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Before moving a production workload to a different CPU architecture, prove three things on the actual target platform: the software is compatible, the service remains correct under representative load, and the resulting capacity and operating cost meet your objectives. Start with a complete inventory and a repeatable baseline; then audit code, binaries, images, libraries, drivers and vendor components for architecture assumptions. A successful compile is only an early checkpoint.
Use a target-architecture build and test path, benchmark source and target under equivalent conditions, and roll out through a small, observable slice while retaining the original capacity for rollback. The process applies to any architecture change; x86-to-Arm examples below illustrate the mechanics without making Arm the universal choice.
1. Define the decision and capture a baseline
Write down the exact workload being considered rather than treating an entire application estate as one migration unit. A stateless API, a batch processor, a database with native extensions and a service attached to a device can have very different portability risks.
Name the objective and acceptance criteria
State why the architecture change is being considered: additional capacity, lower latency, reduced power draw, improved availability in a region, a supported accelerator, or another measurable objective. Convert that reason into thresholds, such as a p99 latency limit, minimum throughput, maximum error rate, batch completion window, or cost per transaction. Without thresholds, a faster benchmark can still be an operationally worse move.
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Record the source platform
Save a reproducible description of the current deployment before changing anything. Include:
- CPU architecture, processor family, instruction-set extensions and virtualisation mode.
- Operating-system distribution and release, kernel version, libc and language runtime versions.
- Deployment model: virtual machines, bare metal, containers, orchestration platform, serverless runtime or a mixture.
- Memory size and limits, storage type and throughput, network bandwidth and latency, and autoscaling limits.
- GPU, FPGA, SmartNIC, cryptographic device, storage controller or other accelerator dependencies.
- Compiler, linker, build system, optimisation flags and reproducible-build settings.
- Application binaries, shared libraries, plugins, agents, monitoring and security sensors.
- Third-party and proprietary components, including their tested operating systems and architectures.
Arm’s porting methodology treats this inventory as the starting point for migration; the same discipline is useful for RISC-V, Power, x86 variants or any other target.
Make the baseline repeatable
Capture representative requests, datasets, batch inputs, concurrency, configuration, autoscaling behaviour and failure scenarios. Record throughput, latency percentiles, CPU time, memory high-water marks, I/O, network traffic, errors and saturation points. Keep the software versions and infrastructure shape with the results so a later comparison changes one variable at a time.
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Compatibility is a chain. An application can be portable while a logging agent, database driver, image layer or vendor plugin is not. Check support matrices and tested combinations for every layer at the versions you actually deploy.
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Verify operating-system and runtime support
Confirm that the target architecture is supported by the selected OS release, kernel features, libc, language runtime, package manager, JIT, garbage collector and debugger. Check drivers for storage, networking, GPUs and security modules. Verify that observability, backup, endpoint-security and policy agents publish target binaries; an unsupported agent can block production even when the application itself works.
Search source and build logic
Use the code-review process and automated scanning to find assumptions that a normal unit test may miss. Arm’s code-assessment guidance highlights common trouble spots:
- x86 SIMD intrinsics or assumptions about a particular instruction-set extension.
- Inline assembly, compiler built-ins and handwritten context-switch or atomic-operation code.
- Platform-specific system calls, ioctl interfaces, CPU feature probes and `/proc` parsing.
- Compiler, linker and assembler flags that select x86 instructions or a fixed microarchitecture.
- Build scripts that branch on a hard-coded architecture name or download a prebuilt tool.
- Tests that assert word size, byte order, alignment or undocumented timing behaviour.
Replace an architecture-specific path with a portable implementation where practical, or isolate it behind a feature-detected interface with a tested implementation for the target. Do not remove an optimisation merely because it is unfamiliar: measure the portable replacement and preserve a specialised path when it is justified.
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List every native dependency, not just direct application packages. Check whether package repositories provide the target architecture for the exact release and whether downloaded command-line tools, migration utilities, certificates, fonts and plugins are compiled for it. Inspect base images and multi-architecture manifests; an image tag can exist while publishing only the source architecture. Confirm that the registry, admission controls and cluster runtime select the correct variant on each node.
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For applications built on Java, Go, Python or Node.js, Arm notes that many modern workloads using portable runtimes require little source change. Treat that as a screening observation, not a guarantee: native extensions, image layers and vendor agents still need explicit verification. Arm’s cloud migration guidance lists missing architecture-specific dependencies, unoptimised containers and late performance validation as common pitfalls; see the migration FAQ and resources.
Check data and protocol behaviour
Run compatibility tests for serialization, compression, cryptography, regular expressions, time handling and file formats. Confirm that client and server libraries agree on ABI and alignment rules. Validate backup restoration and replication, because a workload may pass functional tests yet fail when restoring data produced on the source architecture.
3. Build, deploy and test on the actual target
Static inspection and emulation are useful for finding candidates, but they cannot prove production readiness. Add a native target build to continuous integration whenever possible and exercise the same installation and deployment path used in production.
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- Provision representative target capacity. Use the intended OS image, kernel, firmware, virtualisation settings, accelerators and storage/network classes. Document any difference from the planned production shape.
- Build with target-aware tooling. Produce native binaries or a verified multi-architecture image. Record compiler, linker and dependency versions, and fail the build when a required package lacks a target variant.
- Install and start from a clean environment. Test package installation, certificate and secret handling, service discovery, migrations, startup probes, health checks and shutdown. A container that builds can still fail during entrypoint scripts or dynamic linking.
- Run functional tests. Check business results, data integrity, idempotency, retries, timeouts, authentication and authorisation. Compare outputs with the source platform using the same fixtures.
- Run integration and failure tests. Exercise databases, queues, object storage, external APIs, backups, rolling restarts, node loss, network partitions and resource exhaustion.
- Exercise hardware paths. If the service uses an accelerator, cryptographic module, storage feature or high-speed network device, test the real device and driver on the target. A software-only test is insufficient.
- Collect diagnostics. Save system reports, logs, core dumps, performance counters and dependency manifests. Profile unexpected CPU, lock, memory, I/O or scheduler behaviour before changing compiler flags.
The validation guidance in Arm’s migration material calls for functional correctness, platform compatibility, hardware interaction and performance comparison. Keep those as separate gates so a passing unit-test suite cannot conceal an unsupported driver or a severe tail-latency regression.
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4. Benchmark the workload, not the CPU label
Run source and target with comparable software versions, datasets, workload mix, concurrency, configuration and service objectives. Include steady state and the peak or burst conditions that influence capacity planning. A processor name or a synthetic score cannot predict the result for your code, memory footprint and I/O pattern.
Use metrics tied to the service
| Question | Measures to collect | Why it matters |
|---|---|---|
| Does it meet user demand? | Throughput, p50/p95/p99 latency, queue depth and batch completion time | Shows whether objectives hold at normal and stressed load. |
| What is the resource profile? | CPU utilisation and throttling, instructions or cycles where available, memory usage, cache or stall indicators, disk and network I/O | Identifies the bottleneck and the capacity needed per replica or host. |
| Is it reliable? | Error rate, timeout rate, restart frequency, dropped work and recovery time | Prevents a nominally fast platform from failing operationally. |
| What does it cost? | Measured capacity per instance, instance or host cost, storage/network charges, licensing and engineering overhead | Converts benchmark results into the economics of the selected deployment. |
Design an apples-to-apples run
- Pin the application version, dependency versions, dataset and configuration for both platforms.
- Warm caches and runtimes consistently, then run enough repetitions to expose variance.
- Measure at identical concurrency and arrival patterns; include autoscaling behaviour if it is part of production.
- Separate cold-start, steady-state and burst results instead of averaging unlike phases.
- Profile both systems when results differ. Look for compiler-generated code, garbage collection, lock contention, memory bandwidth, storage latency and network effects.
- Repeat after any meaningful compiler, library, kernel or configuration change, and retain the old result for comparison.
The reviewed migration resources support target-platform testing, system reporting and profiling, but they do not establish a universal x86-versus-Arm score or workload-independent saving. Treat every measured result as specific to your workload and environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Evaluate capacity and economics for the selected environment
Translate measured performance into the deployment you would actually operate. If the target processes 1.2 times the work per instance but needs twice the memory, the host count, instance family or licensing model may change. Include standby capacity, multi-zone or multi-region requirements, storage and network charges, observability, support contracts and the engineering effort to maintain another build target.
Cloud-hosted Arm instances from AWS, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure can be candidates for testing, but family names, regions, accelerator availability and prices change. Verify current provider documentation and pricing for the exact account, region and operating system; no provider or price is universally correct for an unspecified workload.
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Arm’s migration page carries a vendor-hosted case-study summary that says Vociply AI reduced inference costs by 35% by moving LLM workloads to Arm-based AWS Graviton. That is one customer’s reported outcome on a particular workload, not an independently verified or general promise. Use your own measured capacity and current rates for a business case.
6. Plan a controlled rollout and a real rollback
Keep the source architecture available until the target has met technical and service-level acceptance criteria over an agreed observation period. A mixed-architecture cluster is often safer than a single cutover, provided placement and image selection are explicit.
Prepare the cluster
- Confirm that the cluster release, control plane, CNI, storage drivers, admission policies and observability stack support both architectures.
- Publish image variants under a manifest list or separate, unambiguous tags, and verify that each node pulls the intended digest.
- Use node labels and affinity, taints and tolerations, topology constraints or an equivalent mechanism to opt workloads into target nodes.
- Ensure autoscalers, disruption budgets and capacity reservations understand the two node pools.
Move traffic in stages
- Deploy an isolated test workload to target nodes and verify startup, dependencies, metrics and logs.
- Send a small canary or internal traffic slice to the target while comparing the baseline metrics.
- Expand gradually by service, tenant, partition or percentage of traffic. Define stop conditions before increasing the slice.
- Monitor latency tails, errors, saturation, restarts, data correctness, cost rate and downstream impact.
- Retain source replicas and image versions until the rollback window expires. Roll back by routing traffic and scheduling new replicas to the source pool, not by rebuilding under pressure.
The concrete OpenShift migration example uses mixed x86 and Arm nodes, scheduling controls and target-node tests before source workers are removed. It refers to OpenShift 4.18 on AWS and uses native builds rather than cross-architecture builds; those details are specific to that product and version, so do not generalise them to every cluster.
7. Diagnose common migration failures
| Symptom | Likely cause | First corrective action |
|---|---|---|
| Build fails while resolving a package | No target package, incompatible compiler flag or architecture-gated build script | Check the package’s exact version and target support; remove or replace the gated dependency and add a target CI job. |
| Image starts on one node type but not the other | Single-architecture image, wrong manifest digest or native plugin mismatch | Inspect the manifest and image layers, publish a target variant and pin the verified digest. |
| Startup succeeds but requests fail | Inline assembly, system-call assumption, ABI mismatch or missing driver | Capture logs and a trace, isolate the failing code path and run the integration test with the real target device or driver. |
| Correctness passes but latency or throughput regresses | Different compiler code generation, memory behaviour, synchronisation or I/O bottleneck | Profile under representative load before tuning; compare hot functions, stalls, garbage collection and queueing. |
| Costs rise despite acceptable speed | Memory, licensing, network, standby or engineering costs were omitted | Recalculate total cost from measured capacity and all recurring and migration expenses. |
| Rollback cannot restore service | Source capacity was released or source images and data paths changed | Keep source capacity, immutable images and compatible data procedures until the rollback criterion is cleared. |
8. Compare architectures with a decision matrix
When more than one target is plausible, score each option against the same evidence rather than choosing from processor branding. Require documentation or a passing test for each row.
| Comparison axis | Evidence to collect |
|---|---|
| Software and vendor support | Support matrices, tested OS/runtime/library versions, driver status and vendor response commitments. |
| Correctness and operations | Functional, integration, recovery, observability, security and deployment results on target hardware. |
| Performance and capacity | Throughput, tail latency, completion time, resource profile and headroom at realistic load. |
| Memory, I/O, accelerator and power behaviour | Measurements relevant to the service’s bottleneck and physical constraints. |
| Total operating cost | Current infrastructure rates, licensing, support, data transfer, resilience capacity and measured instance count. |
| Migration and rollback effort | CI changes, image maintenance, dual-architecture operations, monitoring, staff skills and time to revert. |
9. A practical go/no-go review
Approve a broader rollout only when the target build is reproducible, every required dependency has a supported target version, functional and hardware tests pass, and the apples-to-apples benchmark meets the predeclared service thresholds. The operating model must also have explicit placement, monitoring and rollback controls, with source capacity retained for the agreed safety period. If any of those conditions is unresolved, keep the workload in a controlled experiment and close the specific evidence gap rather than inferring readiness from a successful compilation or a vendor case study.
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