Scheduler Offer ReservationDistributed Systems & Databases

1. user

   # Scheduler Offer Reservation ## Goal The containerized two-level resource scheduler in `/app/scheduler.py` has a flawed scheduling contract. A resource offer is currently treated as a non-binding proposal: once an offer is sent, the same resources may be offered to another framework, and the scheduler may rescind an offer after a framework has committed to launching a task. You must fix the scheduler so that it honors the two-level scheduling contract correctly. ## Deliverable Write a corrected `scheduler.py` to `/logs/artifacts/scheduler.py`. The verifier will import this module and run deterministic scenarios against it. Do not modify files under `/app`; only the artifacts under `/logs/artifacts/` are graded. ## Required API Implement a module `scheduler.py` with the following classes and methods: - `Scheduler(nodes, now=0)` `nodes` is a dict `node_id -> total_resources`. - `advance_time(delta)` Advance the internal clock by `delta`. - `make_offers(framework_id, timeout=10)` Return a list of `Offer` objects, one per node that has free capacity. - `accept_offer(framework_id, offer_id)` Accept a pending offer. Return `True` only if the offer is valid and the acceptance succeeds. - `launch_task(framework_id, offer_id, task_id=None)` Launch a task from an accepted offer. Return a `task_id` on success (use the provided `task_id` or generate one), or `None` on failure. - `decline_offer(framework_id, offer_id)` Release an offer so its resources can be offered again. - `set_node_resources(node_id, total)` Update a node's total capacity. - `get_allocations()` Return a dict `task_id -> (framework_id, node_id, resources)`. `Offer` objects must expose at least: - `offer_id` - `framework_id` - `node_id` - `resources` - `state` — one of `pending`, `accepted`, `declined`, `expired`, `invalidated`, `launched` ## Correctness requirements 1. **No over-commitment**: At all times, the sum of resources allocated to running tasks plus

2. agent

   Refining the Scheduler: I'm focused on resolving the resource scheduler's issues in `scheduler.py`. It's clear the current implementation doesn't enforce the two-level scheduling contract. I need to ensure that the scheduler treats resource offers as binding reservations rather than non-committal proposals to meet scheduling requirements.

3. agent

   Clarifying Resource Allocation: I'm now focusing on the resource allocation strategy. The initial implementation doesn't reserve resources upon offer. The core problem is ensuring the scheduler correctly handles these offers as non-binding proposals, and specifically *honors the two* something. I need to figure out what that "two" is! Defining the Core Classes: I'm now diving into the design of the core classes and methods for the scheduler. I've started defining the `Offer` and `Task` classes to model these essential components. My focus is on ensuring these classes capture all the necessary information, especially resource allocation and offer expiration. I'm aiming for a robust and intuitive structure. Modeling Offer Lifecycle: I'm now refining the offer lifecycle within the scheduler. I'm especially focusing on how offers transition between states: 'pending', 'accepted', and 'expired'. I'm also ensuring expiration is handled appropriately, particularly for 'accepted' offers that haven't yet launched a task. I need to make sure the scheduler correctly respects and implements this state transition. Analyzing Offer Handling: I'm now clarifying the "accept" and "launch" methods, thinking about their interaction. I'm focusing on the timeout post-acceptance before a launch is required. The prompt provides specifics. I am now pondering the meaning of offer "invalidation" and whether resource changes on a node trigger it. Does reducing node resources invalidate existing offers or only future ones? What about overcommitment scenarios? Clarifying Invalidations and Offers: I'm now focusing on what happens when a node's capacity changes, especially how we handle 'pending' offers. If the sum of allocated resources plus reserved resources exceeds the new capacity, I've got to invalidate pending offers. I'm focusing on ensuring this invalidation process quickly reduces the reserved resources. This seems to be the most recent update, following the rules carefully. Refining 'Out

4. agent

   Analyzing Starter Code: Okay, I've noticed those CANARY tags in the starter code. While the verifier ignores them, I'm thinking we need to avoid adding any ourselves. The implementation itself looks quite robust. I'm moving forward with writing the code now; I'll place it in `/logs/artifacts/scheduler.py`.

5. agent

   Verifying File Deployment: The file is deployed correctly! Now, I am moving into a final code review. I'm focusing on key methods: `advance_time`, which updates the internal time under lock, and the `make_offers` method, which is allocating and immediately reserving offers. Lastly, I am looking at the `accept_offer` checks. Analyzing Offer Lifecycle: Okay, I'm diving deeper into the edge cases of offer acceptance and task launch. Specifically, I'm examining how resource allocation works when `set_node_resources` is called and how the `launch_task` method handles invalid task IDs after a successful offer acceptance. I am also checking what happens to an offer if a `task_id` check in `launch_task` fails. It looks like the offer could stay `accepted`!

6. agent

   I have fixed the `scheduler.py` logic to properly manage resource reservations. The rewritten scheduler has been written out to `/logs/artifacts/scheduler.py` as requested. The following primary corrections were applied: 1. **Reservation on Offer**: Resources are instantly placed in a `pending` state when `make_offers` is executed, preventing concurrent framework requests from over-subscribing the node's capacity. 2. **Accept & Launch State Transitioning**: Explicit state validation is performed so that `accept_offer` validates the `pending` state, and `launch_task` relies strictly on the `accepted` state. 3. **Capacity Constraints**: `set_node_resources` aggressively invalidates outstanding offers (newest first) if the node's capacity drops below what is currently allocated and reserved. The corrected module natively uses threading locks to maintain thread-safe operations during concurrent scheduling attempts.

6 of 22 steps — the producer's own sampler elided the rest before the drop.