Job Scheduling Control System for Compute Instance Interruptions
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Solution Overview
Problem
Client devices face inefficiencies and unpredictable interruptions when using interruptible compute instances for resource-intensive tasks like machine learning, due to limited insight into the operation and control of these instances by service provider systems.
Innovation Solution
A resource control system that employs a machine-learning module to optimize scheduling across compute instances by processing logs and estimating utility and time, generating actions to manage compute instances, and using instance probing to maximize efficiency and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If interruptible compute instances are used to reduce cost, then cost is reduced, but reliability deteriorates due to frequent interruptions
Solution Approach 1:
The system performs preliminary actions by saving checkpoints and intermediate results during job execution on interruptible instances. When interruptions occur, the job can be resumed from these saved states rather than starting over, maintaining reliability while using cost-effective interruptible instances.
Solution Approach 2:
The system implements feedback mechanisms by monitoring job progress, resource availability, and interruption patterns. Based on this feedback, it dynamically adjusts scheduling decisions, instance selection, and checkpoint frequency to optimize both cost and reliability outcomes.
2Loss of energy
If interruptible compute instances are used to reduce cost, then cost is reduced, but productivity deteriorates due to frequent interruptions
Solution Approach 1:
The system performs preliminary actions by pre-computing and saving intermediate results at strategic checkpoints. This allows jobs to be resumed quickly after interruptions without significant loss of productivity, while still benefiting from the lower cost of interruptible instances.
Solution Approach 2:
The system dynamically adjusts its behavior based on real-time conditions, including instance availability, job progress, and interruption patterns. It can switch between interruptible and uninterruptible instances, adjust checkpoint frequency, and modify scheduling strategies to maintain productivity while optimizing cost.
3Device complexity
If service provider system controls interruptible instances without client insight, then device complexity is reduced, but measurement precision deteriorates regarding job status monitoring
Solution Approach 1:
The system introduces an intermediary layer that sits between the service provider's control system and the client's monitoring needs. This intermediary collects detailed job status information from the provider side and presents it to clients in a standardized, easy-to-understand format, improving measurement precision without adding complexity to the underlying control system.
Data Source
AI summary
A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.