Dynamic Resource Allocation via Performance Feedback Control
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Solution Overview
Problem
Cloud computing environments face challenges in efficiently allocating resources to meet Service Level Agreements (SLAs) due to dynamic execution environments, where initial resource estimates may be oversized or undersized, and disturbances such as demand peaks and hardware malfunctions, leading to inefficiencies and potential SLA infringements.
Innovation Solution
A control theory-based method that dynamically adjusts resource allocation by evaluating the relationship between current resource allocation, performance metrics, and workload performance, using feedback control to iteratively solve optimization problems and ensure optimal resource usage while respecting SLA constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed amount of resources is allocated to workloads, then resource allocation simplicity is maintained, but the system cannot adapt to disturbances such as demand peaks and hardware malfunctions, leading to SLA infringements
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload performance metrics and adjusting resource allocation in real-time based on actual system conditions. This allows the system to adapt to disturbances such as demand peaks and hardware malfunctions while maintaining SLA compliance, resolving the contradiction between fixed allocation simplicity and adaptive reliability.
Solution Approach 2:
The system employs feedback control mechanisms where performance metrics are continuously measured and fed back to the resource allocation controller. This feedback loop enables the system to detect deviations from SLA targets and automatically adjust resource allocation to correct these deviations, thereby improving reliability through adaptive response to changing conditions.
2Reliability
If initial resource estimates are made oversized, then SLA compliance is ensured under all conditions, but resource usage efficiency decreases due to wasted allocated resources
Solution Approach 1:
Instead of static oversized allocation, the system dynamically adjusts resource allocation to match actual workload demands. This allows the system to maintain SLA compliance when needed while reducing resource allocation during periods of lower demand, thereby eliminating the waste associated with consistently oversized allocations.
Solution Approach 2:
The system changes the allocation parameters based on monitored performance metrics and system conditions. By adjusting resource allocation parameters dynamically rather than using fixed oversized values, the system maintains reliability while improving efficiency by allocating resources according to actual needs rather than worst-case scenarios.
3Loss of energy
If initial resource estimates are made undersized, then resource usage efficiency is improved, but SLA compliance cannot be guaranteed under disturbed conditions
Solution Approach 1:
The system starts with efficient minimal resource allocation and dynamically increases allocation when performance metrics indicate SLA violations or disturbances. This dynamic approach maintains efficiency during normal operation while ensuring reliability when needed, resolving the contradiction between efficient undersized allocation and reliable compliance.
Solution Approach 2:
The system takes preliminary actions by continuously monitoring performance metrics to detect early signs of SLA violations or disturbances. By detecting these conditions early and proactively adjusting resource allocation before full SLA infringement occurs, the system prevents reliability issues while maintaining efficient baseline resource usage.
4Adaptability or versatility
If dynamic resource adjustment is implemented, then adaptability to disturbances is improved, but system complexity increases due to continuous monitoring and adjustment mechanisms
Solution Approach 1:
The patent implements feedback control with clearly defined control loops that monitor specific performance metrics and adjust resource allocation accordingly. This structured feedback approach provides adaptability while managing complexity through systematic control mechanisms rather than ad-hoc adjustments.
Solution Approach 2:
The system manages complexity by focusing adjustments on key performance parameters rather than all possible system variables. By changing allocation parameters based on monitored performance metrics, the system achieves adaptability while maintaining manageable complexity through targeted parameter optimization.
Data Source
AI summary
Techniques are provided for allocating resources for one or more workloads. One method comprises obtaining a current performance of a workload; determining an adjustment to a current allocation of a resource allocated to the workload by evaluating a representation of a relationship between: (i) the current allocation of the resource allocated to the workload, (ii) a performance metric, and (iii) the current performance of the workload; and initiating an application of the determined adjustment to the current allocation of the resource for the workload. The performance metric may comprise a nominal value of a predefined service metric and the current performance of the workload may comprise a current value of a variable that tracks a given predefined service metric of the workload. An amount (or percentage) of the adjustment permitted for each iteration may be controlled. A sum of allocated resources can be constrained to an amount of available resources.