An application updates an inventory running only one process at any given time to keep the inventory consistent. This process takes 200 milliseconds…

The core of this problem is a processing bottleneck. The inventory system is designed to run only one update process at a time to ensure data consistency. This single-threaded constraint dictates the maximum throughput. Since one process takes 0.2 seconds, the absolute maximum rate of processing is 1 / 0.2 = 5 requests per second.

Horizontal scaling adds more Mule workers, which increases the capacity to handle concurrent incoming requests. However, these workers must still contend for the single, locked resource (the inventory update process). As a result, while more requests can be accepted simultaneously, they will be queued and processed sequentially by the inventory system, leaving the overall throughput unchanged at 5 requests per second.

B. The total process execution time is now 100 milliseconds (.1 seconds): Horizontal scaling adds more workers (instances); it does not make the code within a single process execute faster. That would be related to code optimization or vertical scaling.

C. The application scalability threshold is now 10 requests per second: This would only be possible if two processes could run in parallel. The problem explicitly states this is not allowed, making the inventory system a bottleneck that prevents such an increase.

D. Horizontal scaling cannot be applied to an already-running application: This is incorrect. A key feature of cloud platforms like MuleSoft's CloudHub is the ability to dynamically scale workers for a running application, often with zero downtime.

1. MuleSoft Documentation, "CloudHub Architecture": This document explains that each worker is a dedicated instance of Mule runtime. Horizontal scaling adds more workers to run in parallel and share the workload. However, this parallelism is only effective if the workload itself can be parallelized. (Reference: See the "Workers" and "Load Balancing" sections in the CloudHub documentation).

2. MuleSoft Documentation, "Performance Tuning": Performance tuning guides emphasize identifying and addressing bottlenecks. A slow or single-threaded backend system is a classic example of a bottleneck that limits overall application throughput, regardless of how many resources are allocated to the Mule application layer. The inventory system in this scenario is the bottleneck. (Reference: See concepts on "Identifying Bottlenecks" in MuleSoft performance guides).

3. MuleSoft Documentation, "Designing for Reliability": In the context of reliability and scalability, MuleSoft's architectural guidance discusses the importance of understanding the limitations of downstream systems. An application's scalability is often limited by the scalability of its dependencies. (Reference: See principles of designing for scalable and resilient integrations).