GCP Compute Engine
Google Cloud Platform (GCP) offers a variety of virtual machine (VM) types categorized into families based on their characteristics and intended use cases. Here's an overview of the main types:
1. General-purpose:
Balanced: Suitable for diverse workloads like web applications, databases, and development environments. Examples include C3, E2, N2, N2D, and Tau T2D.
Shared-core: More cost-effective for low-resource workloads, sharing physical cores with other VMs. Examples include E2 shared-core and N1.
2. Compute-optimized:
High-performance: Ideal for CPU-intensive tasks like scientific computing and video rendering. Examples include C2 and C2D.
3. Memory-optimized:
Large memory: Designed for applications requiring significant memory, like databases and in-memory analytics. Examples include M2 and M1.
4. Accelerator-optimized:
Specialized hardware: Equipped with GPUs, TPUs, or other accelerators for specific workloads like machine learning and graphics processing. Examples include A2, A3, and G2.
5. Preemptible VMs:
Discounted resources: Offer significant cost savings with the possibility of termination during high demand.
Types of Block Storage
You can choose the best Persistent Disk option for you based on your cost and performance requirements.
Types
? SSD Persistent Disks leverage flash memory chips akin to USB drives, facilitating swift data access suitable for boot disks, databases, and I/O-intensive workloads.
? HDD Persistent Disks, while more cost-effective, offer lower performance compared to SSDs, making them suitable for less demanding workloads or data archival purposes. Utilizes spinning platters coated with a magnetic material for data storage.
Subtypes
? Local SSD disks are physically attached to the server hosting the VM, providing superior throughput and lower latency compared to standard Persistent Disks or SSD Persistent Disks. However, data stored on Local SSD disks persists only until the VM is stopped or deleted.
? Standard PD, utilizing HDD, offers standard throughput, making it suitable for cost-sensitive applications and scale-out analytics with Hadoop and Kafka.
? Balanced PD, utilizing SSD, provides the best price per GB, making it ideal for common workloads such as line of business apps, boot disks, and web serving.
? Performance PD, employing SSD, offers the best price per IOPS and is well-suited for performance-sensitive applications like databases, caches, and scale-out analytics.
? Extreme PD is SSD optimized for applications demanding uncompromising performance, such as SAP HANA, Oracle, and large in-memory databases.
? Hyperdisk Extreme Persistent Disk (PD) is a high-performance block storage option tailored for mission-critical applications necessitating superior IOPS and throughput.
Regional and Zonal Disks
? Regional persistent disks are replicated across multiple zones within a region, ensuring heightened availability and disaster recovery capabilities.
? Zonal persistent disks are confined to a single zone, offering lower cost but less redundancy.
Storage optimization:
? Choose the right disk type: Utilize Standard SSD Persistent Disks for most workloads. Consider HDD Persistent Disks for cold storage or less demanding use cases. If extreme performance is needed, Hyperdisk Extreme PD can be considered, but at a higher cost.
? Optimize disk size: Don't allocate more disk space than you need. Adjust disk sizes as your storage needs evolve.
? Consider snapshots and backups: Leverage snapshots for efficient backups instead of copying entire disks, saving storage space and cost.
Snapshots:
? Backup and recovery: Snapshots are a quick and efficient way to back up your data or system configuration. If something goes wrong, you can easily restore the system to its previous state using the snapshot. This is especially valuable for protecting against accidental deletions, software errors, or cyberattacks.
? Testing and experimentation: You can create snapshots before trying out new software updates, configurations, or applications. If something goes wrong, you can simply restore the system to its previous state using the snapshot, ensuring a safe testing environment without jeopardizing your actual data.
? Version control: Snapshots can be used to track different versions of your data or system configuration over time. This helps compare changes, roll back to previous versions if needed, or even deploy specific versions to different environments.
? Disaster recovery: In case of a major outage or disaster, snapshots can be used to quickly restore your system to a known good state, minimizing downtime and data loss.
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? They can be stored for later use, but storage space needs to be considered.
Machine Images:
Contains the entire software environment of a virtual machine (VM), including the operating system, installed applications, configurations, and data.
Represent a complete snapshot of a specific VM's state at a given point in time.
Useful for:
Instance Templates:
Define the hardware and configuration settings for a VM, such as the machine type, boot disk image, network, and firewall rules.
Do not contain the actual software or data, but just specify the blueprint for the VM.
Useful for:
Cost Optimization Strategies for GCP Virtual Machines:
Optimizing the cost of your GCP VMs requires a multi-pronged approach. Here are some key strategies:
Discount programs and tools:
? Committed Use Discounts (CUDs): Commit to specific VM usage terms upfront for significant discounts on sustained use.
? Sustained Use Discounts (SUDs): Automatically receive discounts based on consistent monthly VM usage above a certain threshold.
? Reserved Instances: Purchase VMs up-front for a discounted hourly rate, benefiting from predictable costs and guaranteed capacity.
? Cost Management tools: Utilize GCP's built-in tools like Cost Management and Billing to track, analyze, and optimize your cloud spending.
? Enable billing alerts: Set up alerts to notify you of unexpected spikes in VM costs for prompt investigation and action.
Region-Based Optimization Strategies:
? Deploy VMs in the most cost-effective region: VM instance pricing generally varies across regions due to factors like electricity costs, infrastructure differences, and local competition. Analyze VM costs, egress charges, and your user location to identify the most economical region for your workload.
Resource sizing and utilization:
? Right-size your VMs: Choose the VM type and size that closely matches your workload's needs. Don't overprovision. Use tools like Cloud Monitoring and Benchmarking Service to assess resource utilization and identify opportunities for downsizing.
? Utilize preemptible VMs: Use preemptible VMs for batch jobs or fault-tolerant workloads that can handle interruptions at a significant cost discount.
? Vertical scaling vs. horizontal scaling: Consider scaling vertically (increasing core and memory) before adding more VMs horizontally, as it can be more cost-effective for certain workloads.
Instance lifecycle management:
? Stop/delete unused VMs: Identify and terminate idle VMs that are no longer needed. Use scheduling tools like Cloud Scheduler to automate stop/start cycles based on predictable usage patterns.
? Utilize managed instance groups (MIGs): Deploy VMs using MIGs to leverage features like auto-scaling based on CPU, memory, or custom metrics, automatically adjusting resources to match demand.
References
Choosing the right storage:
Choosing the right virtual machine type: https://cloud.google.com/compute/docs/machine-resource
Machine families resource and comparison guide: https://cloud.google.com/compute/docs/machine-resource