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Future Internet 2021, 13, 61 5 of 13 choice was Zeppelin [29], which is another web-based platform for analytics and an Apache project that runs on top of the Spark [30] analytics system. Jupyter notebooks are very widely used, stable, and have a larger community and eco-system around it. Kaggle, CoCalc, and Nextjournal offer some varying levels of support for Jupyter notebooks. While the community for the Zeppelin is growing, it is not yet as popular. Another factor in analytics platform selection was requirement #4. Enabling teams to be able to purchase more resources for their project made it necessary to isolate notebooks into units that could use more resources. This was difficult to do quickly (requirement #7) in highly integrated multiuser implementations of Jupyter notebooks such as JupyterHub [31]. Zeppelin notebooks also are tightly integrated with Spark. Jupyter notebooks can be implemented on a standalone basis [28] (not part of JupyterHub). That property, along with the popularity of Jupyter notebooks, made the standalone Jupyter notebooks our platform of choice. 2.5.2. Team Isolation Teams competing for large monetary prizes need to be effectively isolated from each other (requirement #3). The two most viable placement choices for each team’s notebook are within VMs or within a container. Note that putting each team on an individual server would provide maximum isolation but would meet neither requirement #8 (prohibitively expensive) nor requirement #1 (not scalable). Using VMs would provide better isolation than using containers but would be more resource intensive and therefore more expensive. In the end, we deemed that containers have sufficient isolation at a better price point. To implement requirement #4, each container would be in its own resource group. That allows teams to add resources to the resource group by working directly with the Cloud Service Provider. 2.5.3. Naming Design and Infrastructure Our choice to use standalone Jupyter notebooks instantiated in individual containers, rather than an integrated environment such as JupyterHub, forced us to find an effective way to direct teams to their individual notebooks. If we had used JupyterHub, we could give all the teams a single domain name to connect to, and then they would be directed to their notebook after they authenticated. Instead, we had to direct users on an individual team to the individual container for that team. We chose to do this through DNS host names in the URL for each notebook. Figure 2 shows our design of the infrastructure and process for connecting teams to their notebooks. We present each team that joins a competition with a unique DNS name for their notebook in the xprize.org domain. In the example below, “team1” gets a URL such as “http://team1.comp1.xprize.org/” as step 1. Rather than create a new DNS [32] record for each new team that was added or removed, we used a wild-card domain record for *.comp1.xprize.org that points all teams in a competition to an application load balancer of our Cloud Service Provider. Figure 2. Routing of web requests to team notebook container.PDF Image | Future Internet
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