For centuries, maps have helped us to traverse and understand a territory. Olden maps marked unknown areas with “here be dragons”, but in the case of shadow IT and microservices, “here be risk” might be more accurate. To reduce risk and ensure we understand the relationship between entities, organizations need to think seriously about how they manage application dependencies.
What is application dependency mapping?
Application dependency mapping (ADM) is the process of identifying, tracking, and visualizing the complex relationships between different technological components within an organization's IT environment. It gives you a comprehensive blueprint of your software, revealing how various applications, services, microservices, and infrastructure nodes interact within a system. This can clarify and simplify workloads, providing you with context and clarity from individual IT assets like to cloud usage in Azure and AWS usage further up the IT ecosystem. When done in real time, ADM helps identify vulnerabilities and flaws in these interdependencies, preventing system failures.
Modern systems are characterized by interdependencies and dynamic environments.
Applications today rely on multiple microservices, APIs, databases, and more, creating kaleidoscopes of interaction that shift constantly. Changes to individual components can cascade through a system, creating new and novel interdependencies that must be understood.
IT systems are both deeply interconnected and perpetually in flux. Today’s IT operations require containerization, software updates, CI/CD and dynamic integration of third party tools creating a dynamic environment. New tools will typically create functionality in the short-term and entropy in the medium-term. Some engineers still prefer a “move fast and break things” approach to solving problems, so rather than tie them in red tape and strangle innovation, it makes sense to rationalize and review your topology on the regular.
Getting to grips with this integrated, ever-changing environment means doing real-time mapping based on a solid technical foundation. This starts with three components.
Discovery mechanisms
These are the application dependency mapping tools and processes that you need to identify and catalog relationships between applications and infrastructure components. They automate discovery of dependencies to work effectively in a dynamic environment. Examples include:
Agent-based discovery: lightweight software agents installed on servers or endpoints to collect data on application-level interactions, traffic and configurations.
Network scanning: tools like Hollywood favorite Nmap that capture real-time traffic patterns or send targeted requests to network devices at the level of IP addresses, mapping interactions and uncovering latent dependencies.
API integration: native APIs from cloud and infrastructure providers that retrieve configuration and connectivity metadata. These tools are highly accurate but localized.
Benefits of application dependency mapping
Modern software environments are fluid and dynamic, defined by cloud migrations, DevOps transformations and a constant emphasis on agility and speed. It helps to see the glass as half full and view this as a strength, rather than a source of vulnerability and technical debt. Application dependency mapping allows you to thrive in this landscape, reducing bottlenecks and downtime for developers. This ultimately creates a culture of speed that drives business outcomes.
ADM can help you to spot issues in advance and reduce disruptions, say disaster recovery caused by data center issues. But it can also be a positive enabler of operational tasks like change management, audits and ambitious IT projects.
Some benefits include:
Improved incident response: clunky incident response leads to longer outages and more downtime, which can cost up to $9,000 per minute for large organizations. Building a map that reflects your IT topology makes incident response faster and more efficient. These improvements should support IT operations management while reducing mean time to detect (MTTS) and mean time to resolve (MMTR) as well as saving on incidents.
Enhanced risk management: knowing your systems makes it easier to anticipate risks as well as respond to issues. By combining ADM with observability tools you can proactively detect anomalies, moving from reactive troubleshooting to proactive assessment. This reduces the time needed to detect and mitigate vulnerabilities.
Better resource allocation: ADM supports better asset management by reducing redundancy and optimizing resource utilization. Incorporate a pricing review to keep operations lean and effective. This help the bottom line on infrastructure investment, use of computational resources and cloud costs.
Streamlined cloud migrations: moving from on-premises to cloud-first and hybrid creates new interdependencies and obscures existing ones. Mapping can help you to navigate this, smoothing transitions with minimal disruption. Look for this improvement in reduced migration success rate downtime post-migration.
Regulatory compliance: real-time maps of data movement enables compliance and helps you to stay on top of alphabet soup like GDPR, HIPAA or CCPA by guaranteeing data protection. It also helps to facilitate data residency and sovereignty requirements, and align with security baselines (NIST, ISO 27001, SOC 2).
How does application dependency mapping work?
While the benefits of ADM are clear and the concept is straightforward, getting it right is hard. Best practice can vary between environments, but it should always aspire to real-time usage and be linked to technical and business outcomes. Let’s consider the different techniques used.
Sweep and poll
This traditional approach to ADM involves systematically scanning the network (sweep) and querying network resources (poll) to identify connections and dependencies. The approach is minimally invasive, operating at regular time intervals and drawing from standard network protocols (SNMP, WMI, ICMP). It is cost-effective and useful for stable environments, but doesn’t offer real-time visualization and can miss transient or ephemeral connections like containers or microservices. Consider it as a useful component, but not job done.
Agent-based monitoring
Agent-based methods involve deploying lightweight software agents directly on servers, applications or and endpoints. These constantly gather telemetry data on queries, resource usage and API calls, allowing you to build a dynamic model of dependencies based on process-level interactions. This use case offers more detail in real-time, but is resource-heavy, particularly in the form of computational overhead and agent maintenance.
Configuration file parsing
This method analyzes configuration files to understand application performance and relationships, particularly during the initial discovery phase of ADM. Using a static analysis of configuration files, deployment scripts and infrastructure-as-code, it can identify connections even in containerized and cloud-native architectures. By parsing files there is no runtime overhead, but it requires constant configuration management and still delivers a static snapshot of dependencies.
Orchestration platforms
This involves mapping based on native integrations with Kubernetes, AWS ECS, or Docker Swarm. Integrations provide accurate, real-time data that captures service interactions within containerized environments. While this is the most effective approach it is also the most narrow one, and can be less effective in hybrid or legacy environments.
AI/ML-driven analysis
Artificial intelligence and machine learning can analyse systems to detect dependencies, as well as any associated anomalies. Typically drawing from logs and telemetry, these algorithms can surface complex or evolving relationships, uncovering novel dependencies unavailable by other methods. While the ceiling for this approach is high, it requires significant resources in practice and can be painstakingly slow to implement.
Best practices for effective dependency mapping
Accurate mapping is hard enough without the territory changing in real-time. When you add in challenges with siloed teams running different processes, tool sprawl and the inherent complexities of multi-cloud environments, workflows get tricky. If you’re looking to optimize ADM then consider these tips.
Automate discovery
Dynamic environments are in constant flux, making most of what you discover manually obsolete. You don’t go surfing based on last month’s forecast. Use automation through tools like agent-based monitoring or orchestration platform integrations to keep up to date. Supplement these granular metrics with regular sweeps or scans to uncover structural changes in legacy systems.
Integrate with ITSM (IT Service Management)
Dependencies offer crucial context for processes like incident response and capacity planning. By integrating ADM you get more bang for your buck, helping your company’s bottom line and justifying investment. Extend ADM integration to IT operations management platforms like ServiceNow and Jira in the first instance to align system insights with broader company goals.
Focus on visualization
Maps need to communicate the underlying territory quickly and clearly, but dependency data in real-time can be overwhelming. Use simple, intuitive visual representations to get a broad perspective of your IT territory before diving into details. These should be color-coded and accessible for both technical and non-technical stakeholders.
Adopt an Internal Developer Portal (IDP)
When it comes to centralizing, parsing and acting on complex operational data, go with the experts. By embedding ADM into your Internal Develop Portal you can monitor dependencies in real-time with the crucial context of DevOps and engineering data. Your IDP should allow you to put these insights to work in eliminating waste, identifying risk and improving developer productivity.
Foster collaboration
Because the benefits of ADM stretch beyond IT teams to compliance, security, finance and more, it’s important to get buy-in from these stakeholders. Pick your champions. Dependency maps should be widely reviewed and debated, with findings spelled out in non-technical terms for relevant parties. This should be communicated in terms of investment, risk and ownership between business units.
Prioritize security
Dependencies can create vulnerabilities and novel attack vectors, especially in third-party integrations or outdated components. Ensure that ADM is an enabler of good security hygiene in governance and operations through dependency-aware security policies and proactive threat detection. Mapping has benefits beyond IT and should be seen as a driver of value across business functions.
Continuous improvement
Application dependency mapping is an ongoing, real-time task that is a journey and not a destination. It might be the clearest example of continuous improvement in software. Ensure that you are regularly auditing and updating your methodologies, working closely and proactively with stakeholders across the business and staying on top of new and relevant tools. Consider the value to the business in terms of investment and risk at all times.
Streamline application dependency mapping with Cortex
Cortex's Internal Developer Portal has several features that allow it to play a central role in your ADM processes, by supporting the mapping process as well as by visualizing and communicating its insights. In line with the Cortex Engineering Maturity Curve there are many ways that you can add value.
Centralized service catalog: this comprehensive catalog offers centralized visibility into services, applications and their dependencies. It complements tools focusing on infrastructure or code, aggregating data on dependencies into a unified view tailored to developers’ needs.
Automated integrations: Cortex integrates with tools such as monitoring systems, CI/CD pipelines and cloud providers to consolidate data on dependencies. By working with tools specific to integration, the IDP enhances the accuracy and timeliness of dependency maps.
Customizable scorecards: this feature allows you to define and enforce standards across compliance, operations and engineering. Cortex allows you to use Scorecards as a bridge between ADM and the organizational goals it supports, baking insights into targets for stakeholders.
Engineering intelligence: this feature supports ADM by twinning insights from discovery with actionable insights that can be readily communicated and managed. By aggregating and contextualizing these insights, Engineering Intelligence turns detailed analysis into clear actions.
Developer self-service tools: features like Scaffolder empower developers to create and manage services indecently, ensuring new dependencies are documented and integrated into the system architecture. This streamlines dependency management as they grow and evolve, complementing monitoring, mapping and scanning solutions.
Mapping dependencies is an ongoing technical challenge with wide-ranging benefits to your company’s bottom line.
Ready to streamline dependency mapping? Schedule a demo today to see how Cortex can help.
