Single service comparison

AWS Auto Scaling is a cross-service scaling controller spanning EC2, Spot Fleets, DynamoDB, EMR, Aurora replicas, and ECS/EKS capacity providers, each with its own scaling plan API. Huawei AS is narrower in scope: it scales ECS instances and EIP bandwidth within an AS group, and does not offer a single unified scaling plan service for managed databases, container clusters, or big data. Container autoscaling on Huawei is handled by CCE's cluster scaler or HPA, not AS, so target selection depends on workload type.

Both services support alarm-based (CES-driven), scheduled, and periodic policies, plus health checks and graceful scale-in via lifecycle hooks. AWS exposes target tracking, predictive scaling, instance warmup, and step scaling with cooldowns at the policy level; Huawei AS uses default cooldowns and alarm/scheduled/periodic actions but lacks predictive scaling. Matrix-style mixed-instance policies and capacity-optimized spot allocation in AWS have no direct AS equivalent—Huawei relies on AS configurations referencing one or more ECS flavors, with multi-AZ spread limited to the enabled zones of a single region.

Operational integration differs. AWS Auto Scaling natively couples with CloudWatch, Elastic Load Balancing, IAM, Launch Templates, and Capacity Reservations; Huawei AS integrates with CES for metrics, ELB for traffic distribution, and uses AS configurations (image, flavor, key pair, user data) rather than launch templates. Lifecycle hooks and notifications exist on both sides, but cross-region scaling, warm pools, and stand-by states are AWS features without direct Huawei AS parity. Confirm per-feature gaps before assuming behavioral equivalence at cutover.

Start with assessment: inventory every AWS Auto Scaling target type (EC2, Spot Fleet, ECS service, Aurora, DynamoDB, EMR) because Huawei AS only covers ECS and EIP bandwidth. Map container workloads to CCE with its own autoscaler, managed databases to RDS/GaussDB read replicas, and big data to MRS—do not treat AS as a universal replacement. Re-derive scaling triggers from the CloudWatch metrics actually driving scale-out/in and identify the matching CES metrics Huawei AS can consume.

For target ECS, rebuild launch configuration as an AS configuration: select a Huawei ECS image mirroring your AMI OS, equivalent flavor (vCPU/memory/network), the same key pair or Keystone-equivalent credentials, security groups, user-data bootstrap, and EVS disk layout. Recreate scaling policies as alarm (CES), scheduled, and periodic policies; translate target-tracking into CES alarm thresholds manually since Huawei AS lacks predictive and target-tracking policies. Reattach an ELB listener to the AS group to preserve load-balanced health checks and traffic distribution.

Validate before cutover: run load simulations that exercise scale-out, scale-in, instance replacement, and lifecycle hooks, confirming cooldown and notification behavior parity. Verify graceful scale-in drains in-flight work, and that health checks replace instances on the same failure signals as in AWS. Check regional and AZ coverage, instance quota headroom (ECS instance limits), and any spot/burst capacity expectations, since Huawei AS does not offer capacity-optimized or mixed-purchase strategies equivalent to AWS.

Mind the cost and operational model shift. AWS charges per-second instance hours with optional Savings Plans and Spot; Huawei bills ECS by hour/second plus EVS, EIP bandwidth or traffic, and AS itself is not separately billed but drives its underlying resources. Recompute TCO against peak load, reserved or packaged ECS pricing, cross-region traffic, and the loss of predictive scaling—potentially oversizing min capacity to absorb demand spikes AWS predictive scaling handled. Document remaining gaps (warm pools, cross-region scaling, predictive scaling) and the manual policy translations required for production parity.