Double Fairness Learning (DFL) in ML

• Double Fairness Learning (DFL) is a methodology that simultaneously optimizes accuracy and fairness by integrating dual fairness constraints in model training.
• DFL employs tailored loss functions and constraint optimization to balance disparate impact metrics, ensuring equitable treatment across sensitive groups.
• DFL enhances practical deployment by improving model interpretability and fairness in high-stakes environments while mitigating bias.

An application-level observability framework is a structured approach for capturing, analyzing, and acting upon rich telemetry data from within the software application layer, providing deep insight into system operation, fault behaviors, root-cause analysis, and business-impacting metrics. Modern frameworks unify multiple data modalities—including metrics, traces, logs, and semantic events—to enable real-time monitoring, anomaly detection, and interpretability at both the component and holistic system levels. This is foundational for highly distributed architectures, such as microservices, serverless, multi-agent systems, edge-to-cloud environments, and scientific pipelines, where traditional infrastructure-centric monitoring is insufficient. Below, the landscape of application-level observability frameworks is detailed, covering their architectural patterns, data and signal models, anomaly detection and RCA, platform support, and empirical evaluation.

1. Architectural Principles and Layered Designs

State-of-the-art application-level observability frameworks embrace a multi-layer architecture, modularizing core responsibilities to optimize for real-time analysis, extensibility, and tooling interoperability:

• Monitoring & Logging Layer: Instrumentation hooks are inserted within the application to record events, traces, and structured logs. This can be achieved through source-level SDKs (e.g., OpenTelemetry, Glowroot, custom Python/Java agents), bytecode-injection (e.g., Kieker agents, POBS Docker augmentation), or platform-native mechanisms (e.g., serverless platform hooks, SLURM job script injection) (Solomon et al., 17 Aug 2025, Zhang et al., 2019, Yang et al., 12 Mar 2025, Albuquerque et al., 3 Oct 2025, Balis et al., 2024, Araujo et al., 2024).
• Data Collection and Transport: Telemetry is buffered, serialized (binary, JSON, Avro), and transported using an asynchronous, decoupled model to minimize overhead and avoid blocking the main application workflow. Transport layers employ message queues (Kafka, JMS), direct socket or HTTP/gRPC endpoints, and conform to standards such as OTLP (Yang et al., 12 Mar 2025, Albuquerque et al., 3 Oct 2025, Balis et al., 2024).
• Storage and Indexing: Metrics are typically stored in TSDBs (Prometheus, InfluxDB), logs in search indices (Elasticsearch, OpenSearch), and traces in distributed tracing backends (Jaeger, Zipkin). Some frameworks build additional knowledge graphs or causal graphs on top of the raw signals to facilitate incident response and risk analysis (Ben-Shimol et al., 2024, Hou, 8 Sep 2025).
• Analysis & Visualization: Real-time and batch analytics pipelines compute aggregations, anomaly scores, and enable visual exploration (Grafana dashboards, 3D city metaphors in ExplorViz, Jupyter DataFrame analysis) (Yang et al., 12 Mar 2025, Balis et al., 2024, Araujo et al., 2024).
• Adaptation and Feedback (where relevant): Some frameworks (especially for edge/cloud continuum and adaptive systems) implement SLO-aware controllers that automatically adapt system configuration in response to telemetry (e.g., scaling, knob tuning, or model switching) (Sidi et al., 21 Jan 2026).

A consistently observed architectural pattern is strict separation between low-level event/trace instrumentation, centralized storage/processing, and high-level interpretation/alerting layers.

2. Telemetry Modalities and Data Modeling

Application-level observability synthesizes diverse signal types that can be integrated and correlated:

Modality	Capture Layer	Examples of Data
Metrics	Application/infra agents	Latency histograms, throughput, error counts, domain counters (Albuquerque et al., 3 Oct 2025, Yang et al., 12 Mar 2025, Balis et al., 2024, Sidi et al., 21 Jan 2026)
Traces	Spans/contexts in code	Request chains across service boundaries, span durations, call graphs (Albuquerque et al., 3 Oct 2025, Yang et al., 12 Mar 2025, Borges et al., 2021)
Logs	Structured/binary event logs	Discrete events, errors, state changes, text outputs (Ben-Shimol et al., 2024, Araujo et al., 2024, Solomon et al., 17 Aug 2025, Hou, 8 Sep 2025)
Semantic/Domain Events	Business logic layer	Custom user actions, workflow stages, LLM outputs, knowledge graph triples (Solomon et al., 17 Aug 2025, Ben-Shimol et al., 2024)

Uniform schemas and semantic conventions are critical for cross-signal analysis. Advanced frameworks leverage semantic ontologies (OWL2, knowledge graphs), explicit cross-modal encodings (e.g., aligning metrics/BERT log embeddings/graph-structured traces), and library-provided attribute tagging (e.g., OpenTelemetry's semantic conventions) to facilitate multi-signal join and root-cause reasoning (Ben-Shimol et al., 2024, Hou, 8 Sep 2025, Albuquerque et al., 3 Oct 2025, Shkuro et al., 2022).

3. Anomaly Detection, Classification, and Root Cause Analysis

Detecting and diagnosing faults at the application level requires advanced analytic methods capable of ingesting multimodal telemetry, learning "normal" patterns, and surfacing interpretable explanations:

• Statistical and ML-based detection: Sliding window statistics (mean, percentiles, σ-based thresholds) (Yang et al., 12 Mar 2025), LSTM-Autoencoders over both execution features and semantic embeddings (Solomon et al., 17 Aug 2025), and temporal-causal models (e.g., TCN-autoencoders with perturbation-based causal discovery in KylinRCA) (Hou, 8 Sep 2025) are prevalent.
• Fault Classification and Explanation: Observed anomalies are classified (using LLM-based prompt agents, softmax multi-task heads, rule-based prompts) into discrete categories (e.g., Bias, Hallucination, Injection, Memory Poisoning) (Solomon et al., 17 Aug 2025, Hou, 8 Sep 2025). RCA modules identify fault-propagation chains via cross-modal/cross-layer attention, graph reasoning (type-aware GAT), or programmatic walk-back over structured event logs.
• Explainable Outputs: Modern frameworks emphasize evidence chains (e.g., mask-based feature/edge attributions (Hou, 8 Sep 2025)), causal propagation graphs, and structured report synthesis for human-in-the-loop debugging or incident response.

Quantitative metrics (e.g., F1, false positive rate, detection latency, RCA accuracy) are consistently used for evaluation, with best-in-class methods achieving near-real-time detection and high interpretability (Solomon et al., 17 Aug 2025, Hou, 8 Sep 2025).

4. Implementation Patterns, Instrumentation, and Best Practices

Effective application-level observability is enabled by a range of instrumentation, automation, and integration strategies:

• Automated and Declarative Instrumentation: Approaches range from Dockerfile transformation with agent insertion (POBS) (Zhang et al., 2019), to code-level language SDKs (OpenTelemetry, Glowroot, Kieker) (Yang et al., 12 Mar 2025, Albuquerque et al., 3 Oct 2025), to serverless and scientific computing frameworks adapting environmental/context propagation (Ben-Shimol et al., 2024, Balis et al., 2024, Borges et al., 2021).
• Continuous Assurance and Experimentation: Experiment engines (OXN) inject faults and dynamically adjust observability configurations to systematically optimize detection/overhead trade-offs, enabling continuous assurance integrated with CI/CD (Borges et al., 11 Mar 2025, Borges et al., 2024).
• Schema-First and Semantic Metadata: Rich schema definition languages (e.g., Thrift with type/unit annotations) codify metrics, logs, and event structures a priori, ensuring compatibility, privacy rule enforcement, and cross-signal joinability (Shkuro et al., 2022).
• Resource and Overhead Management: Adaptive sampling, batching, and on-device aggregation are required in resource-constrained environments (e.g., IoT, fog, serverless) to balance fidelity with system impact (Araujo et al., 2024, Borges et al., 2021, Zhang et al., 2019).
• Interoperability and Extensibility: Adoption of open, vendor-neutral APIs and formats (OpenTelemetry, OpenMetrics) supports pluggable pipelines and integration with existing data platforms and visualization tools (Albuquerque et al., 3 Oct 2025, Yang et al., 12 Mar 2025, Balis et al., 2024).

5. Domain-Specific Variants and Case Studies

Frameworks are adapted to a range of target domains, each with bespoke design constraints:

• Multi-Agent Systems: LumiMAS provides agent and workflow-centric logs, semantic feature embedding, and LLM-based anomaly classification, uniquely addressing multi-agent LLM-based workflow failures (Solomon et al., 17 Aug 2025).
• Microservices and Cloud-Native: Patterns for distributed tracing, custom application/infrastructure metrics collection, and dynamic assurance loops (OXN) are prevalent (Albuquerque et al., 3 Oct 2025, Borges et al., 11 Mar 2025, Borges et al., 2024).
• Serverless and Edge/Fog Computing: Lightweight, event/log-centric telemetry, platform-supported tracing, and adaptive/resource-aware aggregation support observability in highly constrained/server-managed contexts (Ben-Shimol et al., 2024, Borges et al., 2021, Araujo et al., 2024, Sidi et al., 21 Jan 2026).
• Scientific and HPC Pipelines: Domain-specific integration of trace context, cgroup resource metrics, and DataFrame-centric analysis in Jupyter environments highlight transition of observability concepts beyond cloud-native systems (Balis et al., 2024).
• Enterprise and Large-Scale Fault Diagnosis: Cross-modal causal analysis, global RCA with explainability, and edge-to-cloud split processing define frameworks like KylinRCA for ultra-large distributed data centers (Hou, 8 Sep 2025).

6. Quantitative Evaluation, Overhead, and Empirical Outcomes

Frameworks are empirically assessed along several axes:

• Detection effectiveness (accuracy, precision, recall, F1); explained in terms of scenarios with benign and anomalous logs/fault-injection (Solomon et al., 17 Aug 2025, Hou, 8 Sep 2025, Borges et al., 11 Mar 2025, Borges et al., 2024).
• Performance and scalability (e.g., detection latency sub-0.1s, per-log inference times, throughput, overhead below 1–3% CPU) (Solomon et al., 17 Aug 2025, Yang et al., 12 Mar 2025, Zhang et al., 2019, Albuquerque et al., 3 Oct 2025, Sidi et al., 21 Jan 2026).
• Impact on fault diagnosis, MTTD, and root-cause accuracy: MTTD reductions by over 50%, RCA accuracy >80% in adversarial or production settings, as well as SLO compliance and business-process criticality improvements (Solomon et al., 17 Aug 2025, Hou, 8 Sep 2025, Ben-Shimol et al., 2024, Sidi et al., 21 Jan 2026, Araujo et al., 2024).
• Usability and analyst experience: user studies indicate strong improvements in incident response speed and accuracy when knowledge graph dashboards or integrated metrics/traces are available (Ben-Shimol et al., 2024, Solomon et al., 17 Aug 2025).

A plausible implication is that application-level observability frameworks, when implemented following these architectural and methodological principles, enable not only efficient troubleshooting and RCA but also proactive, autonomous adaptation and enhanced SLA compliance, especially in the presence of complex, cross-component, or emergent failure modes.

7. Challenges and Ongoing Directions

Several open challenges and emerging trends remain focal in research and development:

• Heterogeneity and Cross-Platform Support: Supporting heterogeneous systems (multiple languages, platforms, deployment regimes) drives the need for uniform standards and pluggable, containerizable agents (Araujo et al., 2024, Albuquerque et al., 3 Oct 2025, Balis et al., 2024).
• Scalability and Real-Time Analytics: PB-scale throughput, edge-cloud federated analytics, and incremental or online learning approaches are essential for next-generation environments (Hou, 8 Sep 2025, Sidi et al., 21 Jan 2026).
• Interpretability and Explainability: There is an increased emphasis on explaining detected anomalies, especially for security-critical and human-in-the-loop settings, with structured evidence chains and causality visualization (Solomon et al., 17 Aug 2025, Hou, 8 Sep 2025, Ben-Shimol et al., 2024).
• Privacy, Security, and Policy Compliance: Particularly in business and regulated domains, schema-first approaches and explicit telemetry annotations for PII/policy compliance are being embedded at the core of frameworks (Shkuro et al., 2022).
• Automation and Continuous Experimentation: Embedding systematic experiment-driven observability tuning, automated fault-injection, and continuously validated SLO feedback loops into standard CI/CD and SRE cycles is an active area of tool and method development (Borges et al., 11 Mar 2025, Borges et al., 2024).
• Forward-Looking Extensions: Directions such as integrating energy-efficient observability budgets, graph mining for proactive threat hunting, incorporating application-internal trace data (e.g., MPI rank-level), and extending knowledge graph-based RCA are under active investigation (Hou, 8 Sep 2025, Balis et al., 2024, Ben-Shimol et al., 2024, Sidi et al., 21 Jan 2026).

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References:

• "LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems" (Solomon et al., 17 Aug 2025)
• "The Kieker Observability Framework Version 2" (Yang et al., 12 Mar 2025)
• "Observability and Incident Response in Managed Serverless Environments Using Ontology-Based Log Monitoring" (Ben-Shimol et al., 2024)
• "Tracing and Metrics Design Patterns for Monitoring Cloud-native Applications" (Albuquerque et al., 3 Oct 2025)
• "Continuous Observability Assurance in Cloud-Native Applications" (Borges et al., 11 Mar 2025)
• "Automatic Observability for Dockerized Java Applications" (Zhang et al., 2019)
• "FaaSter Troubleshooting -- Evaluating Distributed Tracing Approaches for Serverless Applications" (Borges et al., 2021)
• "Towards observability of scientific applications" (Balis et al., 2024)
• "Observability in Fog Computing" (Araujo et al., 2024)
• "Informed and Assessable Observability Design Decisions in Cloud-native Microservice Applications" (Borges et al., 2024)
• "Application-level observability for adaptive Edge to Cloud continuum systems" (Sidi et al., 21 Jan 2026)
• "Research on fault diagnosis and root cause analysis based on full stack observability" (Hou, 8 Sep 2025)
• "Positional Paper: Schema-First Application Telemetry" (Shkuro et al., 2022)