Unveiling the Superiority of MMCloud over AWS Batch as a Nextflow Executor: A Comprehensive Benchmark Analysis

In the rapidly evolving world of cloud computing, the choice of platform can significantly impact both the efficiency and cost of operations, especially in specialized fields like bioinformatics. Recently, a meticulous benchmark study comparing MMCloud with AWS Batch across various bioinformatics pipelines has shed light on the remarkable advantages of MMCloud. Here, we delve into the key findings that position MMCloud as a superior choice for bioinformatics workflows.

Pipelines used

• nf-core/rnaseq

  • • rnaseq samplesheet - 8 samples

• nf-core/atacseq

  • • atacseq samplesheet - 6 samples

• nf-core/sarek

  • • sarek samplesheet - 2 samples

Benchmark Results

• Each pipeline was executed three times for each compute environment tested. The average result for each pipeline is shown in the following sections

Exceptional Cost Efficiency

Cost ($)

The most striking advantage of MMCloud is its cost-effectiveness. Across all tested bioinformatics pipelines, including nf-core/rnaseq, nf-core/atacseq, and nf-core/sarek, MMCloud consistently demonstrated lower costs in both OnDemand and Spot instances. For instance, the rnaseq pipeline on MMCloud-OnDemand is approximately 60% cheaper than on AWS Batch. This significant cost reduction opens up opportunities for more extensive and frequent computational experiments within a constrained budget.

Reduced CPU-Hour Billing

CPU-Hours (billable)

Another crucial aspect where MMCloud shines is in its efficient use of CPU hours. The benchmark results showed that AWS Batch generally incurs more billable CPU hours than MMCloud for the same tasks. This efficiency not only contributes to the cost-saving aspect but also reflects optimized resource utilization, a critical factor in sustainable computing practices.

Competitive Processing Times

Wall-Time (h)

While AWS Batch exhibited slightly better performance in wall-time for some pipelines, MMCloud was not far behind, even outperforming AWS Batch in the sarek pipeline. This finding suggests that MMCloud provides a balanced approach, ensuring timely completion of tasks without compromising on cost and resource efficiency.

Advanced Handling of Spot-Reclaims and Wave-Rider in MMCloud

Adaptive Job Management in Dynamic Environments

MMCloud's approach to handling spot-reclaims and utilizing the wave-rider feature during the benchmarks is a testament to its adaptive and robust job management capabilities. This sophisticated handling ensures uninterrupted workflow execution, even in the volatile environment of cloud computing.

Insight into Job Migration and Resource Optimization

A key inquiry in the thread was about the frequency of task migrations due to memory-instigated resizing. While MMCloud's operational center (opcenter) GUI currently doesn't provide detailed workflow-level reports on such migrations, the float CLI tool offers valuable insights. For instance, during the benchmark period (Nov 8 - Nov 22, 2023), a report generated using float report get usage_report_by_job revealed 125 jobs that migrated to more than one instance, primarily due to spot-reclaims or wave-riding.

Distinctive Features of MMCloud: Spot-Reclaims and Wave-Riding

• Spot-Reclaims: Majority of the migrations were due to spot-reclaims, showcasing MMCloud's efficient response to instance preemptions. This feature is crucial for maintaining workflow continuity in spot instance environments, a common challenge in cloud computing.
• Wave-Riding: The logs indicated several instances where jobs were migrated due to wave-riding, triggered by varying resource needs. For example, migrations occurred when memory consumption was below a certain threshold, or CPU usage exceeded set limits. This dynamic adjustment ensures optimal resource utilization and cost-efficiency.
• Addressing Compatibility Issues: Interestingly, some jobs floated up to 11 times due to instance compatibility issues, a concern that MMCloud has addressed with an upcoming fix in the 2.4 release.

Conclusion: MMCloud's Superior Resource Management

The way MMCloud handles spot-reclaims and wave-rider features adds another layer of efficiency and reliability to its platform. This advanced resource management capability, ensuring seamless job migrations and optimal resource utilization, further underscores MMCloud's edge over AWS Batch. By continually evolving and addressing issues like instance compatibility, MMCloud demonstrates its commitment to providing a robust and user-friendly cloud computing environment, particularly for complex bioinformatics workflows.

The benchmark analysis unequivocally places MMCloud in a favorable position, especially for bioinformatics workflows. Its outstanding cost efficiency, coupled with optimized CPU usage and competitive processing times, make MMCloud an attractive option for researchers and organizations seeking to maximize their computational resources.

In summary, MMCloud emerges as a compelling alternative to AWS Batch, offering a more balanced and economical solution for bioinformatics computing needs. Its advantages in cost-saving and resource optimization are crucial for advancing research within budgetary constraints, making it an essential tool for the future of bioinformatics and cloud computing.

Versions used in Benchmark