Skip to main content

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:

NFRTarget MetricBusiness PriorityArchitectural Option(s)Cost/ComplexityDecision / Comments
Availability99.9% uptimeHigh (risk-management)Multi-zone K8s, active-active DB clusteringHighAccept cost; build in this from day-1
Response Time<2 s 95% of requestsMedium (user satisfaction)Cache hot queries, use async services for heavy tasksMediumGood to have; invest where major benefit
MaintainabilityTime to deploy changes <1 weekHigh (cost-saving, simplification)Modular microservices, CI/CD full automationMedium-highCritical given modernization roadmap
PortabilityDeploy to bare-metal & cloud with same codeMediumContainerize + IaCMediumImportant for future flexibility
SecurityNo major incidents, compliance audit passHigh (risk-management)IAM, encryption, API gateway, secure coding standardsHighNon-negotiable from day-1
ScalabilityScale to 10× current load in 2 yrsMediumStateless services, auto-scaling groups, sharding strategyMediumDesign for growth but optimize cost now
Labels:

Comments

Post a Comment

Popular posts from this blog

Mastering Trade-Off Analysis in System Architecture: A Strategic Guide for Architects

 In system architecture and design, balancing conflicting system qualities is both an art and a science. Trade-off analysis is a strategic evaluation process that enables architects to make informed decisions that align with business goals and technical constraints. By prioritizing essential system attributes while acknowledging inevitable compromises, architects can craft resilient and efficient solutions. This enhanced guide provides actionable insights and recommendations for architects aiming to master trade-off analysis for impactful architectural decisions. 1. Understanding Trade-Off Analysis Trade-off analysis involves identifying and evaluating the conflicting requirements and design decisions within a system. Architects must balance critical aspects like performance, scalability, cost, security, and maintainability. Since no system can be optimized for every quality simultaneously, prioritization based on project goals is essential. Actionable Insights: Define key quality ...
Post a Comment
Read more

Virtual environments in python

 Creating virtual environments is essential for isolating dependencies and ensuring consistency across different projects. Here are the main methods and tools available, along with their pros, cons, and recommendations : 1. venv (Built-in Python Virtual Environment) Overview: venv is a lightweight virtual environment module included in Python (since Python 3.3). It allows you to create isolated environments without additional dependencies. How to Use: python -m venv myenv source myenv/bin/activate # On macOS/Linux myenv\Scripts\activate # On Windows Pros: ✅ Built-in – No need to install anything extra. ✅ Lightweight – Minimal overhead compared to other tools. ✅ Works across all platforms . ✅ Good for simple projects . Cons: ❌ No dependency management – You still need pip and requirements.txt . ❌ Not as feature-rich as other tools . ❌ No package isolation per project directory (requires manual activation). Recommendation: Use venv if you need a simple, lightweight solut...
Post a Comment
Read more

Building a Simple Text Generator: A Hands-on Introduction

Introduction Text generation is one of the most exciting applications of Natural Language Processing (NLP) . From autocorrect and chatbots to AI-generated stories and news articles , text generation models help machines produce human-like text. In this blog post, we’ll introduce a simple yet effective text generation method using Markov Chains . Unlike deep learning models like GPT, this approach doesn’t require complex neural networks—it relies on probability-based word transitions to create text. We’ll walk through: ✅ The concept of Markov Chains and how they apply to text generation. ✅ A step-by-step implementation , fetching Wikipedia text and training a basic text generator. ✅ Example outputs and future improvements. The Concept of Markov Chains in Text Generation A Markov Chain is a probabilistic model that predicts future states (or words) based only on the current state (or word), rather than the full sentence history. How it works in text generation: 1️⃣ We analyze a gi...
Post a Comment
Read more