Effect of Graviton instances on cloud performance under heavy load

When AWS workloads face intense pressure, the processor architecture powering your instances becomes critical to both performance and cost. AWS Graviton instances—with their ARM-based design—are increasingly becoming the preferred choice for organizations seeking to optimize cloud performance during high-load scenarios.

What are AWS Graviton instances and why do they matter?

AWS Graviton processors are custom-built by AWS using ARM architecture, designed specifically for cloud workloads. Unlike traditional x86 processors from Intel and AMD that use hyperthreading to simulate additional cores, Graviton instances map one vCPU directly to one physical core, which provides significant advantages under heavy load.

This architectural difference is crucial when performance matters most. As research has shown, Graviton4 consistently outperforms AMD EPYC Genoa and Intel Xeon Sapphire Rapids in high-performance computing workloads. For organizations dealing with compute-intensive tasks, this translates to both better performance and lower costs.

Performance benefits under heavy load

When your cloud infrastructure experiences peak demands, Graviton instances demonstrate several key advantages:

1. Superior multi-threaded performance: Graviton3 instances achieve approximately 40% better price-performance than Intel c5 instances for fully-utilized, multi-threaded workloads. This is particularly important for containerized applications and microservices that scale horizontally.

2. Memory bandwidth advantages: Graviton4’s DDR5-5600 memory provides triple the bandwidth of previous generations, making it ideal for data-intensive applications processing large datasets.

3. Throughput improvements: Benchmark tests show that Graviton instances can handle up to 20-25% more requests per second for web servers running technologies like Nginx or Node.js.

4. Reduced latency: Tests with PostgreSQL workloads show up to 19% lower latency on Graviton4 compared to Graviton3, and 29% lower latency compared to Graviton2. This translates to more responsive applications, even during traffic spikes.

For companies managing high-traffic websites or processing large data volumes, these performance improvements can be transformative. One financial tech firm that migrated to R7g instances for memory-heavy blockchain workloads saw both cost reductions and significant performance improvements during transaction processing peaks.

Cost optimization strategies with Graviton

Beyond raw performance, Graviton instances deliver substantial cost benefits that become even more pronounced under heavy load:

Instance selection for different workloads

Choosing the right Graviton instance family for your specific workload is critical:

• C7g/C6g: Compute-optimized instances ideal for CPU-heavy tasks like web servers and scientific computing
• R7g/R6g: Memory-optimized instances perfect for in-memory databases and real-time analytics
• M7g/M6g: General-purpose instances balancing compute, memory and networking

Many organizations report cost savings of 30-40% when migrating from x86 to comparable Graviton instances, with GOV.UK achieving 15% per-instance savings moving from m6i (x86) to m7g (Graviton).

Compound savings strategies

To maximize cost efficiency:

1. Stack discounts: Combine Graviton adoption with Reserved Instances or Savings Plans for compounded savings
2. Right-sizing: Leverage Graviton’s efficiency to use smaller instance sizes while maintaining performance
3. Automated monitoring: Use tools like Hykell’s automated optimization platform to continuously identify underutilized resources and optimization opportunities

A hybrid approach—using Graviton for scalable workloads and x86 for legacy applications—often provides the best balance of performance and cost optimization. This is particularly effective for organizations with mixed workloads, where some applications benefit more from Graviton’s architecture than others.

Impact on compliance and governance

While Graviton instances operate within AWS’s standard compliance frameworks (ISO 27001, SOC 2), specific considerations exist when migrating regulated workloads:

1. Data residency: AWS offers Graviton instances in UK/EU data centers, ensuring compliance with GDPR and UK data protection laws
2. Performance validation: Regulated industries often require documented performance metrics, making benchmarking essential before migration
3. Migration documentation: Maintain detailed records of architecture changes when moving regulated workloads to Graviton

Organizations in finance, healthcare, and government sectors should work with compliance teams to update documentation reflecting the shift to ARM architecture. The compatibility of software with AWS Graviton varies by application, which has implications for systems handling sensitive or regulated data.

Best practices for efficient data processing

To maximize Graviton’s efficiency for data processing workloads:

1. Compiler and runtime optimization

Use ARM-optimized toolchains like GCC or LLVM with ARM-specific compiler flags. Applications not specifically compiled for ARM may significantly underperform compared to their x86 counterparts.

For example, a healthcare analytics company optimizing their genome sequencing pipeline for Graviton3 achieved 35% faster processing by using ARM-optimized libraries and compiler settings—a difference that dramatically reduced time-to-insight for critical patient data.

2. Container optimization

For containerized workloads:

• Build container images specifically for ARM64 architecture
• Use multi-architecture images to enable seamless deployment across x86 and ARM
• Consider automated build pipelines that create architecture-specific variants

Docker’s buildx feature makes creating multi-architecture images straightforward, allowing you to maintain a single codebase while optimizing for both ARM and x86 platforms.

3. Workload-specific tuning

Different workloads benefit from specific tuning:

• Databases: Adjust memory settings and buffer sizes for ARM architecture
• Analytics: Optimize thread count and chunk sizes for parallel processing
• Machine learning: Use ARM-optimized ML libraries for inference workloads

Following best practices for Graviton instances can significantly improve performance under heavy load. One financial services firm found that simply adjusting their JVM memory settings for ARM architecture improved transaction processing by 18% on their trading platform.

Case study: E-commerce platform under peak load

A revealing example comes from an e-commerce platform that migrated to Graviton instances before Black Friday. The results were impressive:

• 3× traffic handling capacity during peak events with the same infrastructure costs
• 42% performance improvement for product recommendation engines
• $12M annual infrastructure savings across their platform

This transformation wasn’t just about cost savings—it was about resilience. During flash sales that previously caused performance degradation, their Graviton-powered infrastructure maintained consistent response times even as traffic tripled. The product recommendation system, which previously slowed during peak periods, actually maintained sub-100ms response times throughout Black Friday, contributing to a 7% increase in conversion rates.

Limitations and considerations

Despite the advantages, some limitations should inform your migration strategy:

1. Single-threaded performance: Intel and AMD instances may still outperform Graviton for single-threaded applications. For these workloads, AMD offers approximately 6% better price-performance than Intel.

2. Software compatibility: Some legacy applications or specialized software may require x86 architecture. Always verify compatibility before migration.

3. Migration effort: While containerized applications typically migrate easily, legacy applications may require substantial effort to recompile or refactor for ARM.

When considering the transition, think of Graviton adoption as a spectrum rather than a binary choice. Organizations frequently begin with “low-hanging fruit”—modern, containerized applications—before tackling more complex migrations.

Implementing a migration strategy

For organizations looking to leverage Graviton instances:

1. Start with proof of concept: Conduct benchmarks comparing Graviton (e.g., C7g) against current x86 instances (e.g., C5) to validate performance gains

2. Prioritize containerized applications: These typically require minimal changes and offer immediate benefits

3. Use a phased approach: Begin with development and non-critical workloads before migrating production systems

4. Monitor and optimize: Continuously track performance metrics and costs to identify further optimization opportunities

Organizations working with Hykell can leverage automated cloud cost optimization services to identify ideal Graviton migration candidates and achieve up to 40% savings on AWS without compromising performance.

Are Graviton instances right for your heavy workloads?

For most organizations running modern, cloud-native applications, AWS Graviton instances offer compelling advantages under heavy load:

• Better performance for multi-threaded and memory-intensive workloads
• Cost savings of 20-40% compared to x86 instances
• Improved energy efficiency and reduced carbon footprint
• Scaling advantages during traffic spikes and high-demand periods

The question is less about whether to adopt Graviton and more about which workloads to migrate first. With proper planning and optimization, Graviton instances can transform both the performance and economics of your cloud infrastructure under heavy load.

To determine your potential savings with Graviton instances and identify optimal migration candidates, consider leveraging specialized tools and expertise from cloud optimization partners like Hykell who can automate much of this process while ensuring performance requirements are maintained.