Adaptive Resource Allocation for Cloud Workloads Using Interference Feedback
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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 workloads, interference from other workloads, and unpredictable demand, leading to inefficiencies and potential SLA violations.
Innovation Solution
A control theory-based resource allocation mechanism that dynamically adjusts resources using feedback loops, accounting for self-allocation and interference effects, to ensure SLA compliance while minimizing resource usage and optimizing profit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed amount of resources is allocated to workloads, then Service Level Agreements can be guaranteed, but resource usage efficiency decreases and costs increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously adjusting the amount of resources allocated to each workload based on real-time performance monitoring and feedback. Instead of fixed allocation, the system adapts resource distribution dynamically to match actual workload demands, thereby maintaining SLA compliance while optimizing resource utilization efficiency
Solution Approach 2:
The system employs feedback mechanisms where performance metrics of workloads are continuously monitored and fed back to the resource allocation controller. This feedback loop enables the system to detect when SLAs are at risk of violation and automatically adjust resource allocation accordingly, ensuring reliability while avoiding over-provisioning of resources
2Reliability
If more resources are allocated to meet peak demand, then Service Level Agreements are maintained, but resource allocation efficiency decreases during low demand periods
Solution Approach 1:
The system dynamically adjusts resource allocation levels based on real-time demand conditions. During peak demand periods, resources are automatically increased to maintain SLA compliance, while during low demand periods, resources are reduced to optimize allocation efficiency. This dynamic adaptation eliminates the need for static over-provisioning
Solution Approach 2:
The patent changes the allocation parameters of computational resources based on workload characteristics and demand patterns. By adjusting resource allocation parameters dynamically rather than maintaining fixed parameters, the system achieves both SLA compliance during high demand and improved efficiency during low demand periods
3Loss of energy
If resources are dynamically adjusted without considering interference effects, then resource usage is optimized, but performance prediction accuracy decreases
Solution Approach 1:
The system applies different allocation strategies to different workloads based on their specific characteristics and interference patterns. By analyzing the interference effect of each workload on others, the system tailors resource allocation decisions locally for each workload, improving performance prediction accuracy while maintaining overall resource optimization
Solution Approach 2:
The patent performs preliminary analysis of interference effects between workloads before making resource allocation decisions. By pre-characterizing how workloads interfere with each other, the system can make more accurate performance predictions and adjust resource allocation accordingly, avoiding suboptimal decisions that would arise from ignoring interference effects
Data Source
AI summary
Techniques are provided for adaptive resource allocation for multiple workloads. One method comprises obtaining a dynamic system model based on a relation between an amount of a resource for multiple iterative workloads and a predefined service metric; obtaining an instantaneous value of the predefined service metric; applying to a given controller associated with a given iterative workload of the multiple iterative workloads: (i) the dynamic system model, (ii) an interference effect of one or more additional iterative workloads on the given iterative workload, and (iii) a difference between the instantaneous value and a target value for the predefined service metric. The given controller applies an adjustment to the amount of the resource for the given iterative workload based at least in part on the difference. The resource allocation for the multiple iterative workloads can be performed in a sequence substantially in parallel with an execution of the iterative workloads.