This is a continuation of a series of articles on crowdsourcing application and including the most recent article. The original problem statement is included again for context.     

 

Social engineering applications provide a wealth of information to the end-user, but the questions and answers received on it are always limited to just that – social circle. Advice solicited for personal circumstances is never appropriate for forums which can remain in public view. It is also difficult to find the right forums or audience where the responses can be obtained in a short time. When we want more opinions in a discrete manner without the knowledge of those who surround us, the options become fewer and fewer. In addition, crowd-sourcing the opinions for a personal topic is not easily available via applications. This document tries to envision an application to meet this requirement.     

 

The previous article continued the elaboration on the usage of the public cloud services for provisioning queue, document store and compute. It talked a bit about the messaging platform required to support this social-engineering application. The problems encountered with social engineering are well-defined and have precedence in various commercial applications. They are primarily about the feed for each user and the propagation of solicitations to the crowd. The previous article described selective fan out. When the clients wake up, they can request their state to be refreshed. This perfects the write update because the data does not need to be sent out. If the queue sends messages back to the clients, it is a fan-out process. The devices can choose to check-in at selective times and the server can be selective about which clients to update. Both methods work well in certain situations. The fan-out happens in both writing as well as loading. It can be made selective as well. The fan-out can be limited during both pull and push. Disabling the writes to all devices can significantly reduce the cost. Other devices can load these updates only when reading. It is also helpful to keep track of which clients are active over a period so that only those clients get preference.  In this section, we talk about the busy frontend antipattern. 

 

This antipattern occurs when there are many background threads that can starve foreground tasks of their resources which decreases response times to unacceptable levels. There is a lot of advantages to running background jobs which avoids the interactivity for processing and can be scheduled asynchronously. But the overuse of this feature can hurt performance due to the tasks consuming resources that foreground workers need for interactivity with the user, leading to a spinning wait and frustrations for the user. It appears notably when the foreground is monolithic compressing the business tier with the crowdsourcing application frontend. Runtime costs might shoot up if this tier is metered. A crowdsourcing application tier may have finite capacity to scale up. Compute resources are better suitable for scale out rather than scale up and one of the primary advantages of a clean separation of layers and components is that they can be hosted even independently. Container orchestration frameworks facilitate this very well. The Frontend can be as lightweight as possible and built on model-view-controller or other such paradigms so that they are not only fast but also hosted on separate containers that can scale out.

 

This antipattern can be fixed in one of several ways. First the processing can be moved out of the application tier into an Azure Function or some background api layer. If the application frontend is confined to data input and output display operations using only the capabilities that the frontend is optimized for, then it will not manifest this antipattern. APIs and Queries can articulate the business layer interactions. The application then uses the .NET framework APIs to run standard query operators on the data for display purposes. 

 

UI interface is designed for purposes specific to the application. The introduction of long running queries and stored procedures often goes against the benefits of a responsive application. If the processing is already under the control of the application techniques, then they should not be moved.  

Avoiding unnecessary data transfer solves both this antipattern as well as chatty I/O antipattern. When the processing is moved to the business tier, it provides the opportunity to scale out rather than require the frontend to scale up. 

 

Detection of this antipattern is easier with the monitoring tools and the built-in supportability features of the application layer. If the front-end activity reveals significant processing and very low data emission, it is likely that this antipattern is manifesting. 

 

Examine the work performed by the Frontend in terms of latency and page load times which can be narrowed down by callers and scenarios, may reveal just the view models that are likely to be causing this antipattern 

 

Finally, periodic assessments must be performed on the application tier.