project-proposal-2025

Title: FlashQueue

Abstract

Small service-based businesses such as salons, restaurants, nail spas, etc., often face issues related to unmanaged customer queues. For example, waiting for a table at a restaurant without a proper queueing system can lead to frustration and potential loss of customers.

FlashQueue is a virtual queuing system that enables customers to create and track their position in line in real time. It is a lightweight, cross-platform, queue management solution tailored specifically for small businesses. Customers can join a digital queue via a tablet at the premises or by scanning a QR code to access the system from their own device. They will receive live updates and notifications when it’s their turn.

The system is designed with three key quality attributes in mind: Availability, Deployability, and Scalability. FlashQueue provides a low-friction digital solution that streamlines operations, reduces wait-time anxiety, and makes queueing more transparent and efficient.

Author

• Name: Jasnoor Matharu
• Student Number: 47490165

Functionality

Overview

The complete system will support the following functionality:

Customer Interface

• Join a queue via a tablet kiosk or QR code/web link.
• View live queue position and estimated wait time.
• Receive notifications when their turn is near.
• Cancel or update queue status.

Staff Dashboard

• View current queue with customer names, time joined, and wait estimate.
• Mark customers as served, no-show, or skipped.
• Manually add or reorder entries if needed.

Admin Controls

• Set up and manage one or more business locations.
• Configure service types, duration estimates, and operating hours.
• View usage statistics and customer flow trends.

Platform & Deployment

• Mobile-friendly interface.
• Hosted on AWS using Docker.
• Optional offline/local mode for kiosk tablets.

Scope (MVP)

The MVP will include:

• Customer check-in via web/tablet.
• Real-time queue tracking.
• Staff dashboard for queue management.
• Basic browser-based notifications.
• Single-queue setup with cloud deployment.

Quality Attributes

Availability

End users (customers) rely on the system to join queues and track wait times in real time. If the app is down, customers may leave or call the business directly, defeating the purpose of the system.

Businesses (e.g., restaurants, salons) use FlashQueue to manage service flow. If the dashboard isn’t available, staff must resort to manual tracking, which may result in double bookings or lost queue spots.

Because the system is highly time-sensitive (e.g., for dinner reservations or haircut appointments), even brief downtimes can negatively impact the user experience and reduce trust in the system.

Deployability

Service-based businesses operate on tight schedules and can’t afford disruptions during system updates.

Frequent bug fixes and feature rollouts must be deployed without downtime or compromising existing queue states.

As FlashQueue may be adopted across multiple locations, it is essential to ensure consistent, safe deployment practices across all environments.

Scalability

High-traffic periods (e.g., weekends, evenings) are common in this domain.

If the system fails or slows down under heavy usage, customers and businesses will become frustrated, likely abandoning the system.

Scalability ensures that FlashQueue can support growth — more users, more businesses, and even different sectors (e.g., clinics, dentists) — without needing architectural redesigns.

Evaluation

Availability

Method:

• Set up uptime monitoring using UptimeRobot or Pingdom.
• Define standard business hours (e.g., 9 AM – 9 PM).
• Simulate user actions: joining a queue, refreshing queue status, marking customers as served.
• Measure uptime over a 3-day period.
• Track response times.

Success Criteria:

• ≥ 99.9% uptime during business hours.
• No critical service disruptions.
• 95% of interactions return responses within 2 seconds.

Deployability

Method:

• Use Docker and set up CI/CD pipelines (e.g., GitHub Actions or GitLab CI) for automated deployments.
• Perform blue-green deployments or rolling updates using a dummy version of the system.
• Monitor for disruptions (e.g., queue data loss, broken API responses).
• Measure deployment time and number of manual steps required.

Success Criteria:

• Full deployment completes in under 5 minutes.
• No downtime or visible disruptions during deployment.
• No user data loss or queue inconsistencies.

Scalability

Method:

• Use a load-testing tool such as Apache JMeter, Artillery, or Locust.
• Simulate:
  • 5,000 concurrent users interacting with the system.
  • 500 businesses using their dashboards.
• Gradually increase load and monitor metrics: CPU usage, memory, database performance, latency.

Success Criteria:

• System handles 5,000 concurrent users without crashing.
• Average response time remains under 3 seconds under load.
• Server auto-scales effectively (if enabled) without failing requests.