Stateless Scheduler with Dynamic Workload Termination
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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 and optimize resource allocation in cloud-based computing environments.
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 optimizing resource allocation to meet SLTs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional schedulers are used, then resource allocation can be performed, but Quality of Service (QoS) targets and Service Level Targets (SLTs) are not adequately addressed
Solution Approach 1:
The scheduling system is divided into distinct components: a scheduler that handles workload assignment and a workload manager that manages execution. This segmentation allows each component to focus on specific QoS and SLT requirements without being burdened by the entire system's complexity, thereby improving reliability while managing device complexity.
Solution Approach 2:
The scheduler implements dynamic workload termination based on cost-benefit analysis, allowing the system to adapt resource allocation in real-time. This dynamic approach enables the system to meet QoS and SLT targets by adjusting scheduling decisions based on current system state, improving reliability without requiring a statically complex system architecture.
2Adaptability or versatility
If conventional schedulers are used, then scheduling can be performed, but extendibility and maintainability are lacking
Solution Approach 1:
By separating the scheduler from the workload manager, the system achieves better extendibility. The scheduler can be modified or replaced without affecting the workload management component, and vice versa. This modular architecture allows independent development and maintenance of each component, improving adaptability while managing overall system complexity.
Solution Approach 2:
The workload manager is designed to handle multiple workload types and scheduling scenarios through a unified interface. This universal design allows the system to be extended to handle new workload types without fundamentally redesigning the core scheduling algorithm, thereby improving adaptability while keeping the scheduling algorithm's complexity manageable.
3Reliability
If resources are allocated to meet high priority workloads, then QoS targets can be met, but resource utilization efficiency may decrease
Solution Approach 1:
The system dynamically changes scheduling parameters based on workload characteristics and system state. By adjusting priority levels, allocation ratios, and termination thresholds, the system can optimize the balance between meeting QoS targets and maintaining resource utilization efficiency. This parameter adjustment allows the system to adapt to different operational conditions rather than being locked into a fixed allocation scheme.
Solution Approach 2:
The scheduler implements feedback mechanisms that monitor QoS target achievement and resource utilization simultaneously. Based on this feedback, the system adjusts scheduling decisions to maintain an optimal balance - terminating workloads when they no longer provide value and reallocating resources to higher-priority tasks, thereby maintaining both QoS compliance and resource efficiency.
4Productivity
If dynamic workload termination is implemented, then resource allocation can be optimized, but system complexity increases
Solution Approach 1:
The complex logic for dynamic workload termination and cost-benefit analysis is extracted from the core scheduler and placed in the workload manager. This extraction allows the scheduler to remain relatively simple while delegating the complex optimization decisions to the workload manager, which is designed to handle such complexity. This separation improves resource allocation optimization while managing system complexity through modular design.
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.