Google Cloud Storage Plans – One of the biggest benefits of running on is being able to scale up and down to meet demand and reduce operational costs. And this is especially true when you’re experiencing unexpected changes in customer demand. Here at Google, we have an entire team of solution architects dedicated to helping customers manage their operating expenses. Over the years of working with some of our biggest clients, we’ve identified some of the most common things people miss when looking for ways to optimize costs, and compiled them here for you. We think following these best practices will help you align your costs to your business needs, so you can get through these challenging, unpredictable times.
Cost Management Cost Optimization: Principles for Sustainable Success Learn organizational principles that can help you run your environment efficiently and cost effectively. By Justin Lerma • 11-minute read
Google Cloud Storage Plans
1. Know Billing and Cost Management Tools Due to the on-demand, variable nature, costs have a way of creeping up on you if you’re not monitoring them closely. Once you understand your costs, you can begin to put controls in place and optimize your spending. To help with this, Google offers a robust set of cost-free billing and cost management tools that can give you the visibility and insight you need to keep up with your deployment. At a higher level, “Which projects cost the most, and why?” Learn to look for things like starting, managing and structuring your costs in relation to your business needs. Then, drill down into services using the billing reports to get them. An at-a-glance view of your costs. You should also learn how to attribute costs to departments or teams using labels and build your own custom dashboards for more granular cost views. You can also use quotas, budgets and alerts to monitor closely. Monitor your current cost trends and forecast them over time, to reduce the risk of overspending. If you’re not familiar with our billing and cost management tools, we’re offering free training for a limited time to help you understand and learn the basics of customization. Your Google costs. For a detailed step-by-step guide, see our Billing Guide and watch our Beyond Your Bill video series. Be sure to also check out these hands-on training courses: Understanding Your Google Costs and Optimizing It. ur GCP cost.2. Pay only for the compute you need Now that you have better visibility into your spending, it’s time to set your sights on your most expensive project(s) to identify compute resources that aren’t providing enough business value. Identify idle VMs (and disks): The easiest way to reduce your Google Platform (GCP) bill is to get rid of resources that are no longer being used. Think of those proof-of-concept projects that have since been shelved, or zombie instances that no one bothered to delete. Google offers several recommenders that can help you optimize these resources, including the Idle VM Recommender that identifies idle virtual machines (VMs) and Persistent Disks based on usage metrics. However, always tread carefully when deleting a VM. Before deleting a resource, ask yourself, “What will be the potential impact of deleting this resource and how can I recreate it if necessary?” Deleting instances gets rid of the underlying disk(s) and all of its data. A best practice is to take a snapshot of the instance before deleting it. Alternatively, you can choose to stop the VM, which terminates the instance, but retains resources such as disks or IP addresses until you detach or delete them. For more information, read the recommender document. And stay tuned as we add more usage-based recommendations to the portfolio. Schedule VMs to start and shut down automatically: The advantage of a platform like Compute Engine is that you only pay for the compute resources you use. Production systems run 24/7; However, VMs in development, test or personal environments are only used during business hours, and turning them off can save you a lot of money! For example, a VM that runs 10 hours per day, Monday through Friday costs 75% less per month to run it. To get started, here’s a serverless solution we’ve developed to help you automate and manage VM shutdown at scale.
How To Optimize Your Google Cloud Costs
Rightsize VMs: At Google, you can already realize significant savings by creating a custom machine type with the right amount of CPU and RAM to meet your needs. But workload requirements may change over time. Instances that were once optimized may now be serving less users and traffic. To help, our entitlement recommendations can show you how to effectively downgrade your machine type based on changes in vCPU and RAM usage. These entitlement recommendations for your instance machine type (or managed instance group) are generated using system metrics collected by monitoring over the past eight days.
For organizations using infrastructure as code to manage their environments, check out this guide, which will show you how to implement VM rights recommendations at scale. Leverage preemptible VMs: Preemptible VMs are highly affordable compute instances that last 24 hours and are up to 80% cheaper than regular instances. Preemptible VMs are an excellent fit for fault tolerant workloads such as big data, genomics, media transcoding, financial modeling and simulation. You can also use a mix of regular and preemptible instances to quickly and cost-effectively terminate compute-intensive workloads, by setting up a specially managed instance group. But why limit preemptible VMs to Compute Engine environments? Did you know GPUs, GKE clusters and secondary instances in Dataproc can also use preemptible VMs? And now, you can also reduce your dataflow streaming (and batch) analytics costs by using flexible resource scheduling to supplement regular instances with preemptible VMs.3. Optimize storage costs and performance When you run your own data center, storage tends to get lost in your overall infrastructure costs, making it difficult to properly manage costs. However, where storage is billed as a separate line item, paying attention to storage usage and configuration can result in substantial cost savings. And storage needs, like compute, are always changing. It’s possible that the storage class you chose when you first set up your environment may not be suitable for a given workload. Also, storage has come a long way – it offers many new features that weren’t there a year ago. If you’re looking to save on storage, here are some good places to look. Storage classes: Storage offers a variety of storage classes – standard, closeline, coldline and archive, all with different costs and their own best-fit use cases. If you only use the standard class, it may be time to look at your workloads and reevaluate how often your data is being accessed. In our experience, many companies use standard class storage for archival purposes, and can reduce their costs by taking advantage of nearline or coldline class storage. And in some cases, if you’re holding onto items for cold-storage use cases like legal research, the new archival class storage can offer even more savings. Lifecycle policies: Not only can you save money by using different storage classes, but you can automate it with object lifecycle management. By configuring a lifecycle policy, you can programmatically set an object to adjust its storage class based on a set of conditions — or even delete it entirely if it’s no longer needed. For example, imagine that you and your team analyze the data within the first month of its creation; Beyond that, you only need it for regulatory purposes. In that case, simply set up a policy that adjusts your storage to coldline or archive after your item reaches 31 days. Duplication: Another common source of waste in a storage environment is duplicate data. Of course, there are times when it is necessary. For example, you may want to replicate a dataset across multiple geographies so that local teams can access it quickly. However, in our experience working with clients, a lot of duplicate data is the result of lax version control, and the resulting duplicates can be difficult and expensive to manage. Fortunately, there are several ways to prevent duplicate data, as well as tools to prevent data from being accidentally deleted. Here are some things to consider: If you’re trying to maintain flexibility with a single source of truth, it might make more sense to use a multi-field bucket instead of creating multi-field buckets in different buckets. With this feature, you will enable geo-redundancy for stored objects. This will ensure that your data is replicated asynchronously to two or more locations. It protects against regional failures in the event of natural calamities. A lot of duplicate data comes from not using the storage object versioning feature. Object versioning prevents data from being overwritten or accidentally deleted, but the duplicates it creates can indeed happen.
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