Google Compute Engine is a virtual machine (VM) service that allows users to create and run VMS on Google’s infrastructure. Compute Engine provides a variety of VM types, including standard, high-memory, high-CPU, and custom machine types. It is an unmanaged computing service and is generally referred to as customizable virtual machines in Google Cloud.
To know more about the services offered by Google Cloud read the blog Google Cloud Services & Tools
In this blog, we are going to cover the Compute Engine and its benefits, advantage, and features.
What Are Virtual Machines?
Virtual Machines in layman’s language can be understood as a digital version of the physical computer. It is a virtualized instance of a computer that can perform almost all the functions as that of a computer. Each virtual machine consists of a complete operating system and a set of virtualized hardware resources, including CPU, memory, storage, and network interfaces. The operating system running inside the virtual machine is isolated from the host operating system and other virtual machines running on the same physical machine. Virtual machines run on a physical machine and access computing resources from the software which is known as a hypervisor.
Usage of Virtual Machines:
- Create development and test environments — For testing and development, isolated environments can be built using virtual machines (VMS). In a controlled and separated environment, developers may simply create and test new software without affecting the production environment
- Enable workload migration-Virtual machines (VMS) are used to deliver cloud computing services by cloud service providers like Google Cloud Platform (GCP). For the purpose of running their applications and workloads, customers can construct and manage VMS in the cloud.
- Improve disaster recovery and business continuity- VMS can be used as a part of a disaster recovery strategy. In the event of a disaster, virtual machines can be easily migrated to a backup site, reducing downtime and data loss.
- Consolidated servers– Virtualization enables businesses to combine their physical servers into a smaller number of more potent ones. This lowers hardware expenses and makes management easier.
Google Cloud Compute Engine
Google Compute Engine is Google’s Infrastructure-as-a-Service virtual machine offering. It allows customers to use virtual machines in the cloud as server resources instead of acquiring and managing server hardware. Google Compute Engine offers virtual machines running in Google’s data centers connected to the worldwide fiber network. The tooling and workflow offered by compute engine enable scaling from single instances to global, load-balanced cloud computing.
Applications Of Compute Engine
Below are some of the use cases or applications of the Google compute engine:
1.) Virtual Machine (VM) migration to Comput Engine: It provides tools to fast-track the migration process from on-premise or other clouds to GCP. If a user is starting with the public cloud, then they can leverage these tools to seamlessly transfer existing applications from their data center, AWS, or Azure to GCP. Users can have their applications running on Compute Engine within minutes while the data migrate transparently in the background.
2.) DevOps: GCE provides an easy-to-use platform for building and deploying applications using popular tools such as Docker, Kubernetes, and Jenkins.
3.) Disaster recovery: GCE provides a reliable infrastructure that can be used for disaster recovery purposes. Users can easily create backups and replicas of their infrastructure in case of unexpected outages or failures.
4.) High-performance computing (HPC): GCE provides access to powerful computing resources for running high-performance computing workloads such as simulations, rendering, and scientific computing.
5.) Genomics Data Processing: Processing genomic data is computationally intensive because the information is enormous with vast sets of sequencing. With the Compute Engine’s potential, users can process such large data sets. The platform is not only flexible but also scalable when it comes to processing genomic sequences.
6.) BYOL or Bring Your Own License images: A Compute Engine can help you run Windows apps in GCP by bringing their licenses to the platform as either license-included images or sole-tenant nodes. When users migrate to GCP, they can flexibly optimize their license and promote the bottom line.
Advantages Of Compute Engine
- Storage Efficiency: The persistent disks support up to 257 TB of storage which is more than 10 times higher than what Amazon Elastic Block Storage (EBS) can accommodate. Organizations that require more scalable storage options can go for Compute Engine
- Cost: Within the GCP ecosystem, users pay only for the computing time that they have consumed. The per-second billing plan is used by the Google compute engine.
- Stability: It offers more stable services because of its ability to provide live migration of VMs between the hosts.
- Backups: Google Cloud Platform has a robust, inbuilt, and redundant backup system. The Compute Engine uses this system for flagship products like Search Engine and Gmail.
- Scalability: It makes reservations to help ensure that applications have the capacity they need as they scale.
- Security: Google Compute Engine is a more secure and safe place for cloud applications.
Benefits Of Compute Engine
- Easy Integration: It allows to easily integrate with other Google Cloud services like AI/ML and data analytics.
- Compute globally as per requirement: Rendering reservations for supporting and securing applications having the strength as per measurement and requirement.
- Gain Infinite Value: Preventing cost only for executing Compute with sustained-use discounts, and obtaining huge profits while implementing devoted-use discounts.
- Confidential Computing: Confidential VMs is a kind of advanced technology that enables users to encode delicate data into the cloud while data is being processed.
Google Compute Engine Features
Google Compute Engines has many features and some of them are discussed in detail below.
1.) Machine Types
It describes the virtual hardware that is attached to an instance which also includes RAM and CPUs.
There are several types of machine families:
- General purpose machine types:- Machines with a general purpose are the most prevalent and can handle a variety of workloads. They come in various sizes and offer a balance of CPU and memory resources. n1-standard, e2-standard, and e2-highcpu are some examples.
- Compute-optimized machine types:- These are made to handle workloads that demand on a lot of memory. Compared to general-purpose computers, they feature a larger memory-to-CPU ratio. N1-highmem and e2-highmem are two examples.
- CPU-optimized machine types:- These are developed for applications like video transcoding, gaming, and simulation that demand a lot of CPU power. They have more virtual CPUs per unit of RAM than general-purpose computers. N1-highcpu and e2-highcpu are two examples.
- Accelerated computing machine types:- These are produced for activities that call for specialized hardware, such GPUs or TPUs. They are enhanced for scientific computing, machine learning, and deep learning. Examples include n1-highmem-96 and n1-standard-gpu.
Predefined Machine Types: These are pre-configured virtual machine templates that can be used to set up virtual machines. The configurations have been pre-optimized by Google and meet most of the requirements. The predefined machine types are divided into four categories:
- Standard VMs
- High-memory VMs
- High-CPU VMs
- Shared-core VMs
Custom Machine Types: The virtual hardware can be configured manually for a Compute Engine VM instance. Users can select the number of virtual CPUs (vCPUs) and memory, provided they are within Google’s set limits.
2.) Persistent Disks
These are durable, high-performance block storage for VM instances which can be created in HDD or SSD formats. Users can take snapshots and create new persistent disks from that snapshot. If a VM instance is terminated, the data is retained by the persistent disk which can be attached to another instance. There are two types of persistent disks:
- Shared
- SSD
3.) Local SSD
Google Compute Engine offers always-encrypted local solid-state drive (SSD) block storage which is physically attached to the virtual machine running it. It improves performance and reduces latency.
4.) GPU Accelerators
GPUs are added to accelerate computationally intensive workloads like machine learning, virtual workstation applications, etc.
5.) Images
An image contains the OS and the root file system that users leverage to run VM instances. Google Cloud Platform provides two main types of images:
Public Images: These are a collection of open-source and proprietary options. They serve as a starting point for most virtual machine instances and come packaged with only the operating system.
Custom Images: Public images are a good starting point, but they are designed to be built upon and turned into custom images to match the needs of the customers. A custom image has the software needed along with all the scripts necessary for the instance to work automatically without administrator intervention. These are automatically brought up and shut down for load balancing or recovery needs.
6.) Global Load Balancing
It helps in distributing incoming requests across pools of instances across multiple regions so that users can achieve maximum performance, throughput, and availability at a low cost.
Similarly, there are many other features like Linux and Windows support, container support, reservations, OS patch management, Live migration for VMs, and many more.
Google Compute Engine has many pros such as fast I/O, less access time, smooth integration with other Google services, and few cons as well like most components are based on proprietary technologies and the choice of programming languages is limited. But it offers scale, performance, and value that allows users to launch quickly large compute clusters on Google’s infrastructure.
Compute Engine Pricing
Compute Engine pricing in Google Cloud Platform (GCP) is based on several factors, including the type of machine instance selected, the duration of usage, and the amount of storage and network bandwidth used.
Businesses can use the GCP Pricing Calculator to estimate the cost of running particular workloads on Compute Engine and have a better understanding of Compute Engine pricing in GCP. In addition, Compute Engine provides sustained use discounts, which are automatic reductions based on the length of usage, with reductions rising with usage.
Frequently Asked Questions
What is a Google Compute Engine instance?
An instance is a virtual machine that is hosted on Google's infrastructure. Instances can be created by using the Google Cloud Console, the gcloud command-line tool, or the Compute Engine API.
What is the difference between Compute Engine and App Engine?
Compute Engine is an IaaS offering where the users have to create and configure their own virtual machine instances whereas App Engine is a PaaS offering where users have to deploy their code and everything else is handled by the platform.
What business value can Compute Engine provide?
Compute Engine offers better kernel-level control, and encryption, and makes it easier to create and configure high-performance-based virtual machines that can easily and quickly scale to any size workload.
Related References
- GCP Associate Cloud Engineer: All You Need To Know About
- GCP Professional Cloud Architect: Everything You Need To Know
- Introduction To Google Cloud Platform
- Google Cloud Services & Tools
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