Single service comparison

Amazon EC2 exposes the EC2 API (RunInstances, DescribeInstances, etc.) with AMIs as the image unit, EBS for block storage, and ENIs/EIPs for networking; identities are governed by AWS IAM and per-instance profiles. Huawei ECS is the analog VM service with its own ECS/OpenStack-flavored API, private/public images via IMS, EVS for block disks, and VPC/EIP networking, governed by IAM. The control-plane model, instance naming (e.g., t3/c6 vs. Huawei c9/kc2/m7n flavors), and quota/metering semantics differ, so tooling tied to AWS APIs will not work unchanged.

For scaling and HA, EC2 leans on Auto Scaling groups with launch templates, placement groups, and capacity reservations across multiple AZs, with CloudWatch driving alarms. Huawei pairs ECS with Auto Scaling (AS) for ECS AS groups and scaling policies, plus ECS groups, anti-affinity, and multi-AZ deployment for redundancy. Instance-weighting, predictive scaling, and warm pool capabilities differ, and Huawei metrics come from Cloud Eye (CES) rather than CloudWatch; verify feature-by-feature because managed integrations (load balancers, alarms, lifecycle hooks) are not API-identical.

Operational responsibility is comparable in spirit but differs in detail: with EC2 you patch the guest OS, manage EBS snapshots/AMIs, capacity and AZ placement, and operate within the AWS shared-responsibility split. With Huawei ECS you similarly own the guest but use Huawei tools (IMS for private images, CBR for backups, Cloud Eye for monitoring, TMS/organization policies for governance) within Huawei's shared-responsibility and compliance boundaries. VPC/subnet/security-group/EIP constructs and IAM policy models are conceptually parallel but not drop-in compatible, so refactor rather than translate.

Start with assessment: inventory EC2 instances, AMIs, EBS volumes, ENIs, security groups, IAM instance profiles, Auto Scaling groups, and CloudWatch alarms, and capture OS, kernel, drivers, application bindings, and licensing. Select the target ECS flavor family by matching vCPU:memory ratio and accelerator needs (general-purpose C-series, memory-optimized M-series, ultra-high-I/O I-series with local SSDs, or Kunpeng/Arm kC2 for Arm workloads), and confirm regional/AZ availability and quotas for each target region before planning cutover.

For migration execution, Huawei's Server Migration Service (SMS) performs block-level, agent-based migration of the system disk and data disks from x86 source VMs (including VMs on other public clouds) to ECS, with incremental synchronization to minimize the cutover window; orchestrate via Migration Center (MgC) for batch cloud-to-cloud migration. Recreate AMIs as IMS private images if you prefer image-based provisioning, and rebuild launch templates/Auto Scaling groups as ECS AS configurations, IAM instance profiles as Huawei IAM, and security groups as VPC security groups.

Validate in a staging VPC before cutover: confirm instance parity on boot, application functionality, performance benchmarks, network and security posture, and observability via Cloud Eye and logs. Run SMS incremental syncs to catch delta changes, then schedule a maintenance window to stop source workloads, perform a final sync, update DNS/load-balancer entries, and cut traffic over. Keep the AWS environment running as a rollback until health checks and business validation pass, then decommission to avoid dual billing.

Mind the gaps and the cost model. AWS charges per-second instance hours, EBS GB, and data transfer; Huawei bills ECS runtime (per-second or package/year), EVS capacity and IOPS tiers, EIP/bandwidth, and optional add-ons, frequently with reserved or packaged discounts. Feature parity is not guaranteed for predictive scaling, warm pools, Capacity Providers, Spot blocks, or certain AMI/backup semantics; rebuild those patterns with Huawei equivalents. Recompute TCO including peak load, traffic, retention, and cross-region interconnect, and engage Huawei migration services for unsupported edge cases.