Dynamic Resource Allocation via Workload Fingerprint Extraction
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Solution Overview
Problem
Cloud computing providers face inefficiencies and SLA compliance challenges due to unpredictable workload demands and resource allocation inefficiencies, leading to potential SLA infringements and resource wastage.
Innovation Solution
Dynamic resource allocation based on workload fingerprints extracted from telemetry data using autoencoders, which compare new workloads to past executions to determine optimal initial resource allocations, minimizing interference and ensuring SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large amount of resources is dedicated to each customer workload, then SLA compliance is ensured, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation that adjusts resource assignments based on real-time workload characteristics and historical performance data. Instead of static over-allocation, the system continuously adapts resource distribution to match actual demand patterns, ensuring SLA compliance while optimizing utilization efficiency
Solution Approach 2:
The system changes resource allocation parameters based on extracted workload fingerprints and performance metrics. By monitoring and adjusting allocation parameters dynamically according to measured workload characteristics, the system achieves both SLA compliance and improved resource efficiency
2Productivity
If resources are dynamically allocated to new applications, then resource utilization improves, but interference with running workloads increases
Solution Approach 1:
The system performs preliminary analysis of workload fingerprints and predicts potential interference effects before allocating resources to new applications. By pre-assessing compatibility and resource requirements, the system can make informed allocation decisions that minimize disruption to existing workloads
Solution Approach 2:
The patent implements a feedback mechanism that monitors the impact of newly allocated workloads on running applications. This feedback loop allows the system to detect interference patterns and adjust subsequent resource allocation decisions to prevent harmful interactions while maintaining high utilization
3Speed
If random resource estimation is used for new workloads, then allocation speed improves, but resource wastage increases
Solution Approach 1:
The system performs preliminary workload fingerprint extraction and pattern matching before final resource allocation. By pre-analyzing workload characteristics against historical data, the system generates informed resource estimates quickly, avoiding both random allocation and time-consuming iterative adjustments
Solution Approach 2:
The patent uses copying by matching new workload fingerprints against stored historical workload patterns. When similar workloads are found in the historical database, their resource allocation patterns are copied and adapted for the new workload, providing accurate estimates without random guessing or extensive analysis
Data Source
AI summary
Resource allocation to workloads is disclosed. Telemetry data associated with existing or previously executed workloads is stored and used to develop models. Telemetry data from new workloads are collected and, using the models, a fingerprint is extracted and compared to the fingerprints of previous workloads. This allows the initial allocation of resources to the new workload to be improved and aids in resource allocation convergence.