NFRs and how to tackle them?

1. Decision Framework for NFRs

Here’s a step-by-step process you can follow:

1. Elicit and quantify the quality attributes / NFRs

   • Gather from stakeholders (business, operations, security, compliance) what qualities they need: e.g., “system must respond within 2 seconds 95% of time”, “uptime 99.9%”, “must support 1000 concurrent users”, “must be maintainable by small team for 10+ years”, etc.

   • For each NFR, aim for a measurable target (so you can trade-off).

   • Use a structured taxonomy: e.g., from ISO/IEC 25010:2011: reliability, performance efficiency, security, maintainability, portability etc. jageshwartripathi.blogspot.com

2. Assess constraints and context

   • What is the environment? (Bare-metal, K8s on OpenStack, VMware Cloud) — you know your deployment scenarios.

   • What is legacy/obsolescence risk? What existing systems are you integrating with?

   • What is the budget/time-to-market? What organisational capabilities (DevOps, SRE, monitoring) are in place?

   • What regulatory/compliance requirements apply?

3. Prioritise NFRs

   • You cannot optimise for everything; trade-offs are inevitable.

   • Use stakeholder priorities: for you you said cost-saving, simplification, risk-management are key.

   • Map NFRs to those priorities (for instance: maintainability and simplicity support cost-saving; reliability and security support risk-management).

   • Decide: which NFRs are must haves (non-negotiable), which are nice to have, which are optional.

4. Analyse architectural implications

   • For each NFR / quality attribute, what architectural patterns/tactics support it? E.g., for high availability you might choose active-active deployment, fault-tolerance, microservices; for performance you might choose caching, asynchronous processing, etc. The blog mentions “tactics” as specific design decisions. jageshwartripathi.blogspot.com

   • Evaluate cost, risk, complexity of each design option.

5. Make trade-offs and document decisions

   • Use a decision matrix: list NFRs, target metrics, architectural options, cost/benefit, risk, dependencies.

   • Document why you made the choices (so future architects & maintainers understand).

   • Revisit decisions as system and business context evolve (especially relevant in your modernization roadmap).

6. Validate & monitor

   • Once built, validate that the system meets the NFR targets (via testing, monitoring, KPIs).

   • Monitor drift (e.g., new features may degrade performance, or tech debt may erode maintainability).

   • Use the metrics to feed back into the architecture.

 

2. Key Deciding Factors or Criteria

When deciding which NFRs to emphasise, use these criteria:

Criterion	What to Ask	Why it matters
Business impact	What happens if this attribute fails (e.g., slow performance, outage, security breach)?	Helps prioritise based on risk & value.
Cost/effort	What is the cost (development, infrastructure, operational) of achieving a given target?	Ensures you choose feasible goals.
Operational capability	Do we have people/processes/monitoring to sustain this attribute?	A tall target without ops capability may fail.
Time-to-market	Does achieving this attribute delay delivery significantly?	Sometimes you must sacrifice or phase in quality.
Technical debt/legacy burden	Are there legacy constraints that make achieving this attribute hard?	You may need to plan for modernization or some “good enough” trade-off.
Scalability/future growth	Will the system need to grow in load/users/features?	Ensures you aren’t building for today only.
Compliance/regulatory risk	Does meeting/regulating this attribute avoid legal/regulatory risk?	Sometimes non-negotiable (e.g., security, audit logging).
Maintainability/obsolescence	What is the expected lifecycle, how easy will it be to change/extend?	Over your modernization roadmap (2025+), maintainability becomes critical.

3. Choices and Architectural Implications

Here’s how some common NFRs map to architectural decisions, especially in your very landscape:

• Performance / Efficiency

  • How many concurrent users, what response times, what throughput?

  • Choices: caching layers (in-memory, CDN), asynchronous messaging, microservices to split hot paths, vertical/horizontal scaling, denial of “single large monolith” if that impedes performance.

  • For your stack: e.g., front-end Angular + back-end SpringBoot — might choose reactive/non-blocking APIs, use a caching tier (Redis/Memcached), use asynchronous queues (Kafka, RabbitMQ).

• Reliability / Availability / Fault Tolerance

  • What is the acceptable downtime? What happens on failure of component?

  • Choices: multi-region deployment, active-active vs active-passive, microservices to isolate failure, circuit breakers, graceful degradation.

  • For your deployment: if using K8s on OpenStack/VMware, choose cluster failover, node pools, automated self-healing.

• Security

  • What threats exist (data breach, insider threat, regulatory compliance)? What level of risk is acceptable?

  • Choices: encryption at rest/in transit, zero-trust architecture, least-privilege access, identity & access management, security testing.

  • Given your portfolio: multiple systems (Java, PHP, legacy), you might emphasise a centralized identity service, API gateway, secure data flows.

• Maintainability / Modifiability

  • How easy is it to change/extend the system? What is expected lifespan?

  • Choices: modular architecture, clear separation of concerns, use of domain-driven design, decoupling, clean code, automated tests, microservices vs monolith trade-off.

  • Because you mentioned portfolio simplification and cost-saving, invest in maintainability reduces long-term cost.

• Portability / Deployability

  • How easy is the system to deploy in different environments (bare-metal, cloud, hybrid)?

  • Choices: containerization (Docker/K8s), infrastructure as code (Terraform/Ansible), standardize deployment pipelines, avoid heavy vendor lock-in.

  • Given your stack: you already have bare-metal, K8s, VMware — portability/consistency in deployment matters.

• Scalability / Capacity

  • How will load increase? What is expected growth?

  • Choices: design for horizontal scaling, stateless services, sharding, partitioning, elasticity.

  • For your large document-archive scenario (183k docs, 8k new each year) the knowledge graph/content-management system needs to scale gracefully.

• Usability / Operability / Supportability

  • How easy is it for users/operators to use/support the system?

  • Choices: good UI/UX, logging/monitoring, dashboards, self-service, automation of operations.

  • Considering your aim: delivering more efficient publishing workflow (single-source publishing) means usability and operability are key.

• Cost / Resource Efficiency

• What is the acceptable cost (capital & operational)? What is the resource footprint (CPU, memory, licences)?

• Choices: serverless vs dedicated, cloud vs on-prem, rightsizing infrastructure, shared services.

• Since cost-saving is a priority, you might emphasise resource efficiency and reuse.

4. Putting It All Together – Example Decision Table

Here’s a simplified example of how you might build a table to guide architecture decisions:

NFR	Target Metric	Business Priority	Architectural Option(s)	Cost / Complexity	Decision / Comments
Availability	≥99.9% uptime	High	Multi-AZ deployment, health probes, auto-healing, DB failover	Medium	Default baseline; consider multi-region only for critical services
Reliability	MTTR <30 min; no data loss	High	Replication, retries/idempotency, circuit breaker, backups	Medium	Required for transactional domains
Performance	p95 <2 s; throughput ≥X rps	Medium	Caching, async processing, read replicas, optimized queries	Medium	Optimize hotspots based on telemetry
Scalability	10× load in 2 yrs	Medium	Stateless services, autoscaling, sharding, event-driven	Medium	Design elastic; scale components independently
Security	Zero critical vulns; audit pass	High	IAM, encryption, secrets mgmt, API gateway, DevSecOps	High	Non-negotiable baseline
Maintainability	Deploy <1 week; low MTTR	High	CI/CD, observability, standard frameworks, IaC	Medium	Key modernization driver
Modifiability	Feature change <2 weeks	Medium	Modular services, clean interfaces, feature flags	Medium	Enables roadmap agility
Interoperability	Std APIs; versioned contracts	Medium	API-first, canonical model, adapters/events	Medium	Required for ecosystem integration
Testability	≥80% auto tests; env parity	Medium	DI, contract tests, service virtualization, CI	Medium	Needed for safe delivery
Cost Efficiency	Unit cost ↓ YoY	High	Autoscale, right-size, serverless where fit, tiered storage	Medium	Govern via FinOps metrics
Portability	Cloud & on-prem deploy	Medium	Containers, Kubernetes, IaC	Medium	Constrain to justified workloads