Started with a simple send-and-ack approach.
Start by clarifying the constraints: each node only knows parent and children, and can only send messages to those neighbors. Then propose a link-layer reliability protocol between adjacent nodes, such as a sliding window with acknowledgments and retransmissions, and discuss how to handle message loss, duplication, and ordering. Finally, consider how this interface integrates with higher-level routing or application protocols.
Pro tip: Emphasize that reliability is scoped to adjacent nodes, not end-to-end, and that this design mirrors real-world link-layer protocols like TCP over a single hop. Mention that you would separate concerns: link reliability vs. end-to-end reliability, and that the interface should be simple and well-defined.
Restate the problem: nodes only know parent/children, can only send to neighbors. Identify what 'reliable' means: no loss, no duplicates, in-order delivery. Ask about message size, frequency, and whether the network is static or dynamic.
Propose a protocol between adjacent nodes: sequence numbers, acknowledgments, timeouts, and retransmissions. Consider sliding window for flow control and efficiency. Discuss how to handle duplicate messages and out-of-order delivery.
Specify the API: e.g., send(destination, message) and receive() with callbacks or blocking calls. Ensure the interface hides the complexity of retransmissions and acknowledgments from upper layers.
Discuss what happens if a node fails, if messages are delayed, or if the network partitions. Consider heartbeat mechanisms and how to detect and recover from failures.
Compare design choices: stop-and-wait vs. sliding window, timeout values, buffer sizes. Discuss impact on latency, throughput, and resource usage. Mention alternatives like using existing protocols (e.g., TCP) if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the network topology and assumptions (e.g., tree, general graph, synchronous/asynchronous). Then, design a distributed algorithm using message passing (e.g., convergecast in a tree or flooding with unique IDs in a general graph) to count nodes. Finally, analyze message and time complexity, discussing trade-offs and optimizations.
Pro tip: Explicitly state your assumptions about topology and message-passing model upfront, as they drastically affect the algorithm and complexity. Also, mention practical constraints like message size and failure handling to show depth.
State the network topology (e.g., tree, general graph), whether nodes have unique IDs, and if communication is synchronous or asynchronous. Specify if messages can be broadcast or only sent to neighbors.
For a tree, use convergecast: leaves send count=1 to parent; internal nodes sum children's counts and send to parent; root computes total. For a general graph, use flooding with unique IDs to avoid loops, or build a spanning tree first.
Count total messages sent. For tree convergecast: 2(n-1) messages (each edge carries one message up and one down if needed). For general graph flooding: O(E) messages, where E is number of edges.
Time is proportional to network diameter (longest shortest path) for synchronous models. For tree, time = O(depth). For general graph, time = O(diameter) if flooding, or O(n) if building spanning tree.
Mention termination detection, handling node failures, and reducing message size (e.g., sending only counts). Compare with centralized approaches and note trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The first part is almost trivial if you've thought about it before.
Clarify the network model (tree vs. general graph) and constraints, then design a protocol for root path using parent pointers or routing tables, and extend it to node-to-node paths via LCA or bidirectional search. Discuss trade-offs between centralized and distributed approaches, and consider scalability and fault tolerance.
Pro tip: Demonstrate awareness of real-world constraints: in distributed systems, maintaining parent pointers may be costly, so consider hierarchical routing or gossip protocols; also mention that in a tree, the path is unique, but in a general graph, you need to handle cycles and possibly find shortest paths.
Ask whether the network is a tree, DAG, or general graph; whether it's static or dynamic; and what 'root' means (e.g., a designated node). Clarify if the protocol should be distributed or centralized, and any constraints on message complexity or latency.
For a tree, each node stores a parent pointer; to find path to root, follow parent pointers. For a general graph, precompute a spanning tree or use a routing table with next-hop towards root. Discuss maintenance and update mechanisms.
Use the root path protocol to find paths from each node to root, then find the lowest common ancestor (LCA) or intersection point. Alternatively, use bidirectional search or distance-vector routing. For general graphs, consider shortest path algorithms like Dijkstra or BFS.
Compare approaches: parent pointers are simple but require updates on topology changes; routing tables are more robust but use more memory. Discuss message complexity, latency, and scalability. Mention caching or hierarchical routing for large networks.
Address how the protocol handles node failures, link failures, or network partitions. Propose mechanisms like periodic updates, heartbeats, or fallback to flooding. Emphasize fault tolerance and consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as achieving exactly-once semantics in a distributed system with unreliable communication. Then systematically address each failure mode (loss, duplication, reordering, crashes) using timeouts, retries, acknowledgments, and idempotent operations, while discussing correctness guarantees and complexity trade-offs. Conclude with practical considerations like exponential backoff, deduplication windows, and the CAP theorem.
Pro tip: Emphasize that idempotency is the key to handling duplicates and retries safely, and that timeouts must be tuned to balance latency and false positives. Mention that exactly-once delivery is impossible in theory, but can be approximated with idempotent operations and at-least-once delivery.
Ask about the system's consistency requirements (e.g., exactly-once vs at-least-once), network model (synchronous vs asynchronous), and failure detection mechanisms. State assumptions like bounded message delay or crash-recovery model.
Use acknowledgments (ACKs) and retries with timeouts to handle loss. For duplication, use idempotent operations (e.g., unique request IDs, deduplication tables) to ensure repeated messages don't cause side effects.
For reordering, use sequence numbers or timestamps to detect and reorder messages. For crashes, use persistent state, recovery protocols, and possibly replication to ensure availability and consistency.
Analyze guarantees: at-least-once with idempotency gives exactly-once effects. Discuss time/space complexity of deduplication (e.g., storing IDs) and trade-offs between timeout values, retry counts, and system load.
Mention real-world techniques like exponential backoff, jitter, circuit breakers, and the CAP theorem. Conclude that perfect exactly-once is impossible, but practical systems achieve it with idempotency and careful design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.