The Missing Map: Building a Truly Unified Service View from Cloud to Data Center

By Vishal Singh

October 16, 2025
8 min read

The Missing Map: Building a Truly Unified Service View from Cloud to Data Center

By Vishal Singh

When Infrastructure Monitoring Fails

You know the drill. Your phone starts ringing at 2 A.M. Your dashboard shows a latency spike in payment-service. But is it—the database? The cache? The network? You spend the next hour frantically jumping between tools—APM, network monitors, infrastructure dashboards—mentally stitching together a system map that should already exist.

This is the gap a dynamic service map fills. But most tools show only half the picture. They reveal application traces but blind you to the infrastructure and network layers where real failures often hide.

This guide provides a technical blueprint for building a truly unified service map. We’ll cover architecture for both cloud-native and on-prem environments, dive deep into the correlation engine, and be brutally honest about the challenges. Because if it were easy, everyone would have done it already.

Part 1: Your Current Service Map Blind Spots

A true service map is the operational nervous system of your organization. It’s a live graph where nodes are components (services, databases, load balancers, physical switches) and edges are dependencies. When an incident occurs, it transforms blind searching into targeted traversal—dramatically reducing MTTR.

The Unified Monitoring Lie

Modern observability platforms unify metrics, logs, and traces. But their service maps have a critical limitation: they only see what they instrument.

In Cloud Environments: They beautifully map microservices but ignore the cloud infrastructure (load balancers, managed databases, message queues) and network paths between availability zones.
In On-Prem Environments: Visibility is even worse—split between APM, network monitoring (SNMP), and server tools, with mainframes and legacy systems often completely dark.

This fractures visibility. Teams maintain static architecture diagrams that are outdated upon creation. During incidents, engineers mentally correlate data across siloed tools—a fragile and time-consuming process.

Part 2: The Blueprint: Building Your Unified Service Map

The solution isn’t a single tool—it’s a correlation pipeline. You’ll build a unification engine that joins data from specialized sources into a coherent graph.

Data Collection: The Three Pillars of Observability

You need to collect three layers of data:

Application Layer (The “What”)

Tools: OpenTelemetry Collectors, APM Agents (e.g., Dynatrace, AppDynamics)
Data: Distributed traces containing critical dependency tags:
- db.instance=”db-prod.cluster-abc.us-east-1.rds.amazonaws.com”
- http.host=”api.payments.com”
- peer.service=”auth-service”
Purpose: Identifies logical dependencies between services and external endpoints.

Network Layer (The “How”)

Cloud: Enable VPC Flow Logs (AWS, Azure, GCP) streamed to a central aggregator.
On-Prem/Kubernetes: Deploy eBPF-based tools (Cilium, Pixie) or traditional netflow collectors(pmacct, ntopng) on routers and switches.
Data: Source/destination IPs, ports, protocols, byte counts.
Purpose: Provides the unbiased truth of all network communications, filling gaps where instrumentation is missing.

Infrastructure Layer (The “Where”)

Cloud: Poll Cloud Provider APIs (AWS Resource Tagging API, GCP Asset Inventory) to inventory resources and metadata.
Kubernetes: Poll the Kubernetes API Server for real-time pod-to-IP mappings.
On-Prem: Integrate with CMDB (ServiceNow), IPAM (Infoblox), and use discovery tools (nmap) and server agents.
Purpose: Maps IP addresses and hostnames to logical application and service names.

The Unification Engine: Technical Deep Dive

This is the core—a custom stateful stream-processing application. Its job is to consume, normalize, and join disparate data streams to build and maintain your graph.

Architecture:

Built on: Apache Flink or Spark Streaming for stateful processing, or Kafka Streams for simplicity.
Consumes from: Kafka topics (raw-traces, vpc-flow-logs, netflow-records, cmdb-snapshots).
Writes to: A graph database (Neo4j, JanusGraph) for relationship queries.
State: Maintains internal state (e.g., in RocksDB) for sliding windows of data.

Correlation Logic: A Hybrid Cloud Example

Let’s walk through how the engine discovers a connection between an on-prem service and a cloud database:

Trace Ingestion:
The engine consumes a span from raw-traces:

json

{

“service.name”: “on-prem-inventory-service”,

“tags”: {

“db.instance”: “inventory-db.cluster-abc.us-east-1.rds.amazonaws.com:5432”

}

Action: Extracts FQDN (inventory-db.cluster-abc.us-east-1.rds.amazonaws.com) and port (5432).

2. Cloud Resource Resolution:

Queries internal cache (populated by periodic AWS API calls) for the FQDN.
Cache miss triggers real-time AWS SDK call to resolve FQDN to RDS instance prod-inventory-db with IP 192.0.2.100.
Action: Creates/updates graph node:
- Node ID: resource:aws:rds:prod-inventory-db
- Properties: type: database, engine: postgresql, ip: 192.0.2.100

3. Network Validation:

- - Queries internal state (last 5 minutes of netflow data) for traffic to 192.0.2.100:5432.
  - Finds netflow record:

json

{

“src_addr”: “10.10.5.20”,

“dst_addr”: “192.0.2.100”,

“dst_port”: 5432,

“protocol”: 6

}

Proof: Network conversation confirmed.

4 On-Prem Source Resolution:

Queries CMDB cache: IP 10.10.5.20 → server inventory-server-05.prod.nyc.example.com.
Checks server agent data: finds Java process running inventory-service-latest.jar.
Action: Creates graph relationship:
- host:inventory-server-05 — [HOSTS] –> service:on-prem-inventory-service

5. Final Correlation:

- All evidence aligned: trace + netflow + CMDB + agent data.
- Action: Creates high-confidence edge:
  - service:on-prem-inventory-service — [QUERIES] –> resource:aws:rds:prod-inventory-db
  - Edge properties: protocol: tcp, port: 5432, last_seen: [timestamp]

On-Prem Correlation Example:

Netflow record shows 10.50.10.25 → 10.50.20.30:1521
Engine queries IPAM: 10.50.20.30 → oracle-db-01.prod.lab
Queries CMDB: oracle-db-01 hosts PROD_PAYMENTS database
Checks server agent on 10.50.10.25: finds payment-app process
Scans recent traces for payment-app containing connection string jdbc:oracle:thin:@oracle-db-01:1521:PROD_PAYMENTS
Action: Creates edge: service:payment-app — [QUERIES] –> database:PROD_PAYMENTS

Visualization: The Single Pane of Glass

The graph database feeds visualization tools:

Grafana with node graph panel
Custom UI with D3.js or Cytoscape.js
Commercial tools like Dynatrace or Datadog (if using their APIs)

The result: A single interactive map showing dependencies from physical servers to cloud services, updated in near-real-time.

Part 3: Why This Is So Hard: Network Monitoring Implementation Challenges

This blueprint is technically sound—but extraordinarily difficult to implement. These are the challenges that make unified service mapping a frontier problem.

The Identity Resolution Nightmare

Cloud: Ephemeral IPs (Kubernetes pods change IPs constantly). Requires perfect timestamp alignment between flow logs and Kubernetes API responses.
On-Prem: Stale data (CMDB and IPAM updates lag reality). Manual processes create inaccurate data.
Universal: Naming inconsistencies. Does APM call it user-service while CMDB calls it app-user-prod-17? Without enforced conventions, your graph splinters.

Data Volume and Velocity

VPC Flow Logs and netflow data generate terabytes daily.
Requires significant streaming infrastructure (Kafka, Flink) and smart sampling strategies.
Without filtering, your beautiful map becomes an incomprehensible hairball of noise (health checks, scans, background chatter).

Unification Engine Complexity

State Management: Must maintain sliding windows of network data while handling delayed events (using event-time processing and watermarks).
Conflict Resolution: Must implement rules for reconciling conflicting data (e.g., CMDB vs. actual discovery).
Schema Normalization: Must transform AWS API responses, netflow records, and CMDB exports into a common data model.

Organizational Silos

Network teams own flow data. Platform teams own Kubernetes. App teams own traces. Getting them to agree on metadata standards is a human challenge, not a technical one.
The “Two Problems” Paradox: You now must build and maintain a complex distributed system (the unification pipeline) whose failure means your observability fails.

Part 4: A Pragmatic Implementation Path

Given these challenges, here’s how to approach this realistically:

Start with the Network: Netflow/VPC Flow Logs don’t lie. Begin by visualizing network conversations between IPs. This alone provides massive value.
Pick One Vertical Slice: Choose one critical business flow (e.g., “on-prem order service → cloud Kafka → payment service”). Build your prototype to map just this chain.
Enforce Metadata Governance: Mandate tagging conventions (service, env, owner) across all teams and tools. This is more important than any technology choice.
Evaluate Commercial Options: Before building, see if tools like Pixie (eBPF-based), Flowmon(network-centric), or expanded use of your APM platform can get you 80% of the way.
Iterate: Build your unification engine incrementally. Start with batch correlation before attempting real-time. Prove value at each step.

Conclusion: The Journey Toward True Observability

The perfect unified service map remains the holy grail of observability. While technically achievable, the path is fraught with challenges—ephemerality in the cloud, staleness on-prem, and organizational complexity everywhere. The value isn’t in achieving perfection, but in the journey. Each step toward correlating your data silos—governing metadata, understanding network flows, mapping just one critical service—dramatically improves your operational understanding. Start small. Focus on high-value dependencies. Embrace the fact that this is a marathon, not a sprint. The destination—a living, breathing map of your entire system—is worth the effort for those with the perseverance to see it through. Your next 2 A.M. call will thank you.

Vishal Singh

VP Cloud Engineering, QualityKiosk Technologies

Vishal Singh has over 2 decades of experience in Telecom and IT, working with global leaders (Ericsson) and MTN, Airtel, Orange Group, Telefonica, Vodafone, Indosat, Telenor Asia (DTAC, Digi, TML), and Telstra. Vishal has international stints across Europe, Africa, and APAC regions, leading multi-cultural teams consisting of 300+ professionals. Vishal has rich experience in Network Operations, Planning and Design, Optimization, and support.