,000,000

$200,000,000

84%

16%

SAVINGS36%


Inside Cloud Cost CalculationsTo understand where those savings come from, the following section discusses a representative sample.

Table 1 illustrates an anonymized example from TSO Logic’s data. The sample shows a Customer

Payments application running in the Dev-Global environment. There are three OS instances that make up this application and the current annual cost of operating those servers is $4,103.86.

Table 1: OS Instances with Current Provisioning

Table 1a: OS Instances Detailed

Table 1a shows the details for each of the three instances – Instance A, B, C. Each row details the amount of time the instance is in use, the average and peak CPU and memory, the chipset and core count provisioned, the operating system and the annual cost. This data represents a statistical model created from millions of ingested data points. As an example, Instance A uses a dual core Intel E5 2630V2 processor and is in use 100% of the time. The server’s peak usage is 39.55% and the peak memory is pinned, using all of the 6,144 provisioned Megabytes.

Each on-premise instance ties back to a specific server platform (in this case, an IBM X3550M4) and storage configuration. Annual costs include power, space, cooling, hardware amortization, OS licensing and hardware maintenance. Labor is excluded. These costs are either provided by the client or set using TSO Logic’s benchmark library of compute costs. When benchmark costs are used, TSO Logic selects a machine from its benchmark library with a like configuration and a known cost.

In this sample, the cost to operate the compute and storage is $4,633 per year and the cost of operating the compute only is $4,103 per year.




Now that provisioning is known, let’s explore the costs of operating instances in the public or private cloud. First, we calculate the cost to provision those instances in the cloud using a direct match methodology – provisioning in the cloud with exactly the same configuration that is on-premise regardless of usage and utilization.

Using Instance A from Table 1a as an example, we can see that the instance provisioned is a dual core Intel E52630V2 Processor with 6,244 MB of ram. If you were to go to the cloud catalog and select a matching instance without considering usage and utilization, you would choose an m4.Large with an annual cost of $1,068 per year — representing a small savings over the on-premise cost. Evaluating other OS instances, cloud may even appear to cost more. This is largely because the on-premise instance is currently over provisioned in terms of usage and utilization, and the added cost of that overprovisioning extends to the cloud.

As discussed, however, the direct match methodology may not provide an accurate picture of the true cost savings that can be achieved in the cloud, because it assumes that current on-premise instances are sized appropriately. As the data show, the three instances in this sample are currently over-provisioned based on historical usage and utilization. Here are two examples:

• For Instance A, the OS is in use 100% of the time, but when its used at peak its only consuming 32% of the processor, with the average at 13.7%. Looking closer at the processor type, we can see that it is from 2013. Considering the improvements in modern processors and historical workload levels, the optimal instance size is not an m4 Large, but a T2.Xlarge, which costs $970.33 per year.

• Instance C is currently used 17.3% of the time on-premise and has 4,096 MB of Ram. When it is in use the CPU average is 5.6% and peaks out at 16%. This processor is also from 2013. The optimal match for this workload pattern is a T2.Medium, costing just $266.00 per year.

Table 2: Direct Match vs. Rightsized

As shown in Table 2, by right-sizing each OS instance — the total cost is now $2,588 per year.

That’s a 44% cost savings compared to current on-premise costs.



Tables 2a and 2b show the direct match versus rightsized instance selection for cloud.

Extending the same fine-grained analysis to each of the nearly 105,000 OS instances in the TSO Logic sample will reveal some that are provisioned appropriately, where the economic case for cloud is not as strong. But the analysis reveals many

more—84%—where the organization is paying for substantially more resources than those workloads actually require. Additionally, these results were not associated only with a specific organizational profile—the degree of overprovisioning was largely the same across organizations of all sizes, in all industries.

Table 2a: Direct Match to Cloud

Table 2b: Rightsized to Cloud

Table 3 further illustrates this point by illustrating how the amount of time a server is in use and how it is used affects the economics of

cloud. All servers in this specific sample set are less than eight years old.

Table 3: Average Cost Savings by Time in Use

Avg. Time In Use

Avg. Peak CPU

Avg. Number of

Cores

Avg. On-prem

Cost/Instance

Peak Memory

Used

Avg. Memory Provisioned

(MB)

Avg. Cost/Instance in

Cloud

Savings

0%-10% 4% 3.6523 $ 1,265 81% 9,409 $721 43%

40% -50% 27% 4.7359 $ 1,567 89% 14,491 $1,113 29%

90%-100% 34% 6.3667 $ 1,898 88% 17,238 $1,486 22%





Age of Hardware Dictates EconomicsBeyond over provisioning, the data reveals that another key factor influencing the economics of cloud is the age and generation of current on-premise hardware. The older the hardware in the current environment, the more economical cloud becomes.

Looking at servers by age, we find that by migrating servers from 2009 to more efficient hardware in the cloud would reduce costs by nearly 70%.

0%

10%

20%

30%

40%

50%

60%

70%

80%

0

5

10

15

20

25

2009 2012 2013 2014

HARDWARE SAVINGS ANALYSIS

Average PercentofTime InUse SavingstoCloud



Key Factors in Calculating Compute Costs When organizations can capture an accurate baseline for their current resources—when they can understand and profile the historical utilization of every OS instance deployed, the computational capabilities across multiple generations of processors, changing application patterns and continuous additions to cloud catalogs—the business case for cloud becomes much clearer. Capturing accurate data, however, is not a trivial project. A variety of factors must be considered, including:

• Compute capabilities across on-premise environments and cloud: The age, generation and capabilities of each processor have a direct impact on its annual operating cost. A later-generation single-core Intel processor, for example, may deliver better performance than an older dual-core processor on premise. Considering that large public cloud providers use the latest-generation hardware, and receive bulk pricing for that hardware at massive scales, cloud providers are inherently positioned to deliver superior price/performance than most organizations can achieve on their own. To realize those savings, however, organizations must be able to map current workloads to future cloud offerings.

• Real-world historical utilization: Some workloads may be in use 100% of the time, some may be in use just 10% of the time. Some may reflect 5% utilization for 29 days per month, but 100% peak utilization at month’s end. Organizations need to understand how they are truly utilizing each workload to accurately evaluate cloud options. The more data collected over time, the more accurate those evaluations become.

• Where and how instances are overprovisioned: When deploying servers on-premise, organizations typically choose from just two or three options for each OS (e.g., small, medium and large), and often default to a larger footprint just to be safe. Cloud providers like AWS, however, may offer 60 different compute and memory configurations—large, small and everything in between, with a range of storage options as well. It’s not enough to simply recognize that some instances are overprovisioned—organizations need hard, historical data and ongoing algorithmic analysis to identify the optimal placement and size for each workload from thousands of possible options.


Hard Data Drives Smarter Migration PlanningWhen attempting to develop a business case for cloud, many organizations still rely on spreadsheets or inventory systems. These can provide a high-level picture of what organizations currently have deployed, but they offer no insight into whether current workload provisioning levels are appropriate. Capturing an accurate baseline of the true costs of an environment requires a much finer-grained analysis—especially considering that any given hardware may run multiple virtual OS instances, each with its own unique utilization profile. Comparing price points for a single workload is fairly straightforward. Accurately analyzing hundreds, thousands or tens of thousands of instances in an environment is not.

To conduct this analysis, the TSO Logic platform algorithmically analyzed 104,823 on-premise OS instances across 20 companies evaluating cloud. The platform created a fine-grained statistical model of each organization’s compute resources to determine the most cost-effective place to run each workload. Ingesting millions of data points from the current environments—including age, generation and configuration of all hardware, the OSs they’re running, and each instance’s utilization—it algorithmically profiled compute patterns. It then used machine learning influenced algorithms and pattern matching to determine the best fit for each workload from thousands of potential cloud options. Using up-to-date, validated information from Intel and the major cloud providers, the platform normalized and compared processing capabilities between various generations of processors and running on-premise and in the cloud.

For more details on the TSO Logic platform and methodology:

W: tsologic.com

E: [email protected]

T: 1.866.379.9688

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Overprovisioned Workloads Can Add 30% to the Cost of Cloud ... · IDC has projected that cloud computing spending will grow at 6 times the rate of IT spending from 2015 to 2020 2