The critical constraint for both the website and the background job is the requirement for autoscaling and no costs when not in use (scale to zero). Both workloads are suitable for containerization and HTTP invocation.
* Cloud Run is the single best platform for both use cases under these constraints. It is serverless, fully managed, and natively supports scaling to zero when a containerized service is not receiving requests, making it the most cost-effective option for sporadic workloads.
* The static website can run in a simple web server container on Cloud Run. The HTTP-triggered background job is a perfect fit for a Cloud Run service that runs, performs its work (invoice generation), and then scales down to zero.
Reference: Google Cloud Documentation - Cloud Run (Overview):
"Because Cloud Run is serverless... It scales up or down automatically, including scaling to zero to minimize your cost." Reference: Google Cloud Documentation - Cloud Run (Use cases):
"Cloud Run is suitable for... background processing (jobs, batch processing, and other non-HTTP services), and services that require scaling to zero."

/25:
CIDR to IP Range
Result
CIDR Range 172.16.20.128/25
Netmask 255.255.255.128
Wildcard Bits 0.0.0.127
First IP 172.16.20.128
First IP (Decimal) 2886734976
Last IP 172.16.20.255
Last IP (Decimal) 2886735103
Total Host 128
CIDR
172.16.20.128/25
/24:
CIDR to IP Range
Result
CIDR Range 172.16.20.128/24
Netmask 255.255.255.0
Wildcard Bits 0.0.0.255
First IP 172.16.20.0
First IP (Decimal) 2886734848
Last IP 172.16.20.255
Last IP (Decimal) 2886735103
Total Host 256
CIDR
172.16.20.128/24

Let's analyze each option:
A: Using gsutil: While gsutil can transfer data to Cloud Storage, for 150 TB of infrequently accessed data, direct transfer over the network might be slow and consume significant bandwidth, potentially impacting other network operations. It also lacks built-in mechanisms for filtering files based on age.
B: Using Cloud Interconnect and Filestore: Cloud Interconnect provides a dedicated connection, but Filestore is a fully managed NFS service primarily designed for high-performance file sharing for applications running in Google Cloud. Migrating 150 TB of infrequently accessed data to Filestore would be cost-inefficient compared to Cloud Storage and doesn't directly address the requirement of moving older than one year files.
C: Using Transfer Appliance: Transfer Appliance is suitable for very large datasets (petabytes) or when network connectivity is poor or unreliable. While it addresses bandwidth concerns, it involves a physical appliance and might be an overkill for 150 TB of data, especially if network connectivity is reasonable.
D: Using Storage Transfer Service: Storage Transfer Service is specifically designed for moving large amounts of data between online storage systems, including on-premises file systems and Cloud Storage. It offers features like filtering by file age, scheduling transfers, and bandwidth control, directly addressing all the requirements of the question: migrating infrequently accessed files older than one year to Cloud Storage, minimizing costs (by using appropriate Cloud Storage classes for infrequent access), and controlling bandwidth usage.
Google Cloud Documentation References:
Storage Transfer Service Overview: https://cloud.google.com/storage-transfer-service/docs/overview - This page details the capabilities and use cases of Storage Transfer Service, including transferring from on- premises.
Storage Transfer Service for on-premises data: https://cloud.google.com/storage-transfer-service/docs/on- prem-overview - This specifically covers transferring data from on-premises file systems.
Cloud Storage Classes: https://cloud.google.com/storage/docs/storage-classes - Understanding the different storage classes (Standard, Nearline, Coldline, Archive) is crucial for cost optimization of infrequently accessed data. Storage Transfer Service can be configured to move data to a cost-effective class like Nearline or Coldline.

1. In Stackdriver Logging, create a filter to view only Compute Engine logs. 2. Click Create Export. 3.
Choose BigQuery as Sink Service, and the platform-logs dataset as Sink Destination.

When applied to a dataset, this role provides the ability to read the dataset's metadata and list tables in the dataset. When applied to a project, this role also provides the ability to run jobs, including queries, within the project. A member with this role can enumerate their own jobs, cancel their own jobs, and enumerate datasets within a project. Additionally, allows the creation of new datasets within the project; the creator is granted the BigQuery Data Owner role (roles/bigquery.dataOwner) on these new datasets.
https://cloud.google.com/bigquery/docs/access-control