Stateless Scheduler with Cyclical SLT Optimization
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Solution Overview
Problem
Conventional schedulers fail to adequately address Quality of Service (QoS) targets and Service Level Targets (SLTs) for workload completion times and resource usage, and lack extendibility and maintainability, making it difficult to prove the correctness of scheduling algorithms.
Innovation Solution
A stateless, deterministic scheduler with interruption recovery and scalable resource allocation is implemented, using a local cache to dynamically allocate resources based on real-time data from compute and Continuous Integration clouds, prioritizing workloads according to QoS requirements and iteratively improving scheduling decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional schedulers are used, then basic scheduling functions are provided, but they fail to adequately address QoS targets and SLTs for workload completion times and resource usage
Solution Approach 1:
The scheduler is divided into multiple independent components: a scheduling component that determines resource allocation, a simulation component that models workload execution, and an optimization component that adjusts parameters. Each component has a specific function, allowing the system to achieve complex QoS compliance without monolithic complexity
Solution Approach 2:
A simulation environment acts as an intermediary between the scheduling component and actual workload execution. The simulator predicts workload completion times and resource usage without executing real workloads, enabling QoS validation before actual scheduling decisions are implemented
2Reliability
If conventional schedulers are used, then scheduling operations are performed, but it is impossible to prove the correctness of scheduling algorithms due to lack of extendibility and maintainability
Solution Approach 1:
The system implements continuous feedback loops where the simulation component provides performance data back to the optimization component, which adjusts scheduling parameters. This closed-loop verification allows algorithm correctness to be proven through iterative testing and validation against QoS targets
Solution Approach 2:
Instead of directly executing and verifying scheduling algorithms on production systems, the invention creates virtual copies through simulation environments. These simulated workloads replicate real system behavior, allowing algorithm correctness to be verified in a controlled, repeatable manner without risking production system stability
3Reliability
If resources are allocated to meet QoS requirements, then workload completion reliability improves, but resource utilization efficiency may decrease
Solution Approach 1:
The scheduling system dynamically adjusts resource allocation based on real-time workload characteristics and QoS requirements. Rather than static over-provisioning, the optimization component continuously tunes allocation parameters to meet the minimum reliability thresholds while maximizing resource utilization, adapting to changing system conditions
Solution Approach 2:
The system changes scheduling parameters such as time quotas, priority levels, and resource allocation ratios based on workload analysis and QoS targets. By optimizing these parameters iteratively, the system achieves the required workload completion reliability while improving resource utilization efficiency compared to fixed allocation schemes
Data Source
AI summary
In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing a stateless, deterministic scheduler and work discovery system with interruption recovery. For instance, according to one embodiment, there is disclosed a system to implement a stateless scheduler service, in which the system includes: a processor and a memory to execute instructions at the system; a compute resource discovery engine to identify one or more computing resources available to execute workload tasks; a workload discovery engine to identify a plurality of workload tasks to be scheduled for execution; a cache to store information on behalf of the compute resource discovery engine and the workload discovery engine; a scheduler to request information from the cache specifying the one or more computing resources available to execute workload tasks and the plurality of workload tasks to be scheduled for execution; and further in which the scheduler is to schedule at least a portion of the plurality of workload tasks for execution via the one or more computing resources based on the information requested. Other related embodiments are disclosed.