How do you scale cloud resources and costs in DevOps?

Scaling goals are totally depends on nature of application and type of Industry.Some of the key metrics for scaling goals are 1. Scaling threshold: largely contributes to performance and availability of resources. 2. Scaling efficiency: How quickly the scaling would response to the dynamic workload. This is also contributes largely to performance and reliability of application. 3. Scaling rate: The lower and higher limit for scaling which can be the key factor for billing. The scaling goals also depends on optimised code which uses optimal compute resources to perform to process the request. The data centric capacity planning with the help of load testing would help the organisation to set the proper base line.

Scaling your cloud resources is important for building both high availability and resilience into your systems. As much as you get the business goals, set your maximum budget and all that, you have to set your baselines and thresholds. How many instances of a service should be running at a minimum? How many healthy instances? What is the maximum amount of compute or storage I can realistically allocate to this service? If you’re constantly hitting the limits you set, or your bill is getting larger, congratulations, it seems your product or service is taking off. But do not forget to review and optimise your application. Scaling is amazing, but it can be dangerous and expensive to scale an inefficient application.

One should always start their assessment with business goals. What are we trying to achieve ? Recommendations may vary wildly depending on whether we're focusing on cost optimization, innovation, or simply conducting a landscape assessment. Another source of guiding information is extracting pain points from business KPIs and using them to guide our assessment. For instance, experiencing scaling problems and being unable to handle peak loads is certainly detrimental to business ! Using this data, we should be able to define clear targets that will drive our scaling changes. Example of such targets can be : - Reduce response time by 50% - Improve service avaiability to 99.999% - Keep the cost of cloud resources below $10k/month

Aligning scaling goals and metrics with business objectives and customer expectations is a delicate balance. It's a dance in a sense between what is ideal and what is feasible, especially when budget and resource constraints enter the equation. It's really about understanding the unique needs of your software in terms of performance, availability, reliability, and security. These are not just technical terms; they are the pillars on which user trust and system integrity stand upon and what's needed. Efficency without breaking budget.

Many organizations overlook the fundamental finops practice of tagging, labeling, and annotating resources on the cloud, hindering the effectiveness of cloud-native cost management tools. These identifiers are crucial for insightful breakdowns based on business units. Establishing a robust framework for tagging becomes imperative for unlocking the full potential of cloud-native tools. Once in place, organizations can harness various tools such as cost alerts, budget alerts, and cost management tools like Cost Explorer for comprehensive insights into overall cloud spend and usage patterns. Incorporating these cloud-native tools into a well-established tagging framework enhances observability and foster efficient resource allocation & spend.

This process should be again continous optimization. Get the dashboards built on top of the cloud resources and get the monthly/weekly costs involved for different environments and different resources. Analyse and take decisions on the part where improvisions can be done and cost can be optimzed. Cloudability is one the best tools available for the same and even tools which is part of cloud services can also be used. Review of those dashboards with teams and getting the feedback play a major role. Even before creating resources in cloud, an analysis should be done on cost perspective.

Modern IT usually spans many platforms, networks, and even Cloud Providers ! Therefore, one should not underestimate the complexity of monitoring scattered and different types of resources. It is nearly impossible to have a one-size-fits-solution, but it is possible to combine different specialized tools to extend the monitoring coverage on as much levels as possible. For instance, we can start monitoring cost before resources are even created using tools such as Infracost. Moving on to Cloud Providers, they all provide robust native cost monitoring tools, which can be completed with cross-platform monitoring tools. As for on-premises monitoring, one should not forget human effort, which may end up accounting for most of the cost !

Understanding how Cloud and DevOps resources are being utilized is the key to success. There are wide variety of monitoring tool would help to get the resource utilisation at the component level which can be used for further analysis. Majority of the cloud providers such as AWS, Azure and GCP provides tools to monitor and forecast cloud cost usage at the resource level. We can set the budget at the service level or account level to define the boundary and take an action whenever needed. The reports provided by Cloud service provider can help us narrow down the source of cost contributor and can be used for further analysis as part of Cloud cost optimization. Historical data is key to success.

Sukesh is spot on! Historical data is key to success. Historical data is not only relevant to identify areas for optimisation, it is also important in making plans for the future.

Adopt preventive policies to block resource requests that could lead to unexpected costs Existence of preventive policies can automatically deny creation of high-cost resource combinations that are not expected to be used in specific environments, or certain stages of the software development life-cycle. For example, blocking creation of high-cost GPU instances, denying object storage creation that do not have data retention configurations (X days in dev, Y days in staging), and so on.

Automation is key to success. It can be in the form of IaC for creating infrastructure on demand or ASGs to adjust the capacity according to the dynamic nature of workload. We can make use of different scaling strategies as mentioned below. 1. scheduled scaling: For surge in traffic during specific time in a day/week/month. 2. Predictive scaling: forecasting the load based on ASG history. 3. Dynamic scaling: Setting a threshold for scaling. The managed service such as load balancer in cloud help us to manage unexpected incoming traffic at scale. Selection of these service and strategies varies from case to case.

Make use of heavy discounts offered on unused compute capacity Cloud providers offer heavy discounts on certain instance types which are lying un-used in their data centres. These resources are good candidates for usage in less-critical CI/CD pipelines, for example, as the needs of such workflows are anyways transient in nature. Usage of such resources needs to be combined with effective monitoring and alerting that notifies about the scenarios where the provider claims back the resources.

Optimization can only happen with great experience of underlying application and Infrastructure along with business needs. Optimization is tricky in terms of performance and cost. For an example as part of right sizing recommendation we may have to lower the existing instance type which definitely saves the cost and may have diverse impact on performance. We can use right sizing recommendation to lower the cost along with resource utilization report by identifying unused resources/services. On the other hand we can use better instance type such as graviton, gp3, caching solution to improve the performance and lower the cost. Performance testing plays a vital role to understand the bottleneck and helps to optimise further.

When utilizing AWS for stateless workloads on EC2s, consider leveraging spot instances. Bidding for a higher-tier, uncommon machine type is advantageous as these instances are less frequently bid on, providing potential long-term cost savings, without the risk of having your ec2s de-provisioned.

As part of continuous improvisation one need to keep an eye on changing demand.Improvisation would not be possible without experimentation. We can improve scaling strategy over the period of time with the help of historical data and experience. It can be tuning the threshold, or modifying the ASG group count, or the rate at which it scales.

In DevOps, we fine-tune cloud scaling for efficiency. Automated scaling, careful metric monitoring, cost optimization, right-sizing, and smart resource allocation are key. Add in load balancing, container tricks, and ongoing collaboration between devs and ops for that perfect balance of performance and cost-effectiveness. Always optimizing, always improving—That's the DevOps mantra.

Leverage frameworks and scripts that are environment aware Tag-based identification procedures can be leveraged to identify, and prevent cost wastage coming from over-provisioned resources. These implementations can take actions such as shutting down resources in development environments after working hours, consistent period of non-usage, or over the weekends. It’s important to note, that such implementations are highly dependent on the existence, and correctness, of the metadata attached to respective resources. IaC validations and policies are key to ensuring that right information is in place before the resources are even created.

Understanding and gauging the costs of implementing, migrating, and maintaining DevOps solutions can be challenging. For instance, you might face a dilema such as deciding whether to invest time and resources in cost-effective measures or to wait and focus on revenue-generating activities. For example which one will you choose to focus on. A project that it's projected to save 1k a month on a already profitable area of the business or a new product feature that could generate 100k in sales that quarter.