When you create a virtual machine in Microsoft Azure, you are required to assign it to an Azure Resource Group. This grouping structure may seem like just another bit of administrivia, but savvy users will utilize this structure for better governance and cost management for their infrastructure.
What are Azure Resources Groups?
Azure Resources Groups are logical collections of virtual machines, storage accounts, virtual networks, web apps, databases, and/or database servers. Typically, users will group related resources for an application, divided into groups for production and non-production — but you can subdivide further as needed.
You will manage groups through the “Azure Resource Manager”, where you can deploy and manage groups. Benefits of the Azure Resource Manager include the ability to manage your infrastructure in a visual UI rather than through scripts; tagging management; deployment templates; and simplified role-based access control.
Group structures like Azure’s exist at the other big public clouds — AWS, for example, offers optional Resource Groups, and Google Cloud “projects” define a level of grouping that falls someplace between Azure subscriptions and Azure Resource Groups.
How to Use Azure Resource Groups Effectively for Governance
Azure resource groups are a handy tool for role-based access control (RBAC). Typically, you will want to grant user access at the group level – groups make this simpler to manage and provide greater visibility.
Effective use of tagging allows you to identify resources for technical, automation, billing, and security purposes. Tags can extend beyond resource groups, which allows you to use tags to associate groups and resources that belong to the same project, application, or service. Be sure to apply tagging best practices, such as requiring a standard set of tags to be applied before a resource is deployed, to ensure you’re optimizing your resources.
Azure Resources Groups Simplify Cost Management
Azure Resource Groups also provide a ready-made structure for cost allocation — groups make it simpler to identify costs at a project level. Additionally, you can use managing to manage resource scheduling and, when they’re no longer needed, termination.
You can do this manually, or through your cost optimization platform such as ParkMyCloud. To this end, we have just released functionality that allows you to use ParkMyCloud’s policy engine to manage Azure resources at the group level. For almost all Azure users, this means automatic assignment to teams, so you can provide governed user access to ParkMyCloud. It also means you can set on/off schedules at the group level, to turn your non-production groups off when they’re not needed. Try it out and let us know what you think.
The latest release of ParkMyCloud includes the ability to schedule Google Cloud SQL Databases, among other updates to help you save more money through cloud automation.
Save with Google Cloud SQL Parking
First up, ParkMyCloud can now park Google Cloud SQL Databases! This means you can automate start/stop on a schedule, so your databases used for development, testing, and other non-production purposes are only running when you actually need them – and you only pay for the hours you need. The average schedule in ParkMyCloud is OFF 65% of the time, which means 65% savings – that’s a lot of money.
You can also use ParkMyCloud’s policy engine to create rules that automatically assign your SQL databases to parking schedules and to teams, so they’re only accessible to the users who need them.
Google Cloud SQL databases are just the latest in the growing list of types of cloud resources you can park, which also includes Google VM instances, Google Managed Instance groups, AWS EC2 instances, AWS auto scaling groups, AWS RDS instances, Azure VMs, Azure Scale Sets, and Alibaba Cloud ECS instances.
So why now? A growing number of ParkMyCloud users base their infrastructure in Google Cloud – in fact, GCP users are our fastest-growing segment of users. We’ll continue to add ways to optimize your environment no matter what clouds you use, of course, but expect more GCP features to come. We’ve focused on databases in this release because databases are the biggest area of cloud spend after compute, accounting for about 15-20% of an average enterprise’s bill.
What Else is New in ParkMyCloud?
Users will enjoy a few other recent additions to the ParkMyCloud platform:
Automatically accept SmartParking recommendations – fully automate your resource optimization by using ParkMyCloud’s policy engine to automatically apply schedules (previously, these had to be manually applied). There are several settings you can tweak to suit your needs – more in the release notes.
Chat integrations – we most recently added chat integration for Google Hangouts and MS Teams, joining our existing Slack integration. You can receive notifications and perform override commands and more through your chat window
If you’re new to ParkMyCloud, you can get started with a free trial. After the full-featured 14-day trial, you can choose to subscribe to a premium plan, or use the free tier – visit our pricing page for more information.
If you already use ParkMyCloud, you’ll need to enable ParkMyCloud to discover and manage your Google Cloud SQL databases. Find the details about the updated limited access role permissions in our user guide. Two things to note: first, you’ll need to be subscribed to the Standard or Enterprise tier in order to access this feature.
As always, we welcome your feedback about this new addition to ParkMyCloud, and any features you’d like to see in the future – comment below or shoot us a note. Cheers!
If you’re looking to break into the cloud computing space, there’s an abundance of resources out there, including free Google Cloud training. If you know where to look, open source learning is a great way to get familiar with different cloud service providers. Combined with our previous blog posts on free training resources for AWS and Azure, you’ll be well on your way to expanding your cloud expertise and finding your own niche. No matter where you are in the learning process, there’s a training resource for every experience level and learning type – get started now with our list of 5 free Google Cloud training resources:
1. Google Cloud Free Tier
For free, hands-on training there’s no better place to start than with Google Cloud Platform itself. GCP’s free tier option is a no-brainer thanks to its offerings:
Access to all GCP products. You’ll have everything you need to experiment with building and running apps, sites, and services. Firebase and the Google Maps API are included.
$300 credit is yours to spend for the next 12 months, an expansion from their previous 60-day period and a sizable offer in comparison to Azure’s $200 for 30 days, so take advantage.
No autocharges after the trial period ends – a rarity for most free trials, and a guarantee that this training resource is 100% free.
And for help with navigating the platform as you use it, check out GCP’s documentation for a full overview, comparisons, tutorials, and more.
On the Google Cloud training page, you’ll find plenty of classes to get technical skills and learn best practices for using the platform. Among those options, they have also teamed up with Coursera, an online learning platform founded by Stanford professors, to offer courses online so you can “skill up from anywhere.”
Coursera includes a number of free courses, and until 1/1/19, you can sign up and get your first month free on any select Google Cloud Specialization. Courses include topics in Machine Learning, Architecting, Data Engineering, Developing Applications, and the list goes on.
In conjunction with Coursera, Google Cloud offers hands-on training with specialized labs available via Qwiklabs, a learning lab environment for developers. Choose a “quest” from their catalog to get started with 50+ hands-on labs from beginner to expert level, where you’ll learn new skills in a GCP environment and earn cloud badges along the way. Get started with GCP Essentials and work your way into more advanced, niche topics like Managing Cloud Infrastructure with Terraform, Machine Learning APIs, IoT in Google Cloud, and so on.
You can’t go wrong with YouTube. An endless amount of free videos offers an abundance of Google Cloud training for those of you who prefer to watch the movie instead of reading the book (you know who you are). Some of the most popular YouTube channels for free Google Cloud training include:
Google Cloud Platform (243k subscribers) – “helping you build what’s next with secure infrastructure, developer tools, APIs, data analytics and machine learning.”
Edureka (537k subscribers) is a full-service, online learning platform with curated content in Big Data and Hadoop, DevOps, Blockchain, AI, Data Science, AWS, Google Cloud, and more. Their YouTube channel is a “gateway to high-quality videos, webinars, sample classes and lectures from industry practitioners and influencers.” If you’re jumping into GCP with no prior knowledge or experience, the What is Google Cloud Platform tutorial will get you started.
5. Blogs & Forums
While other resources keep you learning with hands-on training, tutorials, and certification prep, blogs keep your mind flowing with new insights, ideas, and the latest on all things cloud computing. Google Cloud and Qwiklab have blogs of their own, perfect for supplemented reading with their trainings. But for a more well-rounded blog with content on other service providers, check out Cloud Academy. We also cover Google Cloud on the ParkMyCloud blog – check out this guide to Google Cloud machine types, an explanation of sustained use discounts, and introduction to resource-based pricing. And be sure to subscribe to relevant discussion forums such as r/googlecloud on Reddit and the GCP Slack.
Take Advantage of These Free Google Cloud Training Resources
As it becomes clear that cloud computing is here to stay, free training resources only continue to emerge. We picked the 5 above for their reliability, variety, quality, and range of information. Whether you’re new to Google Cloud or consider yourself an expert, these resources will expand your knowledge and keep you up to date with what’s latest in the platform.
There has been a rush of cloud management acquisitions lately, with VMware, Apptio, and Flexera making major acquisitions in the last three months alone (and more to follow). I thought it would be useful to compile a centralized list, so we can take a look at the trends in this market and why these acquisitions are accelerating.
The Multi-Faceted Cloud Management Industry
First, let’s be clear: the cloud management industry is broad and a bit ambiguous but as it matures industry analysts have begun to define specific categories. We found the below put together by Gartner in a recent blog:
ParkMyCloud fits into the “Cost Management and Resource Optimization” category, which in and of itself is broad, but in a nutshell these vendors help enterprises monitor, manage, govern and control cloud spend in a variety of ways. The other category we find intriguing is “Provisioning and Orchestration”. That’s where we feel a lot of the DevOps tools fit, and that is the go-to-market model we like to fashion ourselves after — technical user/buyer, self-service trials, SaaS, and freemium model.
Cloud Management Acquisitions, 2013-2018
So it should be no surprise that we have collected the following data points listed below – we would welcome your feedback on others we should add to this list.
Cloud Technology Partners
In the last 45 days or so the cloud management platform (CMP) space has been hyperactive as VMware acquired CloudHealth, Apptio acquired FittedCloud, and Flexera acquired Rightscale. Good news for all but we are most excited for CloudHealth given we are a commercial and technology partner with them.
What These Cloud Management Acquisitions Tell Us about The State of Public Cloud
So what does this tell us about the cloud management space, and in particular the cost management and optimization space? We have some opinions:
Companies like Cisco, HPE and VMware understand the importance of being in the public cloud game, each basically failed at competing against AWS et. al. head on, so they are now ensuring they have tools that help enterprises manage public, private, hybrid and multi-cloud services.
The cost management portion of cloud management is always a “top 3” concern of CIOs and CTOs according to any cloud survey published, so cloud cost optimization is front in center in enterprise IT and ISVs must be able to address this concern.
Clearly, cloud management acquisitions will continue, and new solutions and companies will evolve as this market grows and matures. The cloud providers are launching new services at a rapid pace, and like any large scale utility there needs to be tools to help manage, govern, secure, and optimize these existing and new services.
Microsoft Azure VM types come in a wide range optimized to meet various needs. Machine types are specialized, and vary by virtual CPU (vCPU), disk capability, and memory size, offering a number of options to match any workload.
With so many options available, finding the right machine type for your workload becomes confusing – which is why we’ve created this overview of Azure VM types (as we did before with EC2 instance types, and Google Cloud machine types). Note that while AWS EC2 instance types have names associated with their purpose, Azure instance type names are simply in a series from A to N.The chart below and written descriptions are a brief and easy reference, but remember that finding the right machine type for your workload will always depend on your needs.
General purpose VMs are suitable for balanced CPU and memory, making them a great option for testing and development, smaller to medium databases, and web servers with lower traffic:
The latest family of virtual machines stand out for data protection and code confidentiality. SGX technology and a 3.7GHz Intel XEON E-2176G Processor back these machines, and in conjunction with Intel Turbo Boost Technology, they can go up to 4.7 GHz.
Dv2 VMs boast powerful CPUs – roughly 35% faster than D-series VMs – and optimized memory, great for production workloads. With the same memory and disk configurations as the D-series, based upon either a 2.4 GHz or 2.3 GHz processor and Intel Boost Technology, they can go to up to 3.1 GHz.
With expanded memory and adjustments for disk and network limits, the Dv3 series Azure VM type offers the most value to general purpose workloads. Best for enterprise applications, relational databases, in-memory caching, and analytics.
Similar to the AWS t-series machine type family, B-series VMs are burstable and ideal for workloads that do not rely on full and continuous CPU performance. Customers can purchase a VM size that builds up credits when underutilized, and the accumulated credits can be used as bursts – spikes in compute power that allow for higher CPU performance when needed. Use cases for B-series VM types include development and testing, low-traffic web servers, small databases, micro services, and more.
With a base core frequency of 2.7 GHz and a maximum single-core turbo frequency of 3.7 GHz, Fsv2 series VM types offer up to twice the performance boost for vector processing workloads. Not only do they offer great speed for any workload, the Fsv2 also offers the best value for its price based on the ratio of Azure Compute Unit (ACU) per vCPU.
F-series Azure VM types are great for workloads that require speed thanks to the 2.4 GHz Intel Xeon processor, reaching speeds up to 3.1 GHz with the Intel Turbo Boost Technology 2.0. The F-series is your best bet for fast CPUs but not so much when it comes to memory or temporary storage per vCPU. Analytics, gaming servers, web servers, and batch processing would work well with the F-series.
Memory optimized VM types are higher in memory as opposed to CPU, and best suited for relational database services, analytics, and larger caches.
For applications that require fast vCPUs, reliable temporary storage, and demand more memory, the Dv2, G, and DSv2/GS series all fit the bill for enterprise applications. The Dv2 series offers speed and power with a CPU about 34% faster than that of the D-series. Based on the 2.3 and 2.4 GHz Intel Xeon® processors and with Intel Turbo Boost Technology 2.0, they can reach up to 3.1 GHz. The Dv2-series also has the same memory and disk configurations as the D-series.
The Ev3 follows in the footsteps of the high memory VM sizes originating from the D/Dv2 families. This Azure VM types provides excellent value for general purpose workloads, boasting expanded memory (from 7 GiB/vCPU to 8 GiB/vCPU) with adjustments to disk and network limits per core basis in alignment with the move to hyperthreading.
For big data, SQL, and NoSQL databases, storage optimized VMs are the best type for their high disk throughput and IO.
VMs provide as much as 32 vCPUs with the Intel® Xeon® processor E5 v3 family. The Ls-series comes with the same CPU performance as the G/GS-Series and 8 GiB of memory per vCPU. This type works best applications requiring low latency, high throughput, and large local disk storage.
GPU VM types, specialized with single or multiple NVIDIA GPUs, work best for video editing and heavy graphics rendering – as in compute-intensive, graphics-intensive, and visualization workloads.
NC, NCv2, NCv3, and ND sizes are optimized for compute-intensive and network-intensive applications and algorithms.
NV and NVv2 sizes were made and optimized for remote visualization, streaming, gaming, encoding, and VDI scenarios.]
High Performance Compute
For the fastest and most powerful virtual machines, high performance compute is the best choice with optional high-throughput network interfaces (RDMA).
For the latest in high performance computing, the H-series Azure VM was built for handling batch workloads, analytics, molecular modeling, and fluid dynamics. These 8 and 16 vCPU VMs are built on the Intel Haswell E5-2667 V3 processor technology featuring DDR4 memory and SSD-based temporary storage.
And besides sizable CPU power, the H-series provides options for low latency RDMA networking with FDR InfiniBand and different memory configurations for supporting memory intensive compute requirements.
What Azure VM type is right for you?
With six virtual machine types belonging to multiple families and coming in a range of sizes, how do you determine the right Azure VM type for your workload? The good news is that with this many options, you’re bound to find the right type to meet your computing needs – as long as you know what those needs are. With good insight into your workload, usage trends, and business needs, you’ll be able to find the Azure VM type that’s right for you.