Stateless Scheduler for Cloud Workload QoS and SLT Compliance
Find Innovative SolutionsGenerate Solutions
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 efficiently manage competing workloads 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 to allocate computing resources, then resource allocation can be performed, but Quality of Service (QoS) targets and Service Level Targets (SLTs) for workload completion times and resource usage are not adequately addressed
Solution Approach 1:
The scheduler dynamically adjusts resource allocation based on real-time workload characteristics and QoS requirements. It continuously monitors workload progress and resource availability, adapting allocation decisions to meet SLTs while optimizing completion times. This dynamic approach replaces static conventional scheduling with responsive, condition-based resource management.
Solution Approach 2:
The system changes scheduling parameters such as priority levels, resource allocation ratios, and time constraints based on QoS targets and SLTs. By adjusting these parameters dynamically according to workload urgency and resource availability, the scheduler ensures compliance with service level agreements while maintaining efficient workload completion.
2Reliability
If conventional scheduling algorithms are implemented, then scheduling can be performed, but it is difficult to prove the correctness of the algorithms due to lack of extendibility and maintainability
Solution Approach 1:
The scheduling algorithm is segmented into distinct, independently verifiable components such as workload analysis modules, resource allocation modules, and QoS verification modules. Each segment can be tested and verified separately, making it easier to prove overall correctness while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor scheduling decisions against QoS targets and SLTs, providing verification of algorithm correctness. This feedback loop enables continuous validation of scheduling behavior, ensuring that the complex algorithm maintains reliability through observable compliance with service level agreements.
3Reliability
If resources are allocated to meet QoS targets, then service level compliance improves, but resource utilization efficiency may be reduced due to conservative allocation
Solution Approach 1:
The scheduler applies partial resource allocation initially, providing enough resources to meet QoS targets without over-provisioning. It dynamically adjusts allocation based on actual workload progress and remaining time constraints, using excessive resources only when necessary to meet SLTs, thereby optimizing the balance between compliance and efficiency.
Solution Approach 2:
The system performs preliminary analysis of workload requirements and QoS constraints before allocation, pre-calculating optimal resource levels needed to meet SLTs. This preliminary action enables efficient resource provisioning from the start, avoiding both under-allocation that would compromise compliance and over-allocation that would reduce efficiency.
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.