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Optimized AI and analytics in the cloud
In the cloud computing world, performance is literally money: cost control, revenue and time to value.
Establishing the most performant foundation for your organization is critical to meeting current needs and preparing for the future.
The research presented shows that SAS® Viya® is a more cost-performant and cost-conscious analytics platform available to enterprise cloud users. That translates directly into a productive workforce and powerful analytics.
The conventional computing wisdom is that performance equals time. The ideal performance state is using your resources to their peak without affecting runtime.
On the right, you’ll see an overly simplified view of the results. In Figure 1, runtime is cut in half when cores are doubled. In Figure 2, CPU is used to maximum capacity while the workload is executing. In a cloud environment, an analytics platform must operate within these extremes to be as cost-performant as possible.
Figure 1: Runtime vs. available cores
Figure 2: CPU use vs. available cores
The top reasons companies back away from the cloud
Falling far short of this ideal state is a key reason that companies abandon cloud strategies they pursued without fully understanding the costs of executing their workloads in the cloud. Industry analyst IDC states that 80% of public cloud customers have repatriated some workloads off-cloud.
The leading drivers are:
Organizations without a cloud optimization process tend to overspend by 40% due to unmanaged costs, unexpected usage, suboptimal design and implementation, and wrong-sized production and waste in development and testing environments, among other reasons. SAS can help you avoid this scenario by accurately scoping your analytics transformation before implementing your cloud migration strategy or supplementing the cloud strategy already underway.
Applying ecosystem diagnostics enables you to see which workloads consume the most resources and runtime; generate cloud environment and workload sizing; and determine business justifications for cloud and data strategies that consider interdependencies between data, locations, routines and users.
Having detailed knowledge of your consumption metrics provides two potent insights. The first is a reasonable forecast of cloud cost and performance expectations and where they can be improved. The second is creating a validated map of which workloads and data benefit from a cloud environment (and which should not migrate at all).
Cloud spending is growing year over year and becoming a more substantial portion of a company’s costs. As a result, infrastructure expenses are a line item of growing importance to senior executives. Estimates are that 80% of organizations will overshoot their budgets for infrastructure as a service (IaaS) as a direct result of poor cloud optimization governance and overspending in advance on cloud commitments that are too small to support their analytic ecosystem’s consumption.
Cloud spending is growing year over year and becoming a more substantial portion of a company’s costs. As a result, infrastructure expenses are increasingly important to senior executives.
Finding cost-performant harmony despite ever-changing requirements
Computations change as constraints are introduced. The data which feeds the math is constrained by the chip and the distribution of the math. Changes to the data and how that data is distributed to the chip can alter performance, cost and accuracy.
SAS Viya helps you move toward the perfect harmony of math, data and chip hardware in several ways.
SAS has proven to be a highly performant language. Our algorithms optimize CPU usage, runtime, memory usage and I/O speed, often eliminating the need to purchase additional infrastructure to compensate for inefficient CPU usage or high memory requirements – frequent issues when running large jobs that translate to unnecessary additional expense. In this way, the cost of compute is married to performance for the best results.
SAS Viya is inclusive; it embraces and improves open source algorithms. Many data scientists who write in Python, R or other languages can readily take advantage of the stability, performance, affordability and model governance. You can also use its ability to run any new algorithms as part of existing model tournaments. SAS actively identifies leading open source algorithms and incorporates them into SAS Viya. If an algorithm is better at parallelizing quickly, maximizing core usage or offering new advances, SAS is ready to adopt and refine its approach for maximum results.
Optimal cost performance can be reliably pursued by calling SAS Cloud Analytic Services (CAS) actions from Python. SAS assists you in choosing from all available options to help ensure the performance and repeatability of the pipeline.
SAS Viya is inclusive; it embraces and improves open source algorithms. Many data scientists who write in Python, R or other languages can readily take advantage of the stability, performance, affordability and model governance.
What does performance harmony look like?
Consider this comparison of workload executions for logistic regression (a common analytics algorithm) between SAS Viya and open source packages (OSPs) when monitoring technology was deployed to the sessions to observe their performance.
View workload execution details
Workload executions used the same data set, code, driver instances and worker nodes per test. SAS executions were automatically mapped to the available cores. Open source packages required manual changes to use all available cores. Each test was performed five times per package to identify anomalies. No anomalies were observed or removed. The aggregate of the five tests is graphed across the various worker nodes.
SAS Viya provides the expected outcome: Adding cores showed that SAS runtime was 12% to 700% faster than the OSPs across the worker nodes. OSP 1, which is closest to SAS in eight cores, didn’t keep up with SAS runtimes as cores were added. This leads to higher costs with no improvement in runtime (or sometimes longer runtime).
Figure 4 shows how well the CPU was used during the workload execution. Because SAS Viya has efficient execution times, the CPU requirements across all cores are highest for eight and 16 cores in this test. The pink line declining indicates that the workload is about to complete, and the free cores can now be reallocated for other work as needed. The OSPs remain high in CPU usage across worker nodes but are not vastly improving the runtimes, similar to driving a car in second gear without going any faster.
Figure 3: Runtimes vs. available cores
Figure 4: CPU use vs. available cores
SAS Viya provides the expected outcome: Adding cores showed that SAS runtime was 12% to 700% faster than the OSPs across the worker nodes.
The value of optimized performance
Performance is money in the cloud computing world. To save on unnecessary costs, most companies choose to turn off cloud resources when they complete the execution of their analytics workloads or during off-peak hours for nonproduction environments.
To achieve cost efficiency and maximize productivity, you must account for the time that cloud resources are used, because this translates directly to cloud spending, and the number of workloads you can execute when your cloud resources are up and running.
Figure 5: Number of runs in one hour at 100% CPU utilization
Figure 5 shows that an organization that uses all the available computing power with SAS Viya can run up to 19 times more workloads compared to OSP2 and 5.5 times more than OSP1. As a result, organizations benefit in multiple ways by either:
- Reducing the computing power needed to minimize costs while still achieving the desired business outcome.
- Or using the same computing power and:
- Running more analytics workloads in less time to achieve better results due to the iterative nature of analytical processes (tune, run, examine the results and repeat).
- Turning off the cloud resources faster to save on costs.
It’s worth mentioning that potentially improved runtimes could be achieved in all three cases by further optimizing the code (the default settings were used for SAS Viya and OSPs), but this would require significant levels of expertise, time and effort that all translate to a large increase in costs.
Our analysis shows that the result is a significant reduction in the cost of computing and an increase in productivity that can be realized by organizations when performance is optimized. You can now see why SAS Viya is a cost-performant leader for analytics.
The development community of open source is vast, but being the best depends on providing the right assurances
The cloud and distributed computing introduce new constraints that affect performance, computing costs, and the repeatability and accuracy of results. Comparing those SAS and OSP workloads is just one example of how SAS is evaluating leading open source algorithms against SAS Analytics.
By embracing open source algorithms, SAS Viya enables your analysts to pursue the best outcome every time regardless of algorithm. However, when it comes to putting analytics into production, assurances need to be provided, including the ability to generalize to different data set sizes, types and shapes while managing model drift.
A SAS algorithm is tested for these qualities:
(effective and effiecient; cost-conscious; repeatable; accurate; ethical and responsible)
SAS also continues to test open source algorithms to evaluate their ability to provide the same assurances. If an open source algorithm delivers on most of those assurances, SAS will improve it to the point where it can be adopted on the SAS Viya platform. This enables users of SAS Viya algorithms to spend less time coding, testing, interpreting and trying to achieve better harmony between the math, data and chip. This frees them to focus on delivering results.
NEW CONSIDERATION NO. 1
It’s accurate, but is it repeatable?
A growing concern about unmanaged cloud costs led to the creation of the FinOps Foundation in 2019. A cloud finance best practices hub and certification provider, it provides guidance on this new financial management discipline and guides the cultural shift precipitated by the cloud. The goal is to maximize the business value of cloud spending that, according to the FinOps Foundation, requires “engineering, finance and business teams to collaborate on data-driven spending decisions.” It’s a new world in which each role takes responsibility for its cloud usage, keeping cloud costs in mind and collectively making the best use of the variable cost advantages offered by cloud computing.
These financial considerations lead back to the need for analytics-driven enterprises to improve accuracy and repeatability. When the “perfect” harmony of math, data and chip is less than perfect, errors and inaccuracies proliferate. A single processor can easily generate a single average with accuracy. But with distributed data, the ability to produce averages is limited by the amount of data that can fit on a specific chip. Unless a central organizing principle is applied, the distributed averages will produce an incorrect macro average. It might be fast, but it is neither accurate nor repeatable.
Each time you distribute the math to the chips in a different way, it will produce a different answer. Python manages this variability by requiring appropriate configuration of the math to the chip. While this can be effective, it requires hand-coding by a user highly adept at the mapping of math, data and chip – reducing productivity, increasing cloud costs and putting the analytic results at risk.
How does SAS alleviate the user of these burdens?
SAS Viya flags that the process will produce an inaccurate answer if it is distributed, indicating that the best route would be to run a single-threaded process to execute that workload, reducing the risk of inaccuracy.
In this way, SAS Viya creates a solid foundation for the cornerstones of predictive modeling: cost performance, productivity, accuracy and repeatability. SAS Viya helps to eliminate common risks faced by users who adopt open source to take advantage of perceived low start-up costs.
Running an off-the-shelf analytics package and execution engine while paying for bulk cloud commitments routinely costs more and can produce inferior answers. Whether migrating from traditional on-premises computing or reassessing your existing cloud economics, SAS Viya can help you balance how cloud commitments are applied to variable workloads with greater flexibility and speed. We believe this is the ideal state of analytics performance in the cloud.
It’s a new world in which each role takes responsibility for its cloud usage, keeping cloud costs in mind & collectively making the best use of the variable cost advantages offered by cloud computing.
NEW CONSIDERATION NO. 2
Does cost constrain access and innovation?
Performance is more than saving time – better performance means your organization can produce more. Unsupervised machine learning results in many “blind” models that can generate good answers, but paying for each iteration of that large project is far less than ideal. This is especially true when the technology is slow: The data scientists will limit their tournament to fewer models to optimize the cost.
In examples like the one shared in Figure 4, our research demonstrated that SAS can run twice as fast as leading OSPs that use nine times as much infrastructure. This enables data scientists to run more models without driving up costs.
In short, bigger tournaments equal better answers. But the cloud cost structure requires an additional framework that has not been a consideration in prior computing ecosystems: achieving the desired goals while ensuring value within cost constraints.
Unsupervised learning and modeling time happen while the meter is running in the cloud. This offers a new dynamic for users who are now responsible for the cost of their analytic design time.
Furthermore, citizen data scientists and business analysts need to be enabled to perform higher-level analytics without being overburdened by cost constraints or writing a large amount of code. This is why no-/low-code interfaces with SAS algorithms can be incredibly powerful. They avoid the coding and overhead. They lower time to code models, offer approachability to new users and eliminate the need to ensure the data, math algorithm and chip are as close as possible to perfect harmony.
NEW CONSIDERATION NO. 2
This new dynamic highlights the importance of cost-performance and why SAS is highly attuned to this issue. In the on-premises world, constraints are addressed by buying more cores and capitalizing the expense, a long-term commitment with predictable IT spending. In the cloud world, IT spending happens at the edge, with resources scaled according to need. This creates challenges for forecasting IT spending and hampers the organization’s ability to benefit from heavily discounted long-term cloud commitments. This change in buying behavior increases the risk of overpaying for cloud computing.
The other element in this dynamic is that the cost decisions are now in the hands of the teams running the models – not with the IT or purchasing departments. In the cloud, system engineers procure the infrastructure, choose the algorithm, select the amount of data – and now carry the fiscal responsibility when they run their workloads.
As a result, organizations are trying to find FinOps unicorns to guide executives, finance, system engineers and product owners toward more fiscally responsible engineering in the cloud.
Analytics users want to spend their time effectively to find better business solutions. Instead, they’re spending time writing code, ensuring it is tailored to be cost-performant, or finding themselves limited to building and testing fewer algorithms and models to stay within cost constraints. Some users may even be excluded entirely from pursuing analytics due to these new considerations.
Figure 6: A FinOps unicorn coordinates cloud finance between stakeholders
Performance is more than saving time – better performance means your organization can produce more.
A cost-performant approach
Achieving cost performance in the cloud provides organizations with the freedom to choose how they optimize their analytics strategy to drive down costs and maximize productivity. SAS suggests an approach of continuous improvement:
Be flexible and innovative by empowering users to manage their cloud consumption.
The cultural empowerment of cloud financial management is essential to prevent excessive cloud costs and unnecessary workload repatriation. SAS offers a solution for ecosystem diagnostics to identify and improve the cost performance of resource-hungry code.
Consistently assess performance to control cloud costs.
Our research showed that two similar approaches can have vastly different outcomes in the cloud. Being able to effortlessly achieve better performance at scale allows cloud costs to be brought into line with expectations. Tight harmony between math, data and chip allows algorithms to run faster and reduces cost considerations by several orders of magnitude.
The shift to cloud has increased the potential to mine untapped value from data, but also the risk of driving costs into uncharted territory. Changing the behavior of data scientists, analysts and engineers and the code they run is a new challenge and an evolving process. SAS Viya provides the potential to achieve 19 times more value out of the same cost, which equates to a continued, compounding return on investment. It is now in your hands to develop an effective strategy that focuses on maximizing productivity and driving down cloud costs by capitalizing on the power of SAS Viya.
SAS Viya provides the potential to achieve 19 times more value out of the same cost, which equates to a continued, compounding return on investment.
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