Alibaba ECS Instance Types Comparison

Alibaba ECS Instance Types Comparison

Alibaba Cloud offers a number of ECS instance types optimized to meet various needs at the enterprise level. Instance types are specialized for different purposes, and vary by virtual CPU (vCPU), disk capability, memory size, and other features, offering a number of options to match any workload.

ECS instance types at the enterprise level are available for computing on the x86-architecture or for heterogenous computing, with generous options among both. But with so much to choose from, how does one find the right ECS instance type to meet their individual needs? We drew up a comparison for a quick guide on the different options and what they offer. The chart below and written descriptions are a brief and easy reference, but remember that finding the right instance type for your workload will always depend on your needs.

General Purpose

All general purpose ECS instance types are I/O optimized, offer a CPU to memory ratio of 1:4 and come with support for for SSD and Ultra Cloud Disks.

g5

The g5 instance type is backed by a 2.5 GHz Intel Xeon Platinum 8163 processor. With an ultra high packet forwarding rate, higher computing specs and higher network performance, this type ideal for scenarios involving transfer of a large volume of packets, running enterprise-level apps, small to medium database systems, data analysis, and computing clusters and data processing that rely on memory.

sn2e

Similar to the g5, the sn2e instance is ideal for the same scenarios and has higher computing specifications and enhanced network performance. Backed by a 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) or Platinum 8163 (Skylake) processors.

hfg5 (high clock speed)

The hfg5 is centered upon stable performance. Having many features in common with the g5 and sn2e options, this type family also offers a 56 vCPU instance type for boosted performance. With a 3.1 GHz Intel Xeon Gold 6149 (Skylake) processor, this instance type is better suited for high-performance front end servers, science and engineering apps, and Massively Multiplayer Online (MMO) games and video coding.

Compute Optimized

Compute optimized ECS instance types are all I/O optimized with support for SSD Cloud Disks and Ultra Cloud Disks, but vary in their vCPU to memory ratios and ideal scenario uses. Read on to learn the nuances.

Se1ne (enhanced network performance)

The se1ne comes with higher computing specs for enhanced network performance, a vCPU ratio of 1:8, ultra high packet transfer rate for receiving or transmitting, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) or Platinum 8163 (Skylake) processors. This type is ideal for large pack transfers, high-performance and large memory databases, data analysis, mining, distributed memory cache, and enterprise-level apps with large memory requirements (Hadoop, Spark).

hfc5 (high clock speed)

Optimized for stable performances, the hfc5 type boasts 3.1 GHz Intel Xeon Gold 6149 (Skylake) processors, vCPU to memory ratio of 1:2, higher computing specs and performance, and ideal for high-performance Web front-end servers, science and engineering applications, and MMO gaming and video coding.

gn6v, gn5, gn5i, gn4, ga1 (with GPU)

The g-series ECS instance type families come are all optimized for GPU compute workloads, with varying GPU processors and compute to memory options. All are ideal for deep learning, scientific computing, high-performance computing, rendering, multi-media coding, decoding, and other GPU compute workloads.

  • gnv6: V100 GPU processors, vCPU to memory ratio of 1:4, 2.5 GHz Intel Xeon Platinum 8163 (Skylake) processors.
  • gn5: NVIDIA P100 GPU processors, no fixed ratio of vCPU to memory, high-performance local NVMe SSD disks, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors.
  • gn51: NVIDIA P4 GPU processors, vCPU to memory ratio of 1:4, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors.
  • gn4: NVIDIA M40 GPU processors, no fixed CPU to memory, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors
  • ga1: AMD S7150 GPU processors, vCPU to memory ratio of 1:2.5, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors, and high-performance local NVMe SSD disks. Also ideal for other server-end business scenarios that require powerful concurrent floating-point compute capabilities

f1, f2, f3 (with FPGA)

The f-series ECS instance type families are ideal for deep learning, genomics research, financial analysis, picture, transcoding, and computational workloads, including real-time video processing and security.

  • f1: Intel ARRIA 10 GX 1150 FPGA, vCPU to memory ratio of 1:7.5, 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors
  • f2: Xilinx Kintex UltraScale XCKU115, vCPU to memory ratio of 1:7.5, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors
  • F3: Xilinx 16nm Virtex UltraScale + VU9P, vCPU to memory ratio = 1:4, and 2.5 GHz Intel Xeon Platinum 8163 (Skylake) processors

Memory Optimized

Memory optimized ECS instance types are optimized to meet for needs for high-performance and high memory databases and come I/O optimized with support for SSD and Ultra Cloud disks.

re4

The re4 is ideal for memory-intensive applications and Big Data processing engines (Apache spark or Presto), with 2.2 GHz Intel Xeon E7 8880 v4 (Broadwell) processors, up to 2.4 GHz Turbo Boot, vCPU to memory ratio of 1:12, up to 1920.0 GiB memory, and ecs.re4.20xlarge and ecs.re4.40xlarge have been certified by SAP HANA.

se1ne, se1

The s-series instance types are good for transfers of large volumes of packets, data analysis and mining, distributed memory cache, and Hadoop, Spark, and other enterprise-level applications with large memory requirements.

  • se1ne: vCPU to memory ratio of 1:8, high packet transfer rate, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) or Platinum 8163 (Skylake) processors.
  • se1: vCPU to memory ratio of 1:8, 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors

Big Data

d1ne, d1

The d-series ECS instance types are I/O optimized with support for SSD and Ultra Cloud disks come with high computing specs and network performance. They’re best use use for Hadoop MapReduce, HDFS, Hive, HBase, etc, Spark in-memory computing, big data computing and storage analysis (i.e. internet and finance industries), Elasticsearch, logs, and so on.

  • d1ne: High-volume local SATA HDD disks with high I/O throughput and up to 35 Gbit/s of bandwidth for a single instance, vCPU to memory ratio of 1:4, 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors
  • D1: High-volume local SATA HDD disks with high I/O throughput and up to 17 Gbit/s of bandwidth for a single instance, vCPU to memory ratio of 1:4, and 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors

Intensive Compute

ic5

The ic5 instance types stands on it own with a vCPU to memory ratio of 1:1. I/O optimized with support for SSD Cloud Disks and Ultra Cloud Disks, a high packet transfer rate, and 2.5 GHz Intel Xeon Platinum 8163 (Skylake) processors. Ideal for web front-end servers, data analysis, batch compute, and MMO game front-ends.

With Local SSD

i2, i2g, i1

The i-series type family all come with high-performance local SSD disks with high IOPs, high I/O throughput, and low latency. They’re ideal for OLTP and high-performance relational databases, NoSQL databases (Cassandra and MongoDB), and search applications, such as Elasticsearch

  • i2: vCPU to memory ratio of 1:8, designed for high-performance databases, 2.5 GHz Intel Xeon Platinum 8163 (Skylake) processors
  • i2g: vCPU to memory ratio of 1:4, designed for high-performance databases, 2.5 GHz Intel Xeon Platinum 8163 (Skylake) processors
  • i1: vCPU to memory ratio of 1:4, designed for big data scenarios, 2.5 GHz Intel Xeon E5-2682 v4 (Broadwell) processors

What Alibaba ECS instance types are right for your workloads?

With a wide range of ECS instance types belonging to multiple families, how do you determine the ECS type is a good match for your workload? With plenty to choose from, it’s highly probably than one of them will meet your needs effectively, but first you need to know what those needs are. Once you have a clear and ongoing vision of your workload, usage trends, and business needs, use the guide to start looking for the ECS type that’s right for you.

Further reading:

4 Mistakes Cloud Users Make When Purchasing Amazon Reserved Instances

4 Mistakes Cloud Users Make When Purchasing Amazon Reserved Instances

Amazon Reserved Instances are a great way to save money on AWS. Whether you’re looking to save on EC2, RDS, Elasticache, Elasticsearch, or Redshift, there are options to save 30-70% compared to on-demand costs. Many customers know about the opportunity and purchase Reserved Instances, but don’t have a solid execution plan to manage them going forward, resulting in wasted spend. Here are some common pitfalls we see.

Mistake #1: Thinking that once you purchase Amazon Reserved Instances, the work is done

Your journey is just beginning! Amazon Reserved Instances are only as effective as their match to your environment. Your usage will shift to difference resource types, and services may be right sized or be revamped. Dev environments will come and go as the team starts and finishes testing new features. It’s essential to continuously monitor your environment to ensure you’re eliminating any wasted spend, as well as identifying growth in usage that opens up opportunities to add additional Reserved Instances to save money.

Mistake #2: Thinking the story ends at EC2

While EC2 is the most common use case for Amazon Reserved Instances, there are 4 other services you need to monitor as well. RDS, Elasticache, Elasticsearch, and Redshift all offer Reserved Instances in one form or another. Users often overlook the savings opportunities Reserved Instances provide for these service. Additionally, did you know that not every server type has a Reserved Instance option for it? By focusing on server types for which you have reservations, and some other simple changes you can unlock additional savings.

Mistake #3: Ignoring AWS’s Pricing Changes

AWS changes prices and you need to make sure that you’re taking full advantage of them. Whether it’s converting Reserved Instances to capture the lower price or knowing what the best savings options are when it comes time to renew your Reserved Instances – you want to make sure you’re on top of all of AWS’s pricing changes when it comes to Reserved Instances.

Mistake #4: Assuming all upfront payments generate equal savings

Amazon Reserved Instances offer partial and Full upfront payment options, which have the potential to save you more – but are you choosing the best ones? Make sure you’re putting your money to work as efficiently as possible by running through various scenarios to identify which mix of reservations is best for you. This will also vary by the service you’re buying Reserved Instances for. In some places, it might be a no brainer to put a little money upfront as your savings are greatly increased. Other scenarios might lead to only a minimal increase in savings.

An Easy Solution to Optimize Amazon Reserved Instances

The key to saving money with AWS RIs is continuously monitor your Reserved Instances Fleet and test different scenarios to identify the best mix of reservations to save you the most amount of money.

Does all this optimization sound like a lot of work? That’s because it is. Eliminate all of this work by using StratCloud, which manages the buying, selling, modifying and monitoring of all your Reserved Instances. We got tired of spreadsheets and hundreds of individual decisions a month, so we created a Reserved Instance Optimizer. It uses big data analytics to analyze millions of data points optimize Reserved Instances for maximum savings. Learn more and sign up for your free demo today.

New: Schedule Google Cloud SQL Databases with ParkMyCloud

New: Schedule Google Cloud SQL Databases with ParkMyCloud

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
  • Join the ParkMyCloud User Community on Slack – feel free to join even if you’re not yet a customer!

How to Get Started

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!

 

5 Free Google Cloud Training Resources

5 Free Google Cloud Training Resources

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.
  • An always-free option. GCP’s free tier takes the cake with this an always-free tier that gives you enough power to run a small app despite limitations on product and usage, a perfect option for learning purposes.

And for help with navigating the platform as you use it, check out GCP’s documentation for a full overview, comparisons, tutorials, and more.

2. Coursera

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.  

3. Qwiklabs

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.

4. YouTube

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.”
  • Simplilearn (164k subscribers) – one of the world’s leading certification training providers, with online training that includes Machine Learning, AWS, DevOps, Big Data, and Google Cloud Platform, among others. The course on Introduction To Google Cloud Platform Fundamentals Certification is a popular one with upwards of 99k views.
  • 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.

More Free Training Resources:

Microsoft Azure VM Types Comparison

Microsoft Azure VM Types Comparison

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

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:

DC-series

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.

Av2 Series

A-series VMs have a CPU-to-memory ratio that works best for entry level workloads, like those for development and testing. Sizing is throttled for consistent processor performance to run the instance.

Dv2-series

Dv2 VMs boast powerful CPUs – roughly 35% faster than D-series VMs – and optimized memory, great for 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.

Dv3-series

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.

B-series

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.

Dsv3-series

With premium storage and a 2.4 or 2.3 GHz Intel Xeon processor that can achieve 3.5 GHz thanks to Intel Turbo Boost Technology 2.0, the Dsv3-series is best suited for most production workloads.  

Compute Optimized

Compute optimized Azure VM types offer a high CPU-to-memory ratio. They’re suitable for medium traffic web servers, network appliances, batch processing, and application servers.

Fsv2-series

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

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

Memory optimized VM types are higher in memory as opposed to CPU, and best suited for relational database services, analytics, and larger caches.

M-Series

Enterprise applications and large databases will benefit most from the M-series for having the most memory (up to 3.8 TiB) and the highest vCPU count (up to 128) of any VM in the cloud.

Dv2-series, G-series, and the DSv2/GS

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 a 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.

Ev3-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.

Storage Optimized

For big data, SQL, and NoSQL databases, storage optimized VMs are the best type for their high disk throughput and IO.

Ls-series

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

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).

H-series

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.

 

Even if you’re not (yet) multi-cloud, you should use cloud agnostic tools

Even if you’re not (yet) multi-cloud, you should use cloud agnostic tools

There’s a simple fact for public cloud users today: you need to use cloud agnostic tools. Yes – even if you only use one public cloud. Why? This recommendation comes down to a few drivers that we see time and time again.

You won’t always use just this cloud

There is an enterprise IT trend to multi-cloud and hybrid cloud – such a prevalent trend that even if you are currently single-cloud, you should plan for the eventuality of using more than one cloud, as the multi-cloud future has arrived. Dave Bartoletti, VP and Principal Analyst at Forrester Research, who broke down multi-cloud and hybrid cloud by the numbers:

  • 62 percent of public cloud adopters are using 2+ unique cloud platforms
  • 74 per cent of enterprises describe their strategy as hybrid/multi-cloud today

In addition, standardizing on cloud agnostic tools also can alleviate costs associated with policy design, deployment, and enforcement across different cloud environments. Management and monitoring using the same service platform greatly reduces the issue of mismatched security policies and uncertainty in enforcement. Cloud agnostic tools that also operate in the context of the data center — whether in a cloud, virtualized, container, or traditional infrastructure — are a boon for organizations who need to be agile and move quickly. Being able to reuse policies and services across the entire multi-cloud spectrum reduces friction in the deployment process and offers assurances in consistency of performance and security.

How do you decide what tools to adopt?

We talk to different size enterprises using the cloud on a daily basis, and always ask if they are using cloud native tools, or if they are using third party tools that are cloud agnostic. The answer – it’s a mix to be sure, often it’s a mix between cloud-native and third-party tools within the same enterprise.

What we hear is that managing the cloud infrastructure is quite a complex job, especially when you have different clouds, technologies, and a diverse and opinionated user community to support. So a common theme with many of the third-party tools we see used tend to include freemium models, a technology someone used at a previous company, tools recommended by the cloud services provider (CSP) themselves, and open-API-driven solutions that allow for maximum automation in their cloud operations. It also serves the tools vendors well if deploying the tool includes minimum effort — in other words, SaaS tools that do not require a bunch of services and integration work. Plug and play is a must.

For context, here at ParkMyCloud support AWS, Azure, Google and Alibaba clouds, and usually talk to DevOps and IT Ops folks responsible for their cloud infrastructure. And those folks are usually after cloud cost control and governance when speaking with us. So our conversations tend to focus on the tools they use and need for cloud infrastructure management like CI/CD, monitoring, cost control, cost visibility and optimization, and user governance. For user governance and internal communication, Single-sign On and ChatOps are must have.

So we decided to compile a list of the most common clouds and tools we run across here at ParkMyCloud, in order of popularity:

  • Cloud Service Provider
    • AWS, Google Cloud, Microsoft Azure, Alibaba Cloud – and we do get requests for IBM and Oracle clouds
  • Infrastructure Monitoring (not APM)
    • Cloud Native (AWS CloudWatch, Azure Metrics, Google Stackdriver), DataDog, Nagios, SolarWinds, Microsoft, BMC, Zabbix, IBM
  • Cost Visibility and Optimization
    • CloudHealth Technologies, Cloudability, Cloudyn/Azure Cost Management, Apptio
  • CI/CD + DevOps (this is broad but these are most common names we hear that fit into this category)
    • Cloud Native, CloudBees Jenkins, Atlassian Bamboo, HashiCorp, Spinnaker, Travis CI
  • Single Sign-On (SSO)
    • ADFS, Ping, Okta, Azure AD, Centrify, One Login, Google OAuth, JumpCloud
  • ChatOps
    • Slack, Microsoft Teams, Google Hangouts
  • Cloud Cost Control
    • Cloud Native/Scripter, ParkMyCloud, GorillaStack, Skeddly, Nutanix (BotMetric)

Beat the curve with cloud agnostic tools

Our suggestion is to use cloud agnostic tools wherever possible. Our experience tells us that a majority of the enterprises lean this way anyways. The upfront cost in terms of license fee and/or set up could be more, but we think it comes down to (1) most people will end up hybrid/multi-cloud in the future, even if they aren’t now, and (2) cloud agnostic tools are more likely to meet your needs as a user, as the companies building those tools will stay laser-focused on supporting and improving said functionality across the big CSPs.

Page 1 of 3312345...102030...Last »