The Most Appropriate Resources for In-Memory ERP Database on Google Cloud

C.

For an Enterprise Resource Planning (ERP) system that holds the full database in-memory, the most appropriate resources on Google Cloud would be Compute Engine instances with local SSDs attached (Option C).

Option A: Provisioning preemptible Compute Engine instances might not be the best solution as they are suitable for workloads that can be interrupted, such as batch processing jobs or data processing pipelines. However, running an ERP system requires a stable infrastructure that runs continuously without interruptions.

Option B: Provisioning Compute Engine instances with GPUs attached would be useful for applications that require high-performance computing, such as video rendering, machine learning, or scientific simulations. However, an ERP system doesn't require that much computing power. Therefore, GPUs are unnecessary for an ERP system.

Option C: Compute Engine instances with local SSDs attached are ideal for storing and accessing data quickly. Since the ERP system holds the entire database in-memory, local SSDs provide high I/O performance for reading and writing data. Local SSDs are also cost-effective and provide low-latency storage access compared to other storage options such as persistent disks or network storage.

Option D: M1 machine type is a general-purpose machine type that offers a balance of computing power and memory. However, for an ERP system that holds the entire database in-memory, the priority is fast access to the data rather than computing power. Therefore, a machine type with high memory capacity, such as the memory-optimized machine type, may be a better choice. However, the machine type alone may not provide the necessary storage performance required by an ERP system.