Capacity Cluster Resource Reservations for Predictable GPU Access
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Solution Overview
Problem
Access to GPUs has become a major obstacle for users attempting to develop AI applications, due to a significant increase in demand outpacing supply, leading to uncertainty and inefficiency in obtaining necessary GPU capacity.
Innovation Solution
The implementation of capacity cluster resource reservations in a cloud provider network, which allows users to reserve blocks of GPU time for specific durations, ensuring predictable access to GPUs without long-term commitments, and utilizing auxiliary compute instances for pre-launch and post-termination optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users request GPU capacity on-demand, then they can access GPUs when needed, but they face uncertainty and inefficiency due to demand outpacing supply
Solution Approach 1:
The patent implements capacity reservations that allow users to pre-book GPU capacity in advance for specific time windows. This preliminary action ensures that when the reserved time window arrives, the GPU capacity is already allocated and ready for immediate use, eliminating the uncertainty and delay of on-demand requests during high-demand periods.
2Reliability
If users reserve GPU capacity for long durations, then they ensure predictable access, but they incur higher costs from idle or underutilized resources
Solution Approach 1:
The patent introduces dynamic capacity reservations with configurable time windows rather than fixed long-term commitments. Users can reserve capacity for specific durations and time periods, allowing the reservation to be released when no longer needed. This dynamic approach maintains access predictability during active periods while preventing waste during idle periods.
Solution Approach 2:
The system implements periodic reservations where capacity is allocated for specific time windows rather than continuously. This allows users to obtain GPUs reliably when needed for AI training workloads while automatically releasing capacity after the time window expires, improving overall resource utilization efficiency.
3Productivity
If cloud providers allocate GPU capacity dynamically, then they maximize resource utilization, but users experience uncertainty in obtaining necessary capacity
Solution Approach 1:
The patent implements capacity reservations that allow users to pre-book GPU capacity in advance for specific time windows. This preliminary action ensures that when the reserved time window arrives, the GPU capacity is already allocated and ready for immediate use, eliminating the uncertainty and delay of on-demand requests during high-demand periods.
Data Source
AI summary
Techniques for implementing and utilizing capacity cluster resource reservations are described. A managed compute service receives a user's request to identify a capacity block for use in launching compute instances. A schedule with pre-computed blocks, each corresponding to an amount of instances and an amount of time, is used to identify a block satisfying the request's criteria. The user can later obtain the capacity block and launch instances into the reservation during its time window, where placement rules associated with the reservation ensure the instances are hosted in locations enabling low-latency intercommunications.