Final answer:
When choosing between big VMs and many VMs on Compute Engine, big VMs are better for in-memory databases and CPU-intensive analytics due to efficient resource usage, while many VMs offer fault tolerance and elasticity by enabling workload distribution and easy scaling.
Step-by-step explanation:
When Compute Engine customers decide between using big virtual machines (VMs) and many VMs, the choice largely depends on the workload requirements and the specific use case. Big VMs, which consist of larger instances with more CPU and memory resources, are better suited for applications that require high computing power in a concentrated form, such as in-memory databases and CPU-intensive analytics. These applications benefit from large contiguous blocks of resources because they can process data more efficiently compared to spreading the same workload across multiple smaller VMs.
On the other hand, deploying many smaller VMs is advisable for achieving greater fault tolerance and elasticity. By spreading the workload across multiple instances, if one VM fails, the others can continue to operate, minimizing downtime and ensuring a more resilient system. Furthermore, this strategy allows for easy scaling, as additional VMs can be brought online to handle increased load, or existing VMs can be decommissioned during periods of low demand.