Google Compute Engine

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.

Google Compute Engine is used for a variety of workloads. Its use is growing as larger parts of the organization start embracing cloud computing.

• Internal applications.
• Tools.
• QA and testing environments.
• Production deployment.
• Experimentation with new technologies.
• Migration from datacenter to cloud environments.
• Training environments (they can be easily created and then deleted after the training).
• Business processing.
• Data processing and pipelines (in combination with various other products available in Google Compute Platform (GCP).

We use Google Compute Engine for staging deployments in the web development department. We are operating off an external assessment that competitor services offer better pricing on production units, so we've agreed to keep Compute Engine in a low tier staging system only. We have yet to do our own audit from an internal efficiency perspective, and how that will impact pricing assessments.

Using Google Compute Engine, you can manage your costs. It helps to avoid the high costs of buying hardware and setting up a new server infrastructure on your physical location. Also, Google Computer Engine offers you the possibility of accessing your files and data with higher security, and save days worth of time spent on setup and installation. We migrated from hardware to visualized servers hosted to Google Compute Engine. We made Windows and Linux servers and hostel our core-services: ERP, accounting, marketing app.

Google Cloud Compute Engine is used by the organization to do automated advanced analytics for clients across the world. The software addresses two important aspects of our business: first, it runs in the background and with speed and accuracy that works well for the analyst as well as others; second, the software runs in the cloud, which makes analyzing the data generally easier.

We use compute engine across a number of departments at our company. We deploy temporary workloads to VMs on a daily basis and have deployed our production systems to GCE for deployment and CI/CD pipelines, ETL for data projects, and for large-scale customer engineering.
GCE is in play primarily for our engineering department as well as our customer engineering and sales teams.

The big advantage of using Google Computer Engine is that pricing is much less than if you bought your own hardware. You are only paying for what you need, there's no over-spend required for redundant systems. Google Compute Engine is one of the best IaaS cloud solution at the moment.

We use Google Compute Engine as IaaS provider across the whole organization. Almost all of our servers are deployed in this environment. We also help some of our customers to deploy virtual machines due to our expertise in cloud computing. We think it is a reliable platform that delivers everything we need regarding IaaS.

Google Compute Engine is used by the DevOps team for hosted Windows servers. This alleviates countless concerns with having a server on-site, and is very comparable to other hosting providers.

We use Compute Engine for one of our main consumer and business products. We have a globally distributed infrastructure for this. I believe we are using almost all the features/functionalities offered by GCE. e.g Instance, groups/templates with full rolling updates capability coupled with CI/CD. We also host part of our monitoring systems in GCE. Other than that, some other teams are on the POC phase for some critical backend products as well. Moreover, we experiment with some of our machine learning on Compute Engine. The benefit we get by using GCE is the combination of the autoscaling, rolling updates, preemptability, and global network.

We use Google Compute Engine for creating cloud virtual machines that are GPU-accelerated. Because we have centralized most of our backend services to DigitalOcean, we use Google Compute Engine for most of the services that DigitalOcean doesn't offer. Additionally, we use the custom image functionality that GCE offers to create VMs with custom images.

We use it for running managing docker containers inside clusters. GCE provides the flexibility for auto scaling and managing application performance easily. It used for running all the applications across departments

We use Google Compute Engine to host our cloud-based web application. We manage a multi-node, shared instance of our application for thousands of monthly users, as well as individual, dedicated instances for a few of our larger clients. We are a fairly small organization and GCE manages all of our hosting needs.

GCE is very straightforward to use, most of our engineers interact with it on a daily basis. Using GCE means that we can forget about the pains of maintaining computing hardware and just focus on making great software. As a Google Apps user, we also benefit from GCE's rich integration with the rest of the Google product line. Picking GCE over competitors was an easy choice for us.

Google Compute Engine is for maintaining our organizational department wise projects such as online training applications, online student registrations, blog posts, tutorials, etc.

We need to separate our organization projects in the various department because these applications are developed for solving several problems for filtered users. Google Compute Engine is used for hosting our online students' maintenance and other related tasks.

Compute Engine is a general purpose VM on top of which you can run basically any software you want, including Windows. They are priced on per-hour basis with discounts when you use it for a long time, and also with an option to run a VM for a short time with significant discount, but at a risk that it could be terminated at any time (Preemptible VM) – it's very useful for data processing since you can spawn tens of VMs for cheap and even if they are terminated you can just do it again later.

Google Compute Engine (GCE) is Google's answer to AWS. We use GCE as a low-cost virtual host solution, and we purposefully migrated off AWS and Rackspace to GCE. We made the decision mostly based on cost, but also the flexibility of the toolkit and deployment. GCE is a one-stop shop for hosting virtual and scalable environments in the cloud.

We're a small custom software company with one main web-based program which we host on Google Compute Engine. Over the years we've gained more and more users and we were having issues scaling up as we grew, sometimes slowly and sometimes rapidly. Finally our old hosting solution had a bad down period and we were forced to look elsewhere to get our software back online. Since switching to Compute Engine, we've been able to save money while scaling up as needed. It has been very reliable and overall a perfect fit for us.

It is being used across the whole organization. It addresses the problem of providing solutions to customers who need an all-inclusive infrastructure solution for web services and apps. It has a lot of systems that are managed by Google which people commonly build themselves without knowing that they are reinventing the wheel.

We develop software for our clients and lean on Google Compute Engine and Google Container Engine for hosting those applications. These applications are used both across our clients' organizations as well as publicly by customers of these clients. We made the decision to use Google Compute Engine in order to reduce costs while getting solid reliability from a VPS platform. Google has provided us with both of those needs.

We use Google Compute Engine to support public facing clinical research patient recruitment services, the service is scalable to accommodate increase in capacity due to news items that sign post patients to the services. The platform is IaaS and data is shared with major health charities.

It is being used by a department. It is used to host our school department's website, which is used by both teaching assistants and students.