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LSPERF14-BP03 Assess edge computing integration for localized processing of research workloads - Life Sciences Lens
Implementation guidance

LSPERF14-BP03 Assess edge computing integration for localized processing of research workloads

Assess edge computing solutions that enable research processing near data sources and users. Review edge systems' compatibility with research computing frameworks while improving version consistency. Test edge-based collaboration tools including shared notebooks, visualization systems, and model training. Compare performance and resource efficiency between edge and centralized processing. Evaluate edge solutions supporting secure multi-tenant environments where researchers from different institutions can share computing resources with proper data isolation.

Desired outcome: You have a secure edge computing environment that enables localized processing of research workloads with effective collaboration tools, optimized performance, and strict multi-tenant isolation. This allows researchers from different institutions to efficiently share computing resources while maintaining data security and low-latency access.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Assess edge computing systems for compatibility with research workflows and computing frameworks. Document system capabilities including processing power, memory requirements, and storage options. Create comprehensive evaluation criteria focusing on version management, framework support, and integration capabilities with existing research infrastructure.

Evaluate edge-based collaboration tools and their effectiveness in supporting research workflows. Test implementation of shared notebooks, real-time visualization systems, and distributed model training capabilities. Document performance metrics and user experience factors across different edge locations and research scenarios.

Conduct systematic comparison of edge versus centralized processing for common research workloads. Measure resource utilization, processing times, and data transfer requirements across different deployment models. Create performance baselines and optimization strategies for various research computing scenarios.

Design secure multi-tenant environments that enable resource sharing while maintaining strict data isolation. Implement access controls and monitoring systems that foster secure collaboration across institutions. Establish clear security protocols and frameworks for edge deployments.

Implementation steps

  1. Deploy comprehensive edge infrastructure with AWS Outposts for local processing, AWS Wavelength for ultra-low latency applications, and AWS Local Zones to reduce latency for research tools and visualization.

  2. Establish collaborative research solutions using Amazon SageMaker AI for distributed model training, AWS IoT Greengrass for edge device management, and container services for consistent application deployment across locations.

  3. Implement robust monitoring and optimization with Amazon CloudWatch for edge performance tracking, AWS Systems Manager for infrastructure management, and AWS X-Ray for distributed application tracing and analysis.

  4. Secure edge environments through AWS IAM for granular access control, AWS Security Hub CSPM for centralized security posture management, and AWS KMS for comprehensive data encryption and key management.

  5. Document edge architecture with connectivity patterns, data synchronization procedures, and failover mechanisms for research continuity.

  6. Conduct regular performance testing to validate latency requirements and optimize resource allocation for evolving research workloads.

  7. Establish governance framework for edge deployments including monitoring and automated security controls.