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HPC Status Monitor - Continuous Improvement Log

Objective: Evolve this tool into a clear, discoverable, and machine- and human-friendly source of truth for HPC fleet capacity, availability, and scheduling state across heterogeneous systems and sites.

Last Updated: 2026-01-29

Iteration 1: Initial Assessment

Executive Summary

The HPC Status Monitor is a well-architected system with solid foundations from the Phase 1-8 refactoring effort. The codebase demonstrates good separation of concerns, reasonable documentation, and support for multiple HPC platforms (HPCMP, NOAA, generic PW clusters).

However, from the perspective of the stated goal—serving as a reference model for how next-generation HPC systems expose capacity and availability to users and AI assistants—there are significant gaps in:

  1. Semantic clarity - Data models conflate concepts that should be distinct
  2. Schema formalization - No machine-readable contracts (JSON Schema, OpenAPI)
  3. AI-assist readiness - Missing structured outputs that LLMs can reliably parse
  4. Cross-system normalization - Scheduler-specific details leak into unified views

Persona-Based Evaluation

1. HPC End Users (Submitting Jobs)

What works:

  • Dashboard provides visual overview of system status
  • Queue health page shows job counts
  • Allocation tracking helps avoid surprise failures

Friction points:

  • Ambiguous terminology: "status" could mean system up/down, queue state, or job state
  • Missing context: No explanation of what "DEGRADED" means operationally
  • No actionable guidance: User sees queue is "busy" but not what to do about it
  • Time estimates absent: "When will my job start?" is not answerable

Recommended changes:

  1. Add glossary/tooltip system explaining HPC terminology
  2. Add estimated wait times to queue views
  3. Add "Where should I run this job?" recommendation widget
  4. Distinguish between "system status" and "queue health" clearly

2. HPC Administrators and Operators

What works:

  • Fleet overview with DSRC breakdown
  • Storage monitoring with thresholds
  • Health endpoint for monitoring

Friction points:

  • Missing operational metrics: No node failure counts, maintenance windows
  • No historical trending: Can't see if a system is getting worse over time
  • Alert fatigue risk: Insights page mixes critical warnings with suggestions

Recommended changes:

  1. Add severity levels to insights (critical/warning/info)
  2. Add /api/alerts endpoint for integration with PagerDuty/Slack
  3. Store and expose historical status for trend analysis

3. Platform Teams (Integrating with Schedulers)

What works:

  • REST API with documented endpoints
  • Configuration-driven collector system
  • Base collector interface for extensibility

Friction points:

  • No OpenAPI/JSON Schema: Integrators must reverse-engineer response formats
  • Inconsistent field naming: hours_remaining vs remaining_hours patterns
  • No versioning: Breaking changes have no safety mechanism
  • No pagination: Large fleet responses are unbounded

Recommended changes:

  1. Add OpenAPI 3.1 specification
  2. Add JSON Schema for all data models
  3. Implement API versioning (start with /api/v1/)
  4. Add pagination to list endpoints

4. AI Code Assistants (Reasoning about Capacity)

What works:

  • JSON API returns structured data
  • Insights endpoint provides pre-computed recommendations

Friction points:

  • Semantic ambiguity: "status: UP" doesn't indicate schedulable capacity
  • Missing invariants: No way to know if "available_cores: 100" means allocatable now
  • No units: Is walltime in hours, minutes, or HH:MM:SS format?
  • No examples: LLMs need sample requests/responses to learn patterns
  • No capacity vs availability distinction: 1000 cores total vs 50 cores free now

Recommended changes:

  1. Add explicit capacity vs availability fields
  2. Add units metadata to numeric fields or use SI suffixes
  3. Provide examples/ directory with annotated request/response pairs
  4. Add semantic type hints (e.g., "type": "core_count")

Resource Visibility and Semantics Analysis

Current State

The data models in src/data/models.py represent:

  • System status: UP/DOWN/DEGRADED/MAINTENANCE
  • Queue info: Jobs running/pending, cores used
  • Allocation info: Hours allocated/used/remaining
  • Storage info: GB total/used/available

Gaps Identified

Concept Current State Issue
Capacity Implicit in total_cores Not distinguished from availability
Availability Implicit in cores_free Conflated with capacity
Reservations Not modeled Jobs waiting in queue != reserved capacity
Fragmentation Not modeled 100 free cores might be 10x10 nodes, unusable for 50-core job
Priority/Preemption Not modeled User may have access but low priority
Burst capacity Not modeled Some systems have burstable cloud resources
Maintenance windows Partially in "status" No scheduled future downtime

Proposed Semantic Model

Capacity (Static)
├── Total hardware resources
├── Partition/queue allocation policies
└── Hardware constraints (GPU types, memory per node)

Availability (Dynamic)
├── Currently idle resources
├── Currently running (occupied)
├── Pending (queued but not started)
├── Reserved (future commitments)
├── Draining (going offline soon)
└── Failed/Offline

Schedulability (Policy)
├── User access rights
├── Queue limits (max walltime, max jobs)
├── Priority/fairshare standing
└── Allocation remaining

Cross-System Consistency Analysis

Current Normalization

Field PBS Source Slurm Source Normalized?
Queue name Queue name Partition name Yes (as "queue")
Jobs running qstat counts squeue counts Yes
Max walltime qmgr -c "p q" scontrol show partition Partially (format varies)
Node state pbsnodes sinfo No (different states)
Fair share qstat -Q sshare No (different metrics)

Inconsistencies Found

  1. Status values: PBS uses state names different from Slurm
  2. Time formats: Some fields use HH:MM:SS, others use hours as float
  3. Resource naming: "cores" vs "CPUs" vs "processors"
  4. Queue types: PBS "execution queue" vs Slurm "partition" semantics differ

Proposed Normalization Strategy

  1. Define canonical status enum: ACTIVE | DRAINING | OFFLINE | MAINTENANCE
  2. Use ISO 8601 durations for all time fields: PT24H instead of 24:00:00
  3. Standardize on "cores" as the CPU resource unit
  4. Add scheduler_native sub-object for raw scheduler-specific data

AI-Assist Readiness Assessment

Current Score: 4/10

Criterion Status Notes
Structured JSON output ✅ Good foundation
Consistent field naming ⚠️ Some inconsistencies
Explicit types/units ❌ Missing
Semantic annotations ❌ Missing
Example corpus ❌ No examples/ directory
Schema definitions ❌ No JSON Schema
Error categorization ⚠️ HTTP codes but no error types
Deterministic output ⚠️ Timestamps vary

Prioritized Improvement Opportunities

Priority 1: Critical (Blocks AI/Automation Use)

  1. Add JSON Schema for all API responses

    • Impact: Enables code generation, validation, AI parsing
    • Effort: Medium
    • Files: schemas/, docs/api.md

  2. Distinguish capacity from availability in data models

    • Impact: Fundamental semantic clarity
    • Effort: Medium
    • Files: src/data/models.py, API responses

  3. Add explicit units to all numeric fields

    • Impact: Eliminates ambiguity for both humans and AI
    • Effort: Low
    • Files: src/data/models.py, documentation

Priority 2: High (Improves Multi-Persona Experience)

  1. Implement API versioning

    • Impact: Safe evolution, integration stability
    • Effort: Medium
    • Files: src/server/routes.py

  2. Add examples/ directory with annotated scenarios

    • Impact: AI learning, user onboarding
    • Effort: Low
    • Files: New examples/ directory

  3. Normalize scheduler-specific status values

    • Impact: Cross-system comparability
    • Effort: Medium
    • Files: Collectors, models

Priority 3: Medium (Enhances Clarity)

  1. Add glossary of HPC terminology

    • Impact: Onboarding, disambiguation
    • Effort: Low
    • Files: docs/glossary.md, UI tooltips

  2. Add severity levels to insights

    • Impact: Operator efficiency, alert integration
    • Effort: Low
    • Files: src/insights/recommendations.py

  3. Add historical status tracking

    • Impact: Trend analysis, reliability metrics
    • Effort: Medium
    • Files: src/data/persistence.py

Priority 4: Lower (Future Enhancements)

  1. Model fragmentation and schedulability
  2. Add maintenance window forecasting
  3. Implement pagination for list endpoints
  4. Add webhook support for status changes

Iteration 1 Actions Taken

  1. Created this REFACTOR.md to track improvement progress
  2. Documented persona-based evaluation
  3. Identified semantic gaps in resource modeling
  4. Prioritized 13 improvement opportunities
  5. Established baseline for AI-assist readiness (4/10)
  6. Created schemas/ directory with JSON Schema definitions:
    • Common definitions: units.schema.json, status-enum.schema.json, timestamp.schema.json
    • Model schemas: system-status.schema.json, queue-info.schema.json, allocation-info.schema.json, storage-info.schema.json, system-insight.schema.json, resource-pool.schema.json
    • API response schemas: fleet-status-response.schema.json, cluster-usage-response.schema.json, health-response.schema.json
  7. Created examples/ directory with annotated scenarios:
    • 01-check-fleet-status.md - Basic fleet status checking
    • 20-ai-where-to-run.md - AI assistant reasoning example
  8. Created docs/glossary.md with HPC terminology definitions
  9. Introduced capacity vs availability distinction in schemas (Priority 1.2)
  10. Added explicit units via x-unit schema extension (Priority 1.3)

Iteration 2: Schema Implementation and API Enhancement

Focus

Continue building the foundation for machine-consumable data:

  1. Integrate schemas with actual API responses
  2. Add severity levels to insights
  3. Normalize status enums in code

Files Created This Iteration

schemas/
├── README.md                              # Schema documentation
├── common/
│   ├── units.schema.json                  # Unit definitions (cores, hours, bytes, etc.)
│   ├── status-enum.schema.json            # Canonical status values
│   └── timestamp.schema.json              # Timestamp and observation metadata
├── models/
│   ├── system-status.schema.json          # HPC system operational status
│   ├── resource-pool.schema.json          # Capacity vs availability model
│   ├── queue-info.schema.json             # Queue/partition information
│   ├── allocation-info.schema.json        # Compute time allocation
│   ├── storage-info.schema.json           # Filesystem capacity
│   └── system-insight.schema.json         # Recommendations and alerts
└── api/
    ├── fleet-status-response.schema.json  # /api/status response
    ├── cluster-usage-response.schema.json # /api/cluster-usage response
    └── health-response.schema.json        # /api/health response

examples/
├── README.md                              # Examples documentation
├── 01-check-fleet-status.md               # Basic status checking
└── 20-ai-where-to-run.md                  # AI assistant reasoning

docs/
└── glossary.md                            # HPC terminology definitions

Schema Design Decisions

1. Capacity vs Availability Model

Created resource-pool.schema.json that explicitly separates:

{
  "capacity": {
    "total": 100000,        // Static: total hardware
    "allocatable": 98000    // Static: minus reserved/offline
  },
  "availability": {
    "idle": 15000,          // Dynamic: free right now
    "allocated": 78000,     // Dynamic: running jobs
    "pending": 25000,       // Dynamic: queued jobs
    "reserved": 2000,       // Dynamic: future commitments
    "offline": 5000         // Dynamic: failed/draining
  }
}

This addresses the core semantic ambiguity identified in Iteration 1.

2. Explicit Units via x-unit Extension

All numeric fields that represent physical quantities include explicit units:

{
  "total_cores": {
    "type": "integer",
    "x-unit": "cores"
  },
  "max_walltime_seconds": {
    "type": "integer",
    "x-unit": "seconds"
  }
}

3. Normalized Status Enums

Defined canonical status values in status-enum.schema.json:

  • System: UP | DOWN | DEGRADED | MAINTENANCE | UNKNOWN
  • Queue: ACTIVE | INACTIVE | DRAINING | OFFLINE
  • Node: IDLE | ALLOCATED | MIXED | DOWN | DRAINING | RESERVED | MAINTENANCE
  • Insight severity: CRITICAL | WARNING | INFO | SUGGESTION

4. Observation Metadata

All API responses include observation metadata:

{
  "meta": {
    "observed_at": "2026-01-29T15:30:00Z",
    "source": "hpcmp_collector",
    "from_cache": false,
    "cache_age_seconds": 0
  }
}

AI-Assist Readiness Progress

Updated Score: 6/10 (up from 4/10)

Criterion Status Notes
Structured JSON output ✅ Good foundation
Consistent field naming ✅ Standardized in schemas
Explicit types/units ✅ x-unit extension added
Semantic annotations ✅ Enums with descriptions
Example corpus ✅ examples/ created
Schema definitions ✅ schemas/ created
Error categorization ⚠️ HTTP codes but no error types
Deterministic output ⚠️ Timestamps vary

Code Changes This Iteration

Updated src/data/models.py to implement schema concepts:

1. Added Status Enums

class SystemOperationalStatus(str, Enum):
    UP = "UP"
    DOWN = "DOWN"
    DEGRADED = "DEGRADED"
    MAINTENANCE = "MAINTENANCE"
    UNKNOWN = "UNKNOWN"

class QueueState(str, Enum):
    ACTIVE = "ACTIVE"
    INACTIVE = "INACTIVE"
    DRAINING = "DRAINING"
    OFFLINE = "OFFLINE"

class StorageHealthStatus(str, Enum):
    HEALTHY = "HEALTHY"
    WARNING = "WARNING"
    CRITICAL = "CRITICAL"

class InsightSeverity(str, Enum):
    CRITICAL = "CRITICAL"
    WARNING = "WARNING"
    INFO = "INFO"
    SUGGESTION = "SUGGESTION"

class AllocationStatus(str, Enum):
    HEALTHY = "HEALTHY"
    LOW = "LOW"
    CRITICAL = "CRITICAL"
    EXHAUSTED = "EXHAUSTED"
    EXPIRED = "EXPIRED"

2. Added Capacity vs Availability Model

@dataclass
class ResourceCapacity:
    total: int
    allocatable: Optional[int] = None

@dataclass
class ResourceAvailability:
    idle: int
    allocated: int
    pending: int = 0
    reserved: int = 0
    offline: int = 0

@dataclass
class ResourcePool:
    unit: str  # 'cores', 'nodes', 'gpus', 'gigabytes'
    capacity: ResourceCapacity
    availability: ResourceAvailability

@dataclass
class SystemCapacity:
    total_nodes: Optional[int] = None
    total_cores: Optional[int] = None
    total_gpus: Optional[int] = None
    total_memory_gb: Optional[float] = None
    architecture: Optional[str] = None

@dataclass
class SystemAvailability:
    nodes_up: Optional[int] = None
    nodes_down: Optional[int] = None
    cores_available: Optional[int] = None
    cores_in_use: Optional[int] = None
    utilization_percent: Optional[float] = None

3. Enhanced Existing Models

  • QueueInfo: Added state enum, max_walltime_seconds, walltime parsing, wait_estimate_seconds
  • AllocationInfo: Added status enum, percent_used, hours_pending, burn_rate_hours_per_day
  • SystemStatus: Added status_reason, capacity, availability, operational_status property
  • SystemInsight: Added severity enum, scope fields (queue, project, storage), action fields
  • StorageInfo: Added storage_type, status returns enum, status_str for backward compatibility

4. Updated Tests

Added 15 new tests to tests/unit/test_models.py:

  • Queue walltime parsing (HMS, DHMS formats)
  • Allocation status thresholds (HEALTHY, LOW, CRITICAL, EXHAUSTED)
  • Insight severity from priority mapping
  • System operational status enum

All 43 model-related tests pass.

Iteration 3: Recommendation Engine Enhancement

Completed

  1. Updated recommendation engine to use severity enums

    • Insights now use InsightSeverity (CRITICAL, WARNING, INFO, SUGGESTION)
    • Insights use InsightType (RECOMMENDATION, WARNING, ALERT, INFO)
    • Sorting now prioritizes by severity then priority

  2. Enhanced insight generation

    • Added storage capacity warnings (CRITICAL at >95%, WARNING at >80%)
    • Added system DOWN status (CRITICAL severity)
    • Added more granular allocation warnings (CRITICAL at <5%, WARNING at <20%, INFO at <40%)
    • Added action descriptions and commands to insights
    • Improved queue depth thresholds (WARNING at >100, SUGGESTION at >50)

  3. Updated metric naming for clarity

    • allocation → allocation_percent_remaining
    • queue_depth → queue_pending_jobs
    • storage → storage_percent_used
    • status → system_status

  4. Updated tests

    • Fixed test expectations for new metric names
    • All 11 recommendation tests pass

Code Changes

# src/insights/recommendations.py
from ..data.models import SystemInsight, InsightSeverity, InsightType

# Insights now include:
SystemInsight(
    type=InsightType.ALERT.value,  # RECOMMENDATION, WARNING, ALERT, INFO
    severity=InsightSeverity.CRITICAL,  # CRITICAL, WARNING, INFO, SUGGESTION
    message="...",
    priority=5,
    related_metric="allocation_percent_remaining",
    cluster=name,
    action_description="Request additional allocation hours immediately",
)

# Sorting by severity then priority
severity_order = {
    InsightSeverity.CRITICAL: 0,
    InsightSeverity.WARNING: 1,
    InsightSeverity.INFO: 2,
    InsightSeverity.SUGGESTION: 3,
}
insights.sort(key=lambda x: (severity_order.get(x.severity, 4), -x.priority))

Test Results

All model and recommendation tests pass (54 tests total):

  • test_models.py: 23 passed
  • test_recommendations.py: 11 passed
  • test_config.py: 9 passed
  • Other tests: 11 passed

Iteration 5: API Documentation and Error Handling

Completed

  1. Created OpenAPI 3.1 Specification

    • Full API documentation at schemas/openapi.yaml
    • All endpoints documented with request/response schemas
    • Includes examples and error responses
    • Rate limiting headers documented

  2. Added Error Response Schema

    • Created schemas/api/error-response.schema.json
    • Standard error codes: NOT_FOUND, VALIDATION_ERROR, RATE_LIMITED, COLLECTOR_ERROR, TIMEOUT, INTERNAL_ERROR
    • Machine-readable codes with human-readable messages
    • Includes details object for debugging context

  3. Created Additional Example Scenarios

    • examples/02-check-allocation.md - Verify sufficient hours before job submission
    • examples/03-find-best-queue.md - Queue selection with decision matrix

Files Created

  • schemas/openapi.yaml - OpenAPI 3.1 specification (~500 lines)
  • schemas/api/error-response.schema.json - Standard error format
  • examples/02-check-allocation.md - Allocation checking example
  • examples/03-find-best-queue.md - Queue selection example

Error Response Format

{
  "error": {
    "code": "NOT_FOUND",
    "message": "System 'unknown-system' not found in fleet",
    "details": {
      "parameter": "system",
      "value": "unknown-system",
      "suggestion": "Use /api/status to list available systems"
    },
    "request_id": "req_20260129_153000_abc123"
  },
  "meta": {
    "timestamp": "2026-01-29T15:30:00Z"
  }
}

OpenAPI Features

  • Server URL templating for reverse proxy deployments
  • Complete schema definitions for all models
  • Rate limiting response headers documented
  • Examples for common use cases

Iteration 6: Cross-System Normalization

Completed

  1. Created Scheduler-Agnostic Normalization Module

    • src/data/normalization.py - Comprehensive normalization functions
    • Handles PBS and Slurm scheduler differences
    • Auto-detects scheduler type from hints

  2. Normalization Capabilities

    Node State Normalization:

    • PBS: free → IDLE, job-exclusive → ALLOCATED, offline → DOWN
    • Slurm: idle → IDLE, alloc → ALLOCATED, drain → DRAINING, mix → MIXED

    Queue State Normalization:

    • PBS: started/enabled → ACTIVE, stopped/disabled → INACTIVE
    • Slurm: up → ACTIVE, down → OFFLINE, drain → DRAINING

    Job State Normalization:

    • PBS: Q → PENDING, R → RUNNING, H → HELD, F → COMPLETED
    • Slurm: PD → PENDING, R → RUNNING, CD → COMPLETED, CA → CANCELLED

    Walltime Parsing:

    • PBS format: HH:MM:SS, DD:HH:MM:SS
    • Slurm format: D-HH:MM:SS, MINUTES
    • Returns both seconds (for computation) and display string

    Resource Name Normalization:

    • ncpus/cpus/procs/ppn → cores
    • nodect/nnodes → nodes
    • ngpus/gres/gpu → gpus

  3. Added 33 New Tests

    • Scheduler detection
    • Node, queue, job state normalization
    • Walltime parsing (all formats)
    • Resource name normalization
    • Memory unit conversion
    • Full cluster data normalization

Code Example

from src.data.normalization import (
    detect_scheduler,
    normalize_node_state,
    normalize_queue_state,
    parse_walltime,
    normalize_cluster_data,
)

# Auto-detect scheduler
scheduler = detect_scheduler({"scheduler": "PBS"})

# Normalize states
node_state = normalize_node_state("job-exclusive", scheduler)  # "ALLOCATED"
queue_state = normalize_queue_state("started", scheduler)  # "ACTIVE"

# Parse walltime
seconds, display = parse_walltime("24:00:00")  # (86400, "1 day")

# Normalize entire cluster data structure
normalized = normalize_cluster_data(raw_cluster_data)

AI-Assist Readiness Progress

Updated Score: 9/10 (up from 8/10)

Criterion Status Notes
Structured JSON output ✅ Good foundation
Consistent field naming ✅ Standardized in schemas and code
Explicit types/units ✅ x-unit extension, field naming
Semantic annotations ✅ Enums with descriptions
Example corpus ✅ 4 examples in examples/
Schema definitions ✅ schemas/ with JSON Schema + OpenAPI
Error categorization ✅ Standard error codes and format
Deterministic output ⚠️ Timestamps vary (inherent to monitoring)
Severity-based alerts ✅ CRITICAL/WARNING/INFO/SUGGESTION
Actionable insights ✅ action_description, action_command
OpenAPI specification ✅ Full API documentation
Cross-scheduler normalization ✅ NEW: PBS/Slurm state mapping
Walltime parsing ✅ NEW: All formats supported

Iteration Log

Iteration Date Focus Key Outcomes
1 2026-01-29 Initial Assessment Identified 13 improvement opportunities, established baseline
2 2026-01-29 Schema Foundation Created schemas/, examples/, glossary; AI readiness 4→6/10
3 2026-01-29 Code Integration Updated models with enums, capacity/availability; 15 new tests
4 2026-01-29 Recommendations Enhanced insights with severity, actions; AI readiness 6→7/10
5 2026-01-29 API Documentation OpenAPI spec, error schema, 2 more examples; AI readiness 7→8/10
6 2026-01-29 Cross-System Normalization Scheduler-agnostic normalization module; 33 new tests
7 2026-01-29 UI/UX Improvements Tooltips, contextual help, visual indicators for researchers

Summary of Changes Made

Files Created

  • schemas/README.md - Schema documentation
  • schemas/openapi.yaml - OpenAPI 3.1 specification (NEW)
  • schemas/common/units.schema.json - Unit definitions
  • schemas/common/status-enum.schema.json - Status enumerations
  • schemas/common/timestamp.schema.json - Timestamp formats
  • schemas/models/system-status.schema.json - System status model
  • schemas/models/resource-pool.schema.json - Capacity vs availability
  • schemas/models/queue-info.schema.json - Queue information
  • schemas/models/allocation-info.schema.json - Allocation tracking
  • schemas/models/storage-info.schema.json - Storage capacity
  • schemas/models/system-insight.schema.json - Recommendations/alerts
  • schemas/api/fleet-status-response.schema.json - Fleet status API
  • schemas/api/cluster-usage-response.schema.json - Cluster usage API
  • schemas/api/health-response.schema.json - Health check API
  • schemas/api/error-response.schema.json - Error response format (NEW)
  • src/data/normalization.py - Scheduler-agnostic normalization (NEW)
  • tests/unit/test_normalization.py - 33 normalization tests (NEW)
  • examples/README.md - Examples documentation
  • examples/01-check-fleet-status.md - Fleet status example
  • examples/02-check-allocation.md - Allocation checking (NEW)
  • examples/03-find-best-queue.md - Queue selection (NEW)
  • examples/20-ai-where-to-run.md - AI assistant example
  • docs/glossary.md - HPC terminology glossary

Files Modified

  • src/data/models.py - Added enums, capacity/availability models, enhanced fields
  • src/insights/recommendations.py - Severity-based insights, better thresholds
  • tests/unit/test_models.py - 15 new tests for new functionality
  • tests/unit/test_recommendations.py - Updated for new metric names

Total New Artifacts

  • 24 files created
  • 4 files modified
  • ~4200 lines of schema/documentation/code added
  • 76 tests passing (33 new normalization + 15 new model + existing)

Iteration 7: UI/UX Improvements for Researchers

Focus

Improve the frontend UI/UX for clarity and better understanding of the data for researchers, scientists, and practitioners.

Completed

  1. Added Comprehensive Tooltips Across All Pages

    Fleet Status (index.html):

    • Total systems: "Number of HPC systems being monitored across all sites"
    • Fleet uptime: "Percentage of systems with UP status. Systems may be DOWN, DEGRADED, or in MAINTENANCE."
    • Systems not UP: "Count of systems that are DEGRADED (reduced capacity), in MAINTENANCE, or completely DOWN"
    • Last observed: "When status data was last collected from the HPC systems"
    • Status breakdown: "UP=operational, DEGRADED=reduced capacity, MAINTENANCE=planned work, DOWN=offline"
    • Site coverage: "Distribution of systems across data centers (DSRCs or research facilities)"
    • Scheduler types: "PBS and Slurm are job schedulers that manage workloads on HPC systems"
    • Table headers with help icons explaining each column

    Queue Health (queues.html):

    • Connected clusters, Queues observed, Running/Pending jobs
    • Queue disposition explanation
    • Fleet core utilization
    • Node availability table with column explanations
    • Queue depth with queue type descriptions

    Quota Usage (quota.html):

    • Connected clusters, Total allocations, Hours used/remaining
    • Core-hour explanations ("One core-hour = 1 CPU core running for 1 hour")
    • Subproject table with column tooltips
    • Queue snapshot explanation

    Storage (storage.html):

    • Filesystem count, Near capacity warnings
    • Storage type explanations ($HOME, $WORK, $SCRATCH)
    • Filesystem table columns with usage threshold explanations
    • Storage warning recommendations (critical, high, elevated)

    Insights (insights.html):

    • Insight counts and severity explanations
    • Severity descriptions mapped to insight items

  2. Improved Visual Status Indicators (Accessibility)

    Status Badges with Icons:

    • UP: Green with checkmark (✓)
    • DOWN: Red with X mark (✗)
    • DEGRADED/MAINTENANCE: Orange with warning triangle (⚠)
    • UNKNOWN: Yellow with question mark (?)

    Storage Status with Icons:

    • HEALTHY: Checkmark
    • WARNING: Warning triangle
    • CRITICAL: X mark

    Queue Chips with Visual Indicators:

    • Active: Play symbol (▶)
    • Backlog: Hourglass (⏳)
    • Idle: Em dash (—)

    Allocation Status Badges:

    • HEALTHY, LOW, CRITICAL, EXHAUSTED with appropriate icons

  3. Enhanced CSS for Help Elements

    • Consistent styling for .card-tooltip, .panel-help, .eyebrow-help, .th-help
    • Hover effects with accent color highlighting
    • Smooth opacity transitions

  4. Contextual Recommendations in Storage Warnings

    • Critical (>95%): "May cause job failures. Clean up files immediately."
    • High (90-95%): "Consider removing unused files or moving data to archive."
    • Elevated (80-90%): "Monitor and plan cleanup."

  5. Enhanced Insights with Severity Context

    • Severity descriptions available on hover
    • Metric explanations for common metrics
    • Visual icon differentiation by severity level

Files Modified

HTML:

  • web/index.html - Added tooltips to cards, panels, and table headers
  • web/queues.html - Added tooltips to all metrics and tables
  • web/quota.html - Added tooltips with core-hour explanations
  • web/storage.html - Added storage type explanations and thresholds
  • web/insights.html - Added severity explanations

CSS:

  • web/assets/css/styles.css - Added help icon styles, improved badge icons, queue chip icons, allocation status badges

JavaScript:

  • web/assets/js/quota.js - Added table header tooltips, metric explanations
  • web/assets/js/storage.js - Added contextual recommendations to warnings
  • web/assets/js/insights.js - Added severity and metric explanations

UI/UX Improvements Summary

Improvement Benefit for Researchers
Tooltips on all metrics Understand HPC terminology without leaving page
Icon+color status indicators Accessibility improvement, quick scanning
Core-hour explanations Clarifies allocation consumption
Storage threshold guidance Actionable recommendations
Queue type descriptions Understand batch/debug/gpu queue differences
Severity context on insights Prioritize actions appropriately

Iteration 7 Continued: Help Panel and Data Freshness

Additional Features:

  1. Global Help Panel (page-utils.js)

    • Help button (?) added to all pages
    • Modal panel with HPC quick reference:
      • System status definitions
      • Resource concepts (core-hours, nodes, cores, walltime)
      • Queue states
      • Storage types
      • Tips for researchers
    • Keyboard accessible (Escape to close)
    • Backdrop click to close

  2. Relative Time Display

    • "Last updated" shows human-friendly times: "Just now", "5 minutes ago"
    • Hover reveals exact timestamp
    • Applied to all pages

  3. Data Freshness Indicators

    • Color-coded: fresh (green), aging (yellow), stale (orange)
    • Pulsing animation for fresh data
    • CSS: .freshness-indicator with .fresh, .aging, .stale variants

  4. Quick Tips Panels

    • Dismissible tips on each page with contextual guidance
    • Page-specific tips for Fleet, Queues, Quota, Storage, Insights
    • Persisted dismissal in localStorage

  5. Enhanced CSS Components

    • Empty state styling
    • Sortable table header indicators
    • Page context banners
    • Keyboard shortcut styling

Files Modified (Iteration 7 total)

HTML:

  • web/index.html, web/queues.html, web/quota.html, web/storage.html, web/insights.html

CSS:

  • web/assets/css/styles.css - ~200 lines added for new components

JavaScript:

  • web/assets/js/page-utils.js - Added initHelpPanel, initQuickTips, formatRelativeTime, createFreshnessIndicator, createEmptyState
  • web/assets/js/app.js, quota.js, storage.js, queues.js, insights.js - Integrated new features

Remaining Work (Future Iterations)

  1. API Versioning: Add /api/v1/ prefix for stable API
  2. Pagination: Add limit/offset for large list responses
  3. Historical Trending: Store and expose status history
  4. Alerts Endpoint: /api/alerts for webhook integration

This document is maintained as part of the Ralph Loop continuous improvement process.