I started with the obvious classes and felt okay until they pushed on concurrency.
Start by clarifying requirements and scale, then define core classes and interfaces with clear responsibilities, and finally discuss concurrency and fault handling. Use a state machine for the elevator and a dispatcher for the controller, and explain trade-offs in scheduling algorithms.
Pro tip: Emphasize separation of concerns: keep the elevator as a state machine, the controller as a dispatcher, and use interfaces for extensibility. Mention that you'd start with a simple scheduling algorithm (e.g., SCAN) and iterate based on metrics.
Ask about number of elevators, floors, traffic patterns, and fault tolerance expectations. This guides design decisions like centralized vs distributed control.
Identify main entities: Elevator, Controller, Request, Floor, Button, Sensor. Specify their key attributes and methods, and how they interact via interfaces.
Describe how requests flow from buttons to controller to elevator, and how elevator state (idle, moving, doors open) is managed. Use a state pattern for elevator states.
Explain thread-safety for shared data (e.g., request queues), use of locks or concurrent collections, and fault handling (sensor failures, power outages) with redundancy and fail-safe modes.
Compare scheduling algorithms (FCFS, SCAN, LOOK) and their impact on wait time and throughput. Mention how to extend for multiple elevators, zoning, or priority requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify that the scheduler must respect the current direction of travel, so it should stop at floor 2 before floor 99 when moving upward. Explain how to use a single priority queue with a custom comparator that orders floors by direction and proximity, and describe the enqueue/dequeue operations and tie-breaking rules.
Pro tip: Mention that the comparator must be dynamic based on the current direction, and that a simple min-heap or max-heap alone won't work without direction awareness. Also, note that the space complexity is O(n) for n pending requests, and time complexity is O(log n) per operation.
Confirm that the elevator is currently moving upward and that the goal is to stop at floor 2 before floor 99. State that the scheduler must serve requests in the current direction before reversing.
Use a single priority queue (heap) with a custom comparator that orders floors based on the current direction and distance. For upward direction, floors above current are prioritized in ascending order, and floors below are deprioritized.
Enqueue: insert floor with O(log n) time. Dequeue: extract the highest-priority floor (next stop) in O(log n) time. Tie-breaking: if two floors are equidistant, prefer the one in the current direction; if still tied, use floor number order.
State that time complexity is O(log n) per operation and space is O(n) for n pending requests. Discuss edge cases like direction change, duplicate requests, and empty queue.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Described a SCAN-based approach and mentioned the starvation problem with requests at one end of the building.
Start by clarifying the problem constraints (e.g., number of elevators, request patterns, optimization goals) and then propose a hybrid algorithm like LOOK with fairness adjustments (e.g., aging or round-robin). Discuss trade-offs between travel time and fairness, and suggest metrics (average wait time, max wait time) to evaluate performance.
Pro tip: Acknowledge that pure optimization for travel time can starve some requests, so fairness mechanisms like aging are crucial; also mention that real-world systems often use heuristics due to dynamic conditions.
Ask about the number of elevators, building size, request patterns (e.g., peak hours), and whether fairness is defined as equal wait time or bounded wait time. This shows you consider practical context.
State that travel time optimization aims to minimize average wait/travel time, while fairness ensures no request waits excessively. Propose metrics like average wait time, 95th percentile wait time, and starvation count.
Describe a standard algorithm like LOOK (elevator continues in direction until no more requests, then reverses) which optimizes travel time by reducing direction changes. Mention its limitations regarding fairness.
Enhance the baseline with fairness: e.g., aging (increase priority of waiting requests over time) or round-robin among floors. Explain how this balances travel time and fairness.
Analyze trade-offs: fairness may increase average travel time. Suggest simulation or real-world testing to tune parameters (e.g., aging rate). Mention that adaptive algorithms could dynamically balance based on load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about a central dispatcher assigning requests to the nearest available elevator using a cost function (distance plus load).
Start by clarifying requirements and scale (number of elevators, floors, traffic patterns), then describe a distributed control architecture where each elevator is an independent agent coordinated by a central dispatcher. Explain how you would handle peak traffic modes by dynamically adjusting scheduling policies (e.g., zoning, express runs) and using real-time data to optimize throughput.
Pro tip: Emphasize trade-offs between centralized vs. decentralized control and how you would handle failures gracefully—this shows you think about reliability and scalability beyond just the happy path.
Ask about the number of elevators, floors, building type, and expected traffic patterns to scope the problem. This ensures your design addresses the actual constraints.
Propose a central dispatcher service that receives requests and assigns them to elevators, with each elevator as an independent controller. Discuss communication protocols (e.g., message queues) and data storage for state.
Explain how the dispatcher can scale horizontally (e.g., sharding by building zones) and how elevators coordinate to avoid conflicts. Mention load balancing and fault tolerance.
Describe dynamic scheduling algorithms (e.g., shortest-seek-time-first, zoning, express elevators) and how they adapt based on real-time demand. Discuss trade-offs between fairness and throughput.
Outline how you would collect metrics (wait times, utilization) and use them to adjust policies automatically or via manual overrides. Mention failover and degradation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.