Written by Verena Barth, Marcel Heisler, Florian Rupp, & Tim Tenckhoff
The search for a useful, simple application idea that could be realized within a semester project proved to be difficult. Our project was meant to be the base for several lectures and its development should familiarize us with new technologies and topics. Someday a team member was standing at a train stop, waiting for the delayed subway to finally arrive and came up with the idea that it would be interesting to see statistics about the average/total delay of the Deutsche Bahn!
This is how the idea was born: We wanted to develop a device-independent web application to visualize both, the average and the accumulated delay, as well as current departures of the public transportation at some stops. Our application called Bahnalyse receives its data via the VVS API. To calculate the statistics, the departure times of the railways are crawled in a regular time interval and stored in a database. To search for station names as well as to display the current delays, the VVS API is called directly.
During the lecture “Web Application Architecture” we thought about a first simple application architecture, patterns and technologies we wanted to use. This was significantly influenced by the lecture “System Engineering and Management”, in the context of which this blog post is written.
General aims, technologies
Our aims for this lecture were initially to get in touch with CI/CD, Docker and the deployment to the Cloud – topics that are currently on everyone’s lips and with which we haven’t had much to do yet. Furthermore we wanted to improve the architectural concept of our quite simple application, e.g. by splitting it up, decouple the components from each other and experiment with different types of databases.
Which cloud provider fits best?
Since deploying to the cloud was one of our initial goals, we started to look for the “best” cloud provider early. We compared the most common ones (in Europe and US) AWS (Amazon Web Services), IBM Cloud, GCP (Google Cloud Platform) and Microsoft Azure in four Categories.
The first category is Enterprise Adoption because it makes sense to learn and try out the cloud provider we will use most likely later on at work. We found a study from Right Scale about this topic where AWS is clearly the leader. 68% of the 997 participating companies from different industries were running applications on AWS in 2018. The second place made Azure with 58% but this might be misleading because it includes Office 365 customers which is not interesting for us.
Category two is documentation. We found a blog post from a fellow student from July 2018 that says “documentation is key”. He tried out AWS, GCP and IBM and again AWS won with a “Still bad documentation but best of all“. Of course this is only his point of view but at least he had tried them out before judging. So for us his opinion gives us at least a good clue.
The third category is the community. Therefore we compared the amount of questions asked on Stack Overflow of all time. If there are already a lot of questions asked the possibility grows, that means the questions we might have would have possibly been answered already. Again the winner is AWS with 65,427 questions. We did not consider the percentage of unanswered questions, because it is not differing very much between the cloud providers. We looked up those numbers in October 2018.
The last but most important category are costs. Being students just trying out some technologies we did not want to spend any money. So we only compared free tiers and student accounts we did not check out what it would cost to run our application for a longer time.
We started by trying out the AWS Student Account. Given only the Starter Account by the Stuttgart Media University (HdM) we don’t have access to any IAM-Tools. Also it is not possible to create access keys for our user. This and the federate login of the student account itself made logging in from a CI-Pipeline impossible. Thus the AWS student account became useless for us. The AWS free tier would probably provide the features we need for free up to 12 month and up to a limited amount of resources. But it requires a credit card for registration that will be charged if any of the free tier limitations are exceeded. There is no possibility to set any limits for the payments so it’s not possible to say “shut down all my instances before I would have to pay for them”.
For the GCP student accounts we are not enabled because our university would have to apply for it first. The free tier also requires a credit card. But at least it will only be charged after confirmation when the free tier is used up.
Still we did not want to provide a credit card at all. Luckily IBM Cloud provided exactly what we are looking for. A six-month free student account with no need to provide a credit card and only few limitations on the available features. To activate it you need to get a promo code and supply it in your free account to upgrade to a student account.
The table below shows an overview of the categories explained above. The last row contains additional considerations. So we were told that containers are running in actual Kubernetes Clusters on GCP whereas AWS uses VMs. Also we read that GCP provides the “best AI” services, but this does not matter for our use case.
Finally we can conclude that we would use IBM Cloud because of the free student trial. But actually we did not deploy to any cloud so far. While working on the project we found other interesting topics we concentrated on and deployed the old-fashioned way to a server we got from the HdM also for free.
|Documentation||“bad but best“||“bad”|| |
„little bit better“
|Free?||Student account: useless|
Free tier: credit card required, no limits possible
|Student account: few limitations, six month free||Student account: HdM not applied|
Free tier: credit card required confirm paid account, few limits possible
best AI Services
|Mainly Office 356 customers|