AWS Cost Saving Tip 5: How Amazon Auto Scaling can save costs

One of the biggest technical challenges of running an online business is how well they are able to handle the scalability requirements.  The Load traffic pattern keeps varying for online businesses and accordingly they will have to scale and maintain the acceptable performance levels.  Since the Traffic patterns are fluctuating in online business, they either tend to under provision and loose customers (or) over provision and waste hardware + costs. This problem is well illustrated in the below diagrams.

Business usually makes detailed capacity planning and large upfront investment in their hardware and software. This HW/SW’s are usually provisioned with fixed capacity.

Many times because of variations in capacity planning and usage predictions, you can observe that HW/SW capacity is underutilized. This is because in reality the actual demand may not be uniform with your predictions. The systems are utilized well during peak period but lie idle most of the times when the peak period is over.  This leakage is illustrated in the below diagram in Grey shades.

On the other hand, sometimes online businesses totally get their predictions wrong and under provision their HW/SW. This is a usual occurrence during holiday seasons and campaigns where one cannot predict the user traffic accurately. Though the business provision 4X more capacity during these period as buffer, still there are lots of chances that they get traffic more than expected. In case the traffic exceeds, users can experience degraded performance or altogether cannot access the site itself sometimes. Companies can lose customers and potential business opportunities because of this mismatch. This scenario is illustrated as grey shade in the below diagram

If businesses can closely align their load requirements and capacities then above scenarios can be countered efficiently. To solve this, we need an elastic and auto scalable infrastructure, which can be automated to expand and collapse depending upon the load traffic. Let us explore in AWS Cloud computing context how this problem can be solved.

Auto scaling service was introduced by Amazon Web services especially to balance this problem by increasing/decreasing capacities automatically depending upon the traffic.

Let us explore few Load Volatility patterns and how Amazon Auto Scaling can be used to reduce leakages and save cost.

Scenario 1: Daily Spikes-Valley

Daily Spikes and Valley pattern can be best illustrated by the below diagram.

This pattern is usually observed by ecommerce companies which have peak usage for 12 hours between 8:00 am to 8:00 pm in a day and rest of the day the capacities are usually under-utilized. Imagine you are running 20 X m1.large for you web/app tier and they are fully utilized during peak hours.  During the non-peak period the load decreases gradually and ~25 % utilization overall is observed in the nights.  Since only 25% utilization is observed in nights if you can reduce your capacity to 5 EC2 web/app instances in an automated way it will save infra + labor costs.  Let us calculate the savings one can achieve by introducing Amazon Auto Scaling into this scenario:

Formula:  Number of EC2 X current cost of m1.large (0.24 USD) X Hours

Without Cost Optimization
20 X m1.Large X 744	~3572 USD per month

Cost Optimization with Amazon Auto Scaling
20 X m1.large X 372 (hrs)	~1785 USD per month
15 X m1.large X 124	~446
10 X m1.Large X 124	~298
5 X m1.large X 124	~149
	~2678 USD per month

Savings: Using Amazon Auto Scaling you can save ~25 % leakage a month in this scenario

Image source: AWS

Scenario 2: Weekly Fluctuation

Weekly Spikes and Valley pattern can be best illustrated by the below diagram.

This pattern is usually observed by online ticketing companies which have peak usage for 4 days week and normal -> under-utilization other days of the week. Since the load is fluctuating in a week, these companies cannot afford to lease capacity weekly. They usually plan their capacity based on the peak load traffic and provision their infrastructure. Since the entire infrastructure is running all days in a week, they will be wasting resources during the normal days.

These dynamic workloads are very good use case for Amazon Web Services and Amazon Auto Scaling. Let us explore this scenario in detail below. Imagine the ticketing company needs 20 m1.large ec2 instances to handle their peak periods. Instead of running 20 X m1.large EC2 instances satisfying your peak capacities all the time, you can run 5 EC2 instances for the normal days and Schedule an auto scale out to 20 EC2 instances before the peak days of the week. During the normal or peak days of the week, if the load traffic exceeds your capacity planning you can still automatically add more EC2 instances into your fleet.  Since 3 of the 7 days your instances are under-utilized (+) 8-12 hours under-utilization (late nights) during the peak days as well, using amazon auto scaling and dynamically expanding/collapsing the web-app tier will save lots of cost to the company. Let us calculate the savings one can achieve by introducing Amazon Auto Scaling into this scenario:

Without Cost Optimization
20 X m1.Large X 168	~806 USD per week

Cost Optimization with Amazon Auto Scaling
20 X m1.large X 48 (hrs)	~230 USD per week
15 X m1.large X 24	~87
5 X m1.large X 96	~115
	~432 USD per week

Savings: From the above calculation you can observe that using Amazon Auto Scaling ~25 % leakage can be saved in a week.

Scenario 3: Seasonal Spike scenario

Seasonal Spikes pattern can be best illustrated by the below diagram.

This pattern is usually observed by online travel/ecommerce companies which have peak holiday period for 3-5 months with heavy utilization and rest of the year with normal/ under-utilized capacities. Companies traditionally used to buy or lease capacities well before the season and spend months provisioning them before the holiday season. Whereas Amazon Cloud world, though you can launch instances on demand, still some companies have this traditional mind set and run their infrastructure with peak capacity throughout the year. Reasons: sometimes it is because of the bad application/infra architecture and at times it because of IT team’s inadequate knowledge in AWS infra. This is a complete waste of resources and will eventually lead to leakage in cost.

Imagine for serving your peak traffic during holiday season you need 20 m1.large web/app instances, instead of having them running for whole year, you can automatically scale them down post the season gradually in Amazon infrastructure.

Using Amazon Auto Scaling you can schedule gradual increase of EC2 instance capacity 2 months before the holiday season and gradually decrease capacity 1-2 months post the peak season.  During the peak season if there is unpredictable increase in traffic auto scaling can still increase the EC2 fleet dynamically to handle the dynamic load. This approach avoids both cost leakage and lost opportunity cost for business. Let us calculate the savings one can achieve by introducing Amazon Auto Scaling into this scenario:

Formula:  Number of EC2 X current cost of m1.large (0.24 USD) X Hours X months

Without Cost Optimization
20 EC2 X m1.Large X 744 hrs X 12 months	~42,854  USD per year

Cost Optimization with Amazon Auto Scaling
20 X m1.large X 744 X 4	~14,284 USD per year
10 X m1.large X 744 X 2	~3571
5 X m1.large X 124 X 7	~6249
	~24,105 USD per year

Savings: Using Amazon Auto Scaling you can save ~43 % leakage over a year in this scenario

Other Tips

Cost Saving Tip 1: Amazon SQS Long Polling and Batch requests
Cost Saving Tip 2: How right search technology choice saves cost in AWS ?
Cost Saving Tip 3: Using Amazon CloudFront Price Class to minimize costs
Cost Saving Tip 4 : Right Sizing Amazon ElastiCache Cluster
Cost Saving Tip 5: How Amazon Auto Scaling can save costs ?
Cost Saving Tip 6: Amazon Auto Scaling Termination policy and savings
Cost Saving Tip 7: Use Amazon S3 Object Expiration
Cost Saving Tip 8: Use Amazon S3 Reduced Redundancy Storage