Google Compute Engine

We use GCP (Google Cloud Platform) for following purposes:
- VM instances for Linux as well as Windows
- GKE Cluster for hosting self auto-scaling cluster running dynamic/multiple applications
- Multi-user project management
- Multiple projects hosted with separate IAM users
- Scaling Up/Down based on requirements of servers and applications.

In our organization, we use Google Compute Engine (GCE) as central component of our cloud infrastructure. It provides us with scalable and flexible platform to sustain our virtual machines, which our applications run on, as well as storage and analytics capabilities. This tool helps us to address some business challenges like allows us easily to scale our resources up or down based on demand, ensuring that our applications remain permanent and available. Google Compute Engine greatly assists us in expanding or decreasing resources without worrying about sustaining it by ourselves. This has helped keeping our software running without interruption and with the highest performance.

Google Compute provides solutions to build dynamic websites and apps for clients without consuming much time. The tool has many features to build a customized apps according to our clients needs. It works on many programming languages and gives us option to build an app based on clients primary requirements.

Google Compute Engine (GCE) is the Infrastructure as a Service platform that we use for running workloads for different product/project teams. With GCE we can create custom VMs for different OS like Windows and different flavours of Linux. These VMs can support small/highly optimized configuration. GCE also provides us with ability to create high performance cloud storage spaces.

Compute engine is an offering of GCP, it provides scalable and flexible virtual machine resources for organisations. Business Problem it addresses: Cost-Effectiveness Scalability Performance.

Google Compute Engine provides a high level of customization, flexibility, & scalability in contrast to this, storage is also a major role that it plays. Performance and Security are the Real Factors to go for the GCP Compute Engine

Google Compute Engine (GCE) to host and manage our applications and services in a scalable, reliable, and cost-effective manner. GCE helps us address business challenges related to scalability, reliability, performance, security, and cost-effectiveness. Our use case for GCE includes hosting web applications, running batch processing jobs, supporting machine learning tasks, and more, contributing to our organization's agility and innovation in the cloud.

We use Google Compute Engine to host websites and APIs for small businesses. It lets us adjust resources based on how much traffic our clients get, making sure their sites and apps run smoothly without them needing to deal with server management. We pick different types of virtual machines depending on what each client needs, e.g a simple blog or an online store.

We use Google Compute Engine instances as our main servers to host our services and applications that are externally or internally used across the whole company. They are easy to use, configure, replicate, update, secure the machines, etc.

Currently, we face no problems with this service and everything is working more than fine.

We use it for cross platform software testing and its performance, earlier we were using VM Ware for the same but because Google Compute Engine grants you with more features to increase/decrease the core capabilities of the OS involved for the application testing, we are able to do regression testings more efficiently.

It helped us in identifying the key lacking areas/bugs during the initials phases of a product development.

A scalable and flexible setup to quickly get our apps up and running without dropping tons of cash upfront, paying only for what we actually use with that pay-as-you-go thing and resources automatically scaling to match demand. High availability comes baked in through load balancing across Google's global infrastructure, with security features and custom VM configs letting us tailor things to our app's needs. With growth, it is possible to integrate with Google's full suite of cloud services like analytics, machine learning, serverless, etc. - to keep adding on capabilities while still focusing on building cool stuff instead of dealing with infrastructure headaches. Comparable to AWS EC2.

In our company, we primarily use Google Compute Engine to create VM instances that would power our website our testing environment and as a CI/CD tool.

We use Google Compute Engine to run jenkins. This jenkins jobs do various tasks.
- Building products
- Running integration test
- Running accreditations tests
Before every release we set up a production grade deployment using Google Compute Engine and do performance test.

We use it for microservices that has burst processing demands - they require lots of processing power for very short periods of time, thus requiring powerful hardware that, for most of the time, goes on without use. We wanted a cloud solution to avoid the hardware and infrastructure inherent to that scenario.

Most development servers and build machines are migrating into Google Cloud Compute engine. This allows us to spin up/down resources on demand based on workload, product needs, etc. QA does the same for testing resources.

My company uses Google Compute Engine to clean our "dirty" EDI files from the ports, terminals, and carriers that we work with for data cleansing services. We then put the cleansed files back into our dashboards for better visibility and to show potentially actionable insights for our customers.

I have used Google Compute Engine for testing and deploying SAP
S/4 Hana appliance for the internal training and learning purpose in Google cloud platform environment.

We use it as compute for external services outside our normal cloud as backups

We are using GCE to prototype new projects at scale for our customers. Unlike on premise infrastructure we can create servers at a moments notice and limited to just the timespan required. This saves us having to procure servers from our partnered procurement organisations which typically has a much longer lead time and hugely improves the overall time it takes to start a project.

Our specific department uses google cloud for its data analytics capabilities but also for its scalable and flexible virtual/compute infrastructure. We have some large jobs that we will provision ad-hoc compute to assist as needed. Google Compute Engine excels in this area and has never failed us.

Google Compute Engine is being used across the company for all of the server needs. It lets us create VMs with a variety of customizable options. It does give us a predefined set of VMs that can be directly used based on needs. It also helps us to reduce the cost by using the right size of VMs.

We use Google Compute Engine to support almost all of our in house computer. We have a web application that has a distributed backend of various workloads, all of which run on Compute Engine. Some of these workloads are even containerized. These workloads support the whole organization and provide analysis for research and development.

We use Google Compute Engine across the organization along with other Google Cloud Platform Services. We have interconnected collections of Google Cloud Platform Services which include among these sets of services components deployed on the Google Compute Engine, which act as the backbone of multiple customer facing production deployments.<br><br>To be more precise we have one product which is a web app for monitoring and prediction of household energy consumption and household solar panel power generation. To implement this on GCP we are using numerous GCP services including Google Compute Engine but not limited only to GCE.<br><br>Additionally, we have another product which focuses on voltage optimization primarily for the purpose of conservation voltage reduction at a particular customer site (e.g. a substation controlling a factory, school, large building or facility, etc). The voltage optimization consists primarily of monitoring sensors and performing a near real-time prediction and optimization, and then providing recommended voltage levels for an OLTC controller. This particular deployment must occasionally be deployed on-site, however we also support production level cloud deployment. The cloud deployment will use Google Compute Engine to host a Docker environment, that mirrors the Docker environment that we set up on the machines for the onsite deployments. <br><br>We also use Google Compute engine in a number of prototype builds for testing the efficacy of data science and machine learning models and as a platform for quick collaboration during remote work. Though the number of such deployments are myriad, hence I will forgo the details.<br><br><br><br><br><br><br><br><br>

For hybrid cloud solution we decided to use the separated cloud provider as a DR (pilot light model) in our project. Compute Engine was created in one Region with general purpose machine family. The project requirements were set - one of the first priorities - cloud provider independence.

We use Google Compute Engine to host a variety of services including proxy servers, small websites, and peripheral services. Additionally, we make use of the Google Compute Engine to quickly spin up instances for testing and experimentation and then destroy them once finished without incurring a lot of costs, both money and time.

Google Compute Engine (GCE) is used for most of the AI workload spanning both on-prem private cloud and public cloud. It is used for both onetime training phase for our Deep Learning workload as well as ongoing Deep Learning inference for customer facing applications.