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 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 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 its 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 probable that 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:

How to Automate and Secure Your Environment with AWS Server Fleet Management

How to Automate and Secure Your Environment with AWS Server Fleet Management

AWS recently announced a combination of AWS Systems Manager and Amazon Inspector into a new offering called AWS Server Fleet Management. The goal of this service is to provide a way to secure, automate, and configure a large array of servers through multiple AWS services all working together. Some enterprises already have a config management tool in place, but might be looking for a more AWS-centric way to manage their numerous EC2 servers. Let’s look at how Server Fleet Management works, how it stacks up against other config management tools, and some of the pros and cons of using this solution.

How It Works

AWS Server Fleet Management utilizes quite a few AWS services under the hood.  The good news is that you don’t have to deploy these services manually, as there’s a Cloudformation template available that will build the entire stack for you. The services include:

  • Amazon Cloudwatch – for kicking off events to trigger other services
  • Amazon Inspector – manages the assessment rules for configuration and security
  • Amazon SNS – message queue for tracking instance IDs and email addresses
  • Amazon Lambda – various tasks, including querying Inspector and updating Systems Manager
  • AWS Systems Manager – tracks inventory and configuration for EC2 instances and manages OS patches
  • Amazon S3 – secure storage of artifacts

Before deploying the Cloudformation stack, you’ll need to enter a few configuration details. The main configuration detail is the “Managed Instances Tag Value”, which is the tag on your EC2 servers that you’ll place if you want them managed via Server Fleet Management. This can work in conjunction with the “Patch Group” tag in AWS Systems Manager if you want the instance to be automatically patched. Once you specify the tag, an email address, and whether you want a sample fleet to be deployed, you’re ready to create the stack!

Comparison to other tools

In the config management world, there are a few major players, including Chef, Puppet, Ansible, and SaltStack. From a purely configuration perspective, Server Fleet Management doesn’t offer anything new. However, if you’re fully bought-in to running everything within AWS, the flexibility of using Lambda functions in addition to other AWS services can be a huge advantage. On the flip side of that, enterprises that are multi-cloud may want to keep using a cloud-agnostic tool.

Pros and Cons

Along with the possible benefit of being purely within the AWS ecosystem, another major pro of AWS Server Fleet Management is the combination of security enforcement and patch management. Solving both of those problems often requires multiple tools, so this can trim down your list of applications. This solution also has lots of opportunities to tie into other existing AWS solutions or to be customized to fit your use cases.

The expandability can also be considered a con, as the built-in uses are fairly specific and require more customization for larger fleets. Some things that aren’t included are topics like cost management (we’ve got you covered), non-EC2 services that need security audits, application grouping, and cross-account access. There also aren’t any built-in hooks to existing config management tools that are likely already in use.

Automated Security and Patching

All in all, AWS Server Fleet Management is worth looking into if you’ve got a large EC2 deployment. Even if you don’t use the pre-made stack, it might give you some ideas on how to use the underlying AWS services to help secure and manage your fleet. With the included sample fleet, it’s easy to get it set up and try it out!

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 different 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 services. 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 to 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 to optimize Reserved Instances for maximum savings. Learn more and sign up for your free demo today.

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