Scheduler Capacity Allocation for Bursty Workloads
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Solution Overview
Problem
Traditional scheduling schemes are inadequate for managing bursty workloads, as they fail to promptly adjust resource allocation when demand exceeds maximum capacity, leading to poor quality of service and inefficient resource utilization.
Innovation Solution
Implementing a scheduler control algorithm that immediately increases the allocation to a predefined maximum amount when the allocated capacity is completely utilized, rather than gradually increasing it, to ensure timely resource allocation for bursty workloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling schemes gradually increase capacity allocation when demand exceeds maximum capacity, then resource allocation adjusts slowly, but quality of service deteriorates for bursty workloads
Solution Approach 1:
The system dynamically adjusts capacity allocation based on real-time utilization monitoring. When utilization reaches 100%, the scheduler immediately transitions from gradual increase to maximum capacity allocation, adapting the allocation speed to the urgency of the situation. This dynamic response resolves the contradiction by making the system both responsive to urgent demands and efficient during normal operation.
Solution Approach 2:
The invention changes the allocation parameter from a gradual increment to an immediate maximum value when utilization hits 100%. This parameter change enables the system to quickly respond to bursty workload demands while maintaining controlled allocation during periods of lower utilization, thereby improving quality of service without excessive resource consumption.
2Reliability
If scheduler immediately increases allocation to maximum amount when capacity is completely utilized, then quality of service improves for bursty workloads, but resource utilization efficiency may worsen
Solution Approach 1:
The scheduler continuously monitors resource utilization and uses this feedback to make intelligent allocation decisions. When utilization is below 100%, the system maintains controlled allocation to preserve efficiency. When utilization reaches 100%, the feedback triggers immediate maximum allocation to ensure service quality. This feedback mechanism resolves the contradiction by allocating maximum resources only when absolutely necessary.
Solution Approach 2:
The system dynamically switches between two allocation modes: controlled gradual allocation during normal operation to maintain efficiency, and immediate maximum allocation when utilization reaches 100% to ensure service quality. This dynamic mode switching resolves the contradiction between efficiency and reliability by applying the appropriate strategy based on current system state.
3Productivity
If scheduler uses controlled parameter adjustments for capacity allocation, then resource utilization is efficient, but responsiveness to peak demands worsens
Solution Approach 1:
The invention changes the allocation parameter from a fixed gradual increment to a variable that can immediately jump to maximum value when utilization reaches 100%. This parameter change enables the system to maintain efficiency during normal operation while achieving maximum speed of response when peak demands occur, resolving the contradiction between efficiency and speed.
Data Source
AI summary
A method comprises defining a scheduler parameter for a maximum allocation of capacity of a shared resource to a consumer for a scheduling interval. Utilization of an allocated capacity of the shared resource by the consumer during a given scheduling interval is measured, and when the allocated capacity of the shared resource is completely utilized by the consumer during the given scheduling interval, the scheduler increases the allocated capacity of the shared resource to the defined maximum allocation for the consumer for a next scheduling interval. Thus, rather than gradually increasing the allocation of capacity over many intervals, the scheduler immediately increases the allocation to a predefined maximum amount in response to an allocated amount of capacity being completely utilized during a scheduling interval.