Scheduler Offer ReservationDistributed Systems & Databases

6 of 22 turns are kept here; the rest were cut to fit the trajectory's byte budget, and each cut is marked where it fell.

1. 1userScheduler Offer Reservation Goal The containerized two-level resource scheduler in /app/scheduler.py has a flawed schedu…

   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

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2. 2Refining the Scheduler: I'm focused on resolving the resource scheduler's issues in scheduler.py. It's clear the current…

   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. 3Clarifying Resource Allocation: I'm now focusing on the resource allocation strategy. The initial implementation doesn't…

   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

   … 1,000 more characters in the raw trajectory.

4. 4Analyzing Starter Code: Okay, I've noticed those CANARY tags in the starter code. While the verifier ignores them, I'm t…

   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. 5Verifying File Deployment: The file is deployed correctly! Now, I am moving into a final code review. I'm focusing on ke…

   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. 6I have fixed the scheduler.py logic to properly manage resource reservations. The rewritten scheduler has been written o…

   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.