To select an instance type, you must know the key features of each instance type. Knowing the features of each instance type allows you to select a variety of instance types to manage scenarios such as insufficient resources, instance families phase-out, and the use of preemptible instances and take full advantages of the elasticity and flexibility of ECS.
This topic describes how to select enterprise-level instance families, instead of entry-level instance families (also called shared instance families). For more information about the selection of entry-level instance families, see Overview.
Learn instance families
When you create an ECS instance, you want to select the most cost-effective and stable
instance type to suit your performance, prices, and workload needs. Alibaba Cloud
provides a variety of instance families suited for different business scenarios with
different specifications of vCPUs, memory, network performance, and throughput. An
instance family is further divided into multiple instance types. Instance family names
are in the ecs. <Instance family> format, and instance type names are in the ecs. <Instance family>.<nx>large format.
- ecs: the product code of ECS.
- <Instance family>: consists of lowercase letters and digits.
- Lowercase letters are abbreviations of the performance field of the instance family.
Some of the abbreviations are described as follows:
- c: compute optimized (computational)
- g: general purpose (general)
- r: memory optimized (ram)
- ne: enhanced network performance (network enhanced)
- Digits identify the release dates of instance types within an instance family. A larger digit represents a newer generation of the instance family and is cheaper, more effective, and has higher performance.
- Lowercase letters are abbreviations of the performance field of the instance family.
Some of the abbreviations are described as follows:
- <nx>large: the number of vCPUs of the instance type. The larger the digit n is, the more vCPUs the instance type has.
Select instance types
You can use one of the following methods to view details about instance families and
types to select an appropriate instance type:
- Instance families: See this topic to learn details about instance families without logging on with an Alibaba Cloud account.
- DescribeInstanceTypes: Call this API operation to obtain the latest performance specifications
of instance types. You must log on with an Alibaba Cloud account to perform this operation.
aliyun ecs DescribeInstanceTypes --InstanceTypeFamily ecs.g6 - Pricing: View the pricing information, latest special offers, and the estimated cost on the pricing page.
- Custom Launch: Learn more about instance purchase instructions in the Instance Type section of the Basic Configurations step.
Select instance families based on scenarios
The following figure shows certain ECS instance families and the business scenarios
for which they are suited.
Select instance families based on applications
If you are using software or applications similar to those in the following figure,
select corresponding instance families from the right side of the picture.
Select instance families based on user-created services
If you are using user-created services, select corresponding instance families based on your services and the selection principles.
| Application type | Common application | Selection principle | Recommended instance family |
|---|---|---|---|
| Load balancing | NGINX | Connections can be frequently established.
|
Instance families: c6 and hfc6 |
| RPC |
|
A huge amount of memory is available for network connection-intensive workloads. | Instance families: g6 |
| Cache |
|
|
|
| Configuration center | ZooKeeper | A large number of I/O operations generated when applications initiate negotiations
can be handled.
|
|
| Message queues |
|
Disks are preferred for the purpose of message integrity.
|
|
| Container orchestration | Kubernetes | The X-Dragon architecture and containers are combined to maximize computing power. | Instance families: ebmc6 and ebmg6 |
| Large table storage | HBase |
|
|
| Database |
|
|
|
| SQLServer |
|
|
|
| Text search | Elasticsearch |
|
|
| Real-time computing |
|
You can select general purpose ECS instances attached with disks or select d series instances. | d1, d1ne, and d2s |
| Offline computing |
|
d series instance families are preferred. | d1, d1ne, and d2s |
Recommended instance families for scenarios such as common scenarios, game servers, and live streaming
These scenarios are CPU-compute intensive. We recommend that you select an instance
type with a relatively balanced CPU-to-memory ratio, typically 1:2, use an ultra disk
as the system disk, and use standard SSDs or enhanced SSDs as data disks. For scenarios
that require higher network performance such as on-screen video comments, you can
select an instance type with higher specifications to improve the packet forwarding
rate.
| Scenario category | Specific scenario | Recommended instance family | Performance requirement | CPU-to-memory ratio |
|---|---|---|---|---|
| Common scenarios | Balanced performance applications and backend applications | g series instance families, such as g6 | Medium clock speed, compute-intensive | 1:4 |
| Applications with high packet forwarding rates | g series instance families, such as g6 | High packet forwarding rates, compute-intensive | 1:4 | |
| High-performance computing | c series instance families, such as c6 | High clock speed, compute-intensive | 1:2 | |
| Game applications | High-performance client games | c series instance families, such as c6 | High clock speed | 1:2 |
| Mobile or web games | g series instance families, such as g6 | Medium clock speed | 1:4 | |
| Live streaming applications | Video forwarding | g series instance families, such as g6 | Medium clock speed, compute-intensive | 1:4 |
| On-screen video comments | g series instance families, such as g6 | High packet forwarding rates, compute-intensive | 1:4 |
Recommended instance families for big data scenarios such as Hadoop, Spark, and Kafka
These scenarios have different performance requirements for each node. You must balance
the performance of each node, including computing performance, storage throughput,
and network performance.
- Management nodes: Select an instance family in the same manner as you would in common scenarios. For more information, see the Recommended instance families for scenarios such as common scenarios, game servers, and live streaming section.
- Compute nodes: Select an instance family in the same manner as you would in common
scenarios. For more information, see the Recommended instance families for scenarios such as common scenarios, game servers, and live streaming section. You must select instance types based on the cluster size. For example, you
can select ecs.g6.4xlarge for clusters with less than 100 nodes and ecs.g6.8xlarge
for clusters with more than 100 nodes.
Note Compute nodes can be billed on preemptible instances to improve cost-effectiveness. For more information, see Overview.
- Data nodes: require high storage throughput, high network throughput, and balanced CPU-to-memory ratios. We recommend that you use the d series big data instance family. For example, you can select ecs.d1ne.6xlarge for MapReduce and Hive, or ecs.d1ne.8xlarge for Spark and MLib.
Recommended instance families for scenarios such as databases, caches, and search scenarios
Typically, these scenarios require that the CPU-to-memory ratio is greater than 1:4.
Some software in these scenarios is sensitive to latency and storage I/O capabilities.
We recommend that you select instance families whose unit price of memory is more
reasonable.
| Scenario category | Specific scenario | Recommended instance family | CPU-to-memory ratio | Data disk |
|---|---|---|---|---|
| Relational databases | High performance and dependent on high availability in the application layer | i series instance families | 1:4 | Local SSDs, ultra disks, and standard SSDs |
| Small and medium-sized databases | g series instance families, or other instance families with CPU-to-memory ratios of 1:4 | 1:4 | Ultra disks and standard SSDs | |
| High-performance databases | r series instance families | 1:8 | Ultra disks and standard SSDs | |
| Distributed caches | Medium memory usage scenarios | g series instance families, or other instance families with CPU-to-memory ratios of 1:4 | 1:4 | Ultra disks and standard SSDs |
| High memory usage scenarios | r series instance families | 1:8 | Ultra disks and standard SSDs | |
| NoSQL databases | High performance and high availability in the application layer | i series instance families | 1:4 | Local SSDs, ultra disks, and standard SSDs |
| Small and medium-sized databases | g series instance families, or other instance families with CPU-to-memory ratios of 1:4 | 1:4 | Ultra disks and standard SSDs | |
| High-performance databases | r series instance families | 1:8 | Ultra disks and standard SSDs | |
| ElasticSearch | Small clusters that depend on disks to ensure high data availability | g series instance families, or other instance families with CPU-to-memory ratios of 1:4 | 1:4 | Ultra disks and standard SSDs |
| Large clusters and dependent on high availability in the application layer | d series instance families | 1:4 | Local SSDs, ultra disks, and standard SSDs |
Take databases as an example. Traditionally, business systems directly connect to
online transaction processing (OLTP) databases and data redundancy is mainly achieved
by RAID. However, if you use ECS provided by Alibaba Cloud, you can flexibly deploy
both lightly and heavily loaded databases.
- Lightly loaded databases: use enterprise-level instance families with disks, which are more cost-effective.
- Heavily load databases: require high storage IOPS and low read/write latency. We recommend that you use i series instance families with local SSDs. The local SSDs are high I/O NVMe SSDs that can meet the requirements of large heavily-loaded databases.
Recommended instance families for scenarios such as deep learning and image processing
In these scenarios, applications require high-performance GPU accelerators. The following section describes recommendations for the GPU-to-CPU ratio:
- Deep learning training: The recommended GPU-to-CPU ratio is 1:8 to 1:12.
- General-purpose deep learning: The recommended GPU-to-CPU ratio is 1:4 to 1:48.
- Image recognition and reasoning: The recommended GPU-to-CPU ratio is 1:4 to 1:12.
- Speech recognition and synthesis reasoning: The recommended GPU-to-CPU ratio is 1:16 to 1:48.
Check and adjust your selection
After you select an instance type and start using the instance, we recommend that you check whether the instance type is suitable based on the performance monitoring information.
If you select the ecs.g6.xlarge instance type and find that the CPU utilization is
low, we recommend that you log on to the instance to check whether the memory usage
is high. If the memory usage is high, you can change to another instance family with a more suitable CPU-to-memory ratio. For more
information, see the following topics:
For scenarios such as insufficient resources, instance families phase-out, change of the current instance family to a more cost-effective one, you can upgrade or downgrade the instance configurations based on the instance family features. The following table lists some recommendations for instance configuration changes. For more information, see Overview of instance upgrade and downgrade and Instance type changes.
| Current instance families | Preferred target instance family | Alternative target instance family |
|---|---|---|
| sn1 and sn2 |
|
|
| c4 | hfc6 and c6 | hfc5 and c5 |
| ce4 | r6 | r5 and se1ne |
| cm4 | hfc6 | hfc5 and g5 |
| n1, n2, and e3 |
|
|
|
|
|
References
For more information about application scenarios, see Selection of enterprise-level instance types.