The AdWantageS

Every customer has a reason to move in to the cloud. Be it cost, scalability, on demand provisioning, there are plenty of reasons why one moves into the Cloud. The latest whitepaper "The Total Cost of (Non) Ownership of Web Applications in the Cloud" by Jinesh Varia, Technical evangelist of Amazon Web Services provides a good insight between hosting an infrastructure in-house and in the Cloud (AWS). There are plenty of pricing models available with AWS currently which can provide cost benefits ranging from 30% to 80% when compared to hosting the servers in-house.

On-Demand Instances - this is where every one starts with. You simply add your credit card to your AWS account and start spinning up Instances. You provision them on demand and pay for how long you run them. You of course have the option of stopping and starting them whenever needed. You are charged for every hour of running an Instance. For example, a Large Instance (2 CPU, 7.5GB Memory, Linux) you will pay $0.32/hr (US-East).

Reserved Instances - let's say after you migrate/host your web application to AWS and run multiple web servers and DB servers in AWS. After couple of months, you may notice that some of your servers will run 24 hours/day. You may spin up additional web servers during peak load but will at least run 2 of them always; plus a DB server. For such cases, where you know that you will always run the Instances, AWS provides an option for reducing your cost - Reserved Instances. This is purely a pricing model, where you purchase the required Instance type (say Large/X-Large) in a region for a minimum amount. You will then be charged substantially less for your on-demand charge of that Instance. This way there is a potential savings of 30% over a period of one year when compared to on-demand Instances. The following provides an illustration of the cost savings for purchasing an m1.large Reserved Instance against using it on demand through an year

Cost comparison between On-Demand and Reserved Instance for m1.large Linux Instance in US-East

Be careful that,

• Reserved Instances are purchased against an Instance type. If you purchase an m1.large Reserved Instance, at the end of the month when your bill is generated, only m1.large usage will be billed at the reduced Instance hourly charge. Hence, on that given month if you figure out m1.large is not sufficient and move up to m1.xlarge, you will not be billed at the reduced hourly charge. In such a case, you may end up paying more to AWS on an yearly basis. So, examine your usage pattern, fix your Instance type and purchase a Reserved Instance.
• Reserved Instances are for a region - if you buy one in the US-East region and later decide to move your Instances to US-West, the cost reduction will not be applicable for Instances running out of US-West region

Of course, you have the benefits of,

• Reduced cost - a minimum of 30-40% cost savings which increases if you purchase a 3-year term
• Guaranteed Capacity - AWS will always provide you the number of Reserved Instances you have purchased (you will not get an error saying "Insufficient Capacity")

Spot Instances - in this model, you will bid against the market price of an Instance and if your's is the highest bid then you will get an Instance at your bid price. The Spot Market price is available through an API which can be queried regularly. You can write code that will check for the Spot Market price and keep placing bids mentioning the maximum price that you are willing to pay against the current Spot Market price. If your bid exceeds then you will get an Instance provisioned. The spot price will be substantially low than on demand pricing. For example, at the time of writing this article, the spot instance pricing for a m1.large Linux Instance in US-East was $0.026/hr as against $0.32/hr on demand pricing. This provides about 90% cost reduction on an hourly basis. But the catch is,

• The Spot Market price will keep changing as other users place their bids and AWS has additional excess capacity
• If your maximum bid price falls below the Spot Market price, then AWS may terminate your Instance. Abruptly
• Hence you may loose your data or your code may terminate unfinished.
• Jobs that you anticipate to be completed in one hour may take few more hours to complete

Hence Spot Instances are not suitable for all kind of work loads. Certain work loads like log processing, encoding can exploit Spot Instances but requires writing careful algorithms and deeper understanding. Here are some of the use cases for using Spot Instances.

Now, with that basic understanding, let's examine the whitepaper little carefully not just from cost point of view. Web applications can be classified in to three types based on traffic nature - Steady Traffic, Periodic Burst and Always Unpredictable. Here is a summary of the comparison of benefits of hosting them in AWS

AWS benefits for different type of web applications

Steady Traffic
The website has steady traffic. You are running couple of servers on-premise and consider moving it to AWS. Or you are hosting a new web application on AWS. Here's the cost comparison from the whitepaper

Source: AWS Whitepaper quoted above

• You will most likely start with spinning up On-Demand Instances. You will be running couple of them for web servers and couple of them for your database (for HA)
• Over long run (3 years) if you only use On-Demand Instances, you may end up paying more than hosting it on-premise. Do NOT just run On-Demand Instances if you your usage is steady
• If you are on AWS for about couple of months and are OK with the performance from your setup, you should definitely consider purchasing Reserved Instances. You will end up with a minimum of 40% savings against on-premise infrastructure and about 30% against running On-Demand Instances
• You will still benefit from spinning up infrastructure on demand. Unlike on-premise, where you need to plan and purchase ahead, here you have the option of provisioning on demand; just in time
• And in case, you grow and your traffic increases, you have the option to add more capacity to your fleet and remove it. You can change server sizes on demand. And pay only for what you use. This will go a long way in terms of business continuity, user experience and more sales
• You can always mix and match Reserved and On-Demand Instances and reduce your cost whenever required. Reserved Instances can be purchased anytime

Periodic Burst

In this scenario, you have some constant traffic to your website but periodically there are some spikes in the traffic. For example, every quarter there can be some sales promotion. Or during thanks giving or Christmas you will have more traffic to the website. During other months, the traffic to the website will be low. Here's the cost comparison for such a scenario

Source: AWS Whitepaper quoted above

• You will spin up On-Demand Instances to start with. You will run couple of them for web servers and couple of them for the database
• During the burst period, you will need additional capacity to meet the burst in traffic. You need to spin up additional Instances for your web tier and application tier to meet the demand
• Here is where you will enjoy the benefits of on demand provisioning. This is something that is not possible in on-premise hosting. In on-premise hosting, you will purchase the excess capacity required well ahead and keep running them even though the traffic is less. With on demand provisioning, you will only provision them during the burst period. Once the promotion is over, you can terminate those extra capacity
• For the capacity that you will always run as the baseline, you can purchase Reserved Instances and reduce the cost up to 75%
• Even if you do not purchase Reserved Instances, you can run On-Demand Instances and save around 40% against on-premise infrastructure. Because, for the periodic burst requirement, you can purchase only during the burst period and turn off later. This is not possible in an on-premise setup where you will anyways purchase this ahead of time

Always Unpredictable

In this case, you have an application where you cannot predict the traffic all the time. For example, a social application that is in experimental stage and you expect it to go viral. If it goes viral and gains popularity you will need to expand the infrastructure quickly. If it doesn't, then you do not want to risk a heavy cap-ex. Here's the cost comparison for such a scenario

Source: AWS Whitepaper quoted above

• You will spin up On-Demand Instances and scale them according to the traffic
• You will use automation tools such as AutoScaling to scale the infrastructure on demand. You can align your infrastructure setup according to the traffic
• Over a 3 year period, there will be some initial steady growth of the application. As the application goes viral you will need to add capacity. And beyond its lifetime of say, 18 months to 2 years the traffic may start to fall
• Through monitoring tools such as CloudWatch you can constantly tweak the infrastructure and arrive at a baseline infrastructure. You will figure out that during the initial growth and "viral" period you will need certain baseline servers. You can go ahead and purchase Reserved Instances for them and mix them with On-Demand Instances when you scale them. You will enjoy a cost saving benefit of around 70% against on-premise setup
• It is not advisable to plan for full capacity and run at full capacity. Or purchase full Reserved Instances for the full capacity. If the application doesn't go well as anticipated, you may end up paying more to AWS than the actual

As you can see, whether you have a steady state application or trying out a new idea, AWS proves advantageous from different perspectives for different requirements. Cost, on-demand provisioning, scalability, flexibility, automation tools are things that even a startup can think off and get on board quickly. One question that you need to ask yourself is "Why am I moving in to AWS?". Ask this question during the early stages and spend considerable time in design and architecture for the infrastructure setup. Otherwise, you may end up asking yourself "Why did I come into AWS?".