Skip to main content

Workload Analysis with nOps Discovery

Overview​

Workload Analysis is a core capability of nOps Discovery that provides deep insights into your IT estate's resource utilization patterns, licensing implications, and business intent. By combining advanced telemetry collection with AI-driven analysis, nOps helps you make informed decisions about cloud migration strategies, right-sizing, and modernization opportunities.

Resource Utilization Modeling​

Comprehensive Utilization Metrics​

nOps Discovery captures and analyzes detailed resource utilization across multiple dimensions:

  • CPU Analysis:
    • Peak, average, and P95 utilization patterns
    • Core usage distribution across time
    • Thread and process affinity
    • Instruction set requirements (e.g., AVX, SSE4)
  • Memory Usage:
    • Working set size
    • Page file utilization
    • Memory pressure indicators
    • Allocation patterns over time
  • Storage Performance:
    • IOPS (read and write)
    • Throughput
    • Latency requirements
    • Block size distribution
    • Sequential vs. random access patterns
  • Network Behavior:
    • Bandwidth consumption
    • Packet rates
    • Connection frequency
    • Protocol distribution
    • Internal vs. external traffic patterns

Time-Series Pattern Analysis​

The nOps platform evaluates utilization across multiple time dimensions to identify:

  • Daily Patterns: Variations in utilization during work hours vs. off-hours
  • Weekly Cycles: Weekend vs. weekday utilization differences
  • Monthly Trends: End-of-month processing or reporting spikes
  • Seasonal Variations: Business cycles and seasonal demand changes

Workload Classification​

Based on utilization patterns, nOps Discovery classifies workloads into categories such as:

  • Steady-State: Consistent, predictable resource consumption
  • Bursty: Intermittent high-utilization periods
  • Periodic: Regular, scheduled processing peaks
  • Dev/Test: Irregular usage with low utilization averages
  • Idle/Dormant: Minimal activity suggesting potential decommissioning

Licensing Analysis​

nOps Discovery performs comprehensive software licensing analysis to optimize costs and compliance in your cloud migration:

Operating System Licensing​

  • Windows Server: Edition identification and licensing model (Standard/Datacenter)
  • Linux: Distribution detection and commercial support requirements
  • License Mobility: Eligibility for license mobility across environments

Database Licensing​

  • Oracle: Edition detection, options in use, and cloud migration implications
  • SQL Server: Edition identification, licensing models (core vs. CAL)
  • MySQL/PostgreSQL: Commercial support requirements
  • Cloud-Compatible Alternatives: Recommendations for managed database services

Application Licensing​

  • Commercial Software: Detection of major ISV products and licensing models
  • Virtualization Implications: Licensing constraints in virtual environments
  • Bring Your Own License (BYOL): Eligibility and cost-benefit analysis
  • Pay-As-You-Go Alternatives: Comparison with consumption-based licensing

AI Pattern Analysis for Business Intent​

nOps Discovery employs sophisticated AI models to determine the business intent and context of each workload:

Behavioral Analysis​

  • Application Patterns: Identification of common application architectures
  • Inter-Service Communication: Mapping of service dependencies and data flows
  • Authentication Patterns: Identification of identity services and access models
  • Data Processing Characteristics: Analysis of data handling patterns

Business Criticality Assessment​

  • Availability Requirements: Analysis of uptime patterns and redundancy
  • Performance SLAs: Inference of performance requirements from utilization patterns
  • Data Sensitivity: Identification of potential regulatory or compliance requirements
  • Business Impact: Categorization of workloads by business criticality

Modernization Opportunity Identification​

Deep Code Introspection​

With appropriate permissions, nOps Discovery can perform deep code introspection to gain unprecedented insights into custom applications. This opt-in capability uses advanced AI techniques to analyze source code and binaries without storing or retaining your proprietary code.

Key Capabilities​

  • Architecture Analysis: Identification of component boundaries, dependencies, and architectural patterns
  • Functional Intent Recognition: Understanding business logic, API semantics, and data flows
  • Modernization Potential: Assessment of container compatibility, serverless suitability, and microservice boundaries

Business Benefits​

  • More Accurate Migration Planning: Better resource sizing and configuration based on actual code
  • Enhanced Modernization Opportunities: Concrete recommendations for containerization and serverless conversion
  • Risk Reduction: Early identification of compatibility issues and technical debt
  • Accelerated Transformation: Automated generation of containerization and serverless artifacts

For full details on security measures, AI technology, and how to enable this feature, see the Deep Code Introspection documentation.

Output and Recommendations​

The Workload Analysis process produces several valuable outputs for migration planning:

Detailed Workload Profiles​

Each workload receives a comprehensive profile including:

  • Resource utilization patterns
  • Licensing status and constraints
  • Business criticality assessment
  • Dependencies and integration points
  • Right-sizing recommendations

AWS Instance Recommendations​

  • Instance Family Selection: Optimal instance family based on workload characteristics
  • Instance Size Optimization: Right-sized instance recommendations
  • Reserved Instance Opportunities: Recommendations for RI purchases where appropriate
  • Spot Instance Candidates: Workloads suitable for spot instance usage

Modernization Roadmap​

  • Phase 1 - Lift and Shift: Workloads suitable for immediate migration
  • Phase 2 - Platform Refactoring: Opportunities for database modernization, OS upgrades
  • Phase 3 - Application Modernization: Containerization and serverless conversion candidates
  • Phase 4 - Rearchitecting: Long-term modernization opportunities

Cost Optimization Projections​

  • Current vs. Future State: Detailed cost comparison between current environment and AWS
  • Optimization Scenarios: Multiple migration scenarios with varying levels of modernization
  • License Cost Impacts: Detailed analysis of licensing cost changes in AWS
  • Long-term TCO: 3-year projected total cost of ownership

How to Access Workload Analysis​

  1. Deploy the Agentless Data Collector using the installation guide
  2. Allow at least 7 days of data collection for comprehensive pattern analysis (2 weeks recommended)
  3. Access the Analysis Dashboard from your nOps portal under "Discovery" → "Workload Analysis"
  4. Export Reports in various formats for stakeholder review
  5. Engage with nOps Solution Architects for personalized guidance (available with Premium support plans)