Auto-Scaling and Elasticity in Cloud Infrastructure
Bring cost-savings, high performance and operational efficiency to your cloud infrastructure.
Auto-scaling and elasticity are key to a cost effective, responsive, and efficient cloud strategy. Companies that don’t integrate these features may face a host of serious operational challenges. Those that embrace them enjoy numerous benefits.
But first, let’s look at the relationship between auto-scaling and elasticity. Understanding how they work together is the first step in optimizing your cloud infrastructure.
What are Auto-Scaling and Elasticity?
Auto-scaling refers to the automatic adjustment of compute resources (processing power, memory, networking, storage, I/O, etc.) to meet shifting workload demands. Elasticity is the system’s ability to efficiently scale those resources up or down. Together, they provide your cloud computing environment with flexibility to meet changes in demand at any time.
There are two types of auto-scaling:
Vertical scaling (scaling up and down) means increasing or decreasing the capacity of a resource. Because a system is often temporarily unavailable during vertical scaling, it’s less common to automate it.
Horizontal scaling (scaling out and in) means adding or removing a resource. This type of scaling is commonly automated, as the system will continue to run without disruption. Cloud services such as Amazon Web Services (AWS) support automatic horizontal scaling.
In our experience, it is rare to invest horizontally across an entire technology stack to implement auto-scaling. You only need to auto-scale the compute resources that become bottlenecks during the heaviest usage or demand.
What is the difference between load balancing and auto-scaling?
Load balancing ensures that compute resources in a cloud environment are evenly distributed.
When more compute resources are needed, auto-scaling will identify new resources, and load balancing will connect and distribute workload among them. This ensures that cloud applications achieve greater efficiency and reliability.
Cloud Infrastructure Before and After Auto-Scaling
Without auto-scaling and elasticity in place, you can only address issues reactively and via a manual process. And, you may have no idea how much to scale.
One client we worked with ran a monolithic system. The only way they could handle more customer load was to deploy a bigger system with more memory and CPUs; an engineer did this manually. They only knew they had to deploy a bigger system when customers started complaining about performance. Another client nearly lost their top customer because the system kept crashing. Without scalability built into their systems, both clients were wasting money and risking massive revenue losses.
Manual scaling is not the answer, as it is prone to errors and labor intensive. When will demand change? By how much will it change? And which resources will be needed? Guessing can lead to over-provisioning that costs you money or under-provisioning that affects performance and customer experience.
With auto-scaling and elasticity in place, the resources your system is consuming are adaptive based on the parameters you decide are important to ensure smooth operation. For example, one parameter may be 80% of CPU usage. When your application hits that 80% threshold, the system will scale automatically and trigger load balancing. No downtime, no disruptions, no manual effort.
Whether you have a memory-intensive, compute-intensive application or a data mining system with heavy demand for storage and I/O, you no longer have to worry about overloading the system.
What Are the Benefits of Auto-Scaling and Elasticity?
Auto-scaling alone can yield huge financial savings for your company. When demand is high, your resources will quickly scale up. When demand drops, your resources will scale down. Scalability ensures you do not pay for extra infrastructure that is not actively being used. Instead, you will only pay for the resources you need at the moment. Thus, you will strike a balance between performance and cost.
Auto-scaling and elasticity also support a positive customer experience by ensuring your services remain available and responsive, regardless of changes in demand.
Efficiency is baked into any cloud computing infrastructure that auto-scales. For companies who are expecting massive growth or surges in demand, you no longer need to assign engineers to actively monitor and manually deploy a bigger system or more compute resources (depending on your architecture). Auto-scaling and elasticity optimize the use of compute resources as needed.
With better load management, servers you don’t need are allocated to other companies, saving you money.
Finally, auto-scaling supports lower energy consumption. When traffic is low, servers can be shut down, thus using less electricity. For companies that host their own server infrastructure, this can lead to savings on utility bills.
Strategies for Effective Implementation
Autoscaling and elasticity aren’t an instant solution. Simply moving your architecture to the cloud (lift-and-shift) doesn’t provide the benefits of cloud elasticity. On the other hand, you do not need to re-architect everything from the ground up before making the move.
Stage One: Align Technology and Business Strategy
The first step in implementation is aligning your technology architecture with your business strategy. What are your specific business objectives? How can your cloud infrastructure, whether it’s hosted on AWS or another service, support those objectives?
Talk to your operations and sales teams. Let’s say sales has planned an aggressive customer acquisition strategy during an upcoming trade show, and they plan to provide one month of free access to an app. Knowing you will see a spike in usage, you can plan to auto-scale to meet this demand.
At this stage, make sure you take into account data sensitivities, service level agreements (SLAs), and compliance requirements. Scaling strategies must be compliant with industry standards and data protection laws to keep data safe.
This is a great time to review your SLAs so you will meet or exceed SLA requirements, and put a process in place to regularly assess and adjust current compute resources so you can avoid unnecessary expenses.
Stage Two: Model Your Workload
In stage two, you will need to model your workload so sudden peaks in demand don’t leave your auto-scaling response in the dust. Auto-scaling happens quickly, but not instantaneously, and is ultimately a function of your software design. By carefully mapping out workload and usage scenarios, you will ensure customers don’t experience poor performance.
Stage Three: Integrate Monitoring Systems
For stage three, turn your attention to monitoring systems, which are key for auto-scaling. You’ll need them at the application, service, and infrastructure levels.
Proactive monitoring allows you to track metrics like response times, queue lengths, CPU utilization, and memory usage. If any issues arise, you can detect and address them promptly.
Stage Four: Decide on Thresholds for Scaling
In our next stage, you’ll need to understand where your scaling points are and decide on the thresholds or schedules for scaling. For example, at what point does an app’s performance slow down? Do the work upfront to understand how your systems need to scale. Once you know this, decision-making logic can compare system metrics against those thresholds or schedules so auto-scaling happens at the right time for the right resources.
Stage Five: Determine Auto-Scaling Strategy
Good monitoring is key to easily drive event-based auto-scaling that is based on a trigger. If you do not yet know when you will hit a threshold, predictive or proactive auto-scaling uses artificial intelligence to predict when demand will spike based on historical usage patterns. You can then schedule additional servers to come online at those times. (AWS offers this feature.)
Final Stage: Integrate Auto-Scaling and Testing
Onto stage five, where you integrate the right auto-scaling tools and components. Finally and importantly, you need to implement testing, monitoring, and fine-tuning to ensure the system functions as expected and can meet new business needs.
Our Approach Includes Vertical Slicing
Our implementation process involves vertical slicing based on Martin Fowler’s “strangler fig pattern.” It’s a crazy name for a logical and cost-effective architectural pattern. Just like the strangler fig plant grows on and around trees, the strangler fig pattern wraps new code around old code.
We make vertical slices through your existing software architecture and write new code that makes part or all of it auto-scalable. First we identify where the problem areas are, prioritize those candidate areas, and create a timeline for changing and implementing the code to support auto-scaling.
Scalability in cloud computing can provide your organization with a competitive advantage, minimize the risk of a poor customer experience, and save you money.
Our experts at Ten Mile Square can help you implement auto-scaling and elasticity in a cloud infrastructure.
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