The correct answer is B. Increase the memory limit in the application deployment.
The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory.The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.

https://cloud.google.com/architecture/continuous-delivery-toolchain-spinnaker-cloud
https://spinnaker.io/guides/user/pipeline/triggers/pubsub/
The most efficient way to build an automated pipeline that deploys the application when the image is updated is to use Cloud Pub/Sub to trigger a Spinnaker pipeline. This way, you can leverage the built-in integration between GCR and Cloud Pub/Sub, and use Spinnaker as a continuous delivery platform for deploying your application .

https://cloud.google.com/container-registry/docs/pushing-and-pulling

https://cloud.google.com/binary-authorization/docs/overview

Comprehensive and Detailed Explanation:
The delay in initiating on-premises deployments is likely caused by slow artifact retrieval. Using a local artifact repository:
Reduces dependency on external storage (e.g., Artifact Registry in GCP).
Speeds up deployments by ensuring artifacts are read from a closer, low-latency source.
#Why not other options?
A (Blue-green deployment strategy)## Helps with zero-downtime deployments but doesn't address slow initiation.
B (Upgrading CPUs and memory)## CPU/RAM are not the issue (since resource utilization is normal).
C (Increasing Jenkins executor threads)## Would help only if Jenkins was the bottleneck, but the issue is artifact retrieval.
#Official Reference:
Best Practices for Artifact Management in Jenkins