Updated on 2025-06-19 GMT+08:00

Compute Service Selection

Huawei Cloud provides the Elastic Cloud Server (ECS) and Cloud Container Engine (CCE) services. The following table lists the ECS types provided by Huawei Cloud to meet the requirements of diversified computing scenarios.

For details about the preceding ECS types, see ECS Types.

Table 1 ECS type

Architecture

ECS Type

Instance Family

Description

Scenario

x86

General computing-plus

c

A balance of compute, storage, and network performance, dedicated CPU, and stable performance

Applicable to most application scenarios

ac

Compared with C series, smaller network bandwidth is allocated to different CPUs and the same specifications, ensuring stable performance and lower costs.

High-performance computing

h

Compared with the C series, higher CPU frequency delivers 20% higher compute performance.

HPC/Gaming/Scientific computing

Memory-optimized

m

Compared with the C series, the memory-optimized ECSs with a CPU/memory ratio of 1:8 provide higher memory performance.

Memory-intensive and database/memory database

am

Compared with the AC series, the memory-optimized ECSs with a CPU/memory ratio of 1:8 provide higher memory performance.

Large-memory

e

Compared with the C series, the memory-optimized ECSs with a CPU/memory ratio of 1:20 provide higher memory performance.

Disk-intensive

d

Compared with the C series, the large-capacity and low-cost SATA local disks are provided.

Big data/Cached database

Ultra-high I/O

i

Compared with the C series, large-capacity NVMe local disks with high IOPS and low latency are provided.

ir

Compared with the C series, small-capacity NVMe local disks with high IOPS and low latency are provided.

General computing

s

Compared with the C series, General computing ECSs use a CPU-unbound scheduling scheme. When the host load is light, the same computing performance as the C series can be provided. The cost is lower. However, the stability of computing performance cannot be guaranteed. It is suitable for scenarios that can tolerate performance jitter.

General web/Development environment/Small database

General computing-basic

t

Burstable performance instances with low costs. The burstable duration is determined by CPU credits.

Personal use/Maintenance terminal

GPU-accelerated

g

T4 GPU for image acceleration

3D animation rendering, CAD, and more

p

V100 GPU for computing acceleration

AI deep learning and scientific computing

pi

T4 GPU for inference acceleration

Real-time inference + light-load training

AI-accelerated

ai

Ascend 310 for computing or inference acceleration

Deep learning, scientific computing, and CAE

ARM

Kunpeng general computing-plus

kc

Compared with the C series, the Kunpeng processor is used, and the price is lower.

Adapted to most Arm application scenarios

Kunpeng memory-optimized ECSs

km

Compared with the M series, the Kunpeng processor is used, which is more cost-effective.

Database/Memory database

Kunpeng Ultra-high I/O ECSs

ki

Compared with the i series, the Kunpeng processor is used, and the price is lower.

Big data/Cached database

Kunpeng AI Inference-accelerated ECSs

kai

Compared with the AI series, the Kunpeng processor is used, and the price is lower.

Deep learning, scientific computing, and CAE

The following describes the ECS selection principles:

  • Service applicability: Meet service requirements is the first principle for selection. In addition to CPU and memory, pay attention to bandwidth requirements. Generally, the larger the instance specifications of the same series, the higher the bandwidth allowed.
  • Cost-effectiveness: A cost-effective solution must be selected if service requirements can be met. For example, with the same specifications, the price of the S/AC series is lower than that of the C series. If there is no high performance requirement for O&M terminals, the T series is more cost-effective. For fluctuates services, you are advised to use multi-node cluster load sharing and AS. In this scenario, you are not advised to use high-specification instance nodes. Otherwise, performance will be wasted when the number of nodes is reduced to the minimum.
  • Reliability: Consider how to reduce the failure rate and prevent single points of failure when selecting resources. Therefore, you are advised to select the new series (with a larger number in the specifications) and deploy the resources in a balanced manner across two AZs. Resource selection optimization and cost reduction cannot compromise service reliability. The failure of a single node in a cluster network should not cause overload of other nodes.
  • Consistency: To ensure fast scale-out, fast recovery, and auto scaling based on images, hosts that carry the same type of services must have the same specifications. Do not use too many instance types or specifications in the same service system unless otherwise specified.
  • Resource satisfaction: Considering service development and scale-out requirements, you are advised to select mainstream models in mainstream AZs (for example, AZ 1 and AZ 7 in CN North-Beijing4 and AZ 1 and AZ 4 in CN East-Shanghai1) and avoid selecting old flavors.

BMSs are required in special scenarios such as AI. Generally, ECSs are used for general-purpose computing. The recommended models for typical scenarios are as follows:

Table 2 ECS selection for typical scenarios

Location

Typical Application

Selection Suggestion

Access layer

Load balancing/Application proxy

Nginx

C/M series

O&M terminal

Jump server

T series

Application layer

Common application

Web services

AC/AM series

High-performance computing service

Transcoding

C/M series

Middleware

Self-built middleware

Self-built Redis/RocketMQ

C/M series

Data layer

Self-built databases

Self-built MySQL/Oracle

C/M series