Workload Profiler and Performance Interference Model for Cloud QoS
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Solution Overview
Problem
Current cloud computing systems face inefficiencies in workload consolidation and resource allocation due to static rules that do not consider real-time resource usage, leading to poor workload performance and inefficient resource utilization, which affects Quality of Service (QoS) guarantees for cloud consumers.
Innovation Solution
The Workload Profiler and Performance Interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler, influence matrix, and performance interference model to optimize workload assignments and consolidation strategies, predicting performance impacts and adjusting resource allocations in real-time to meet QoS goals and maximize revenue for both cloud providers and consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static consolidation rules are used, then resource utilization is simplified to manage, but workload performance deteriorates due to lack of real-time adaptation
Solution Approach 1:
The patent implements dynamic workload consolidation by continuously monitoring real-time resource usage metrics and automatically adjusting consolidation decisions based on current system state, replacing static rules with adaptive control mechanisms that respond to changing workload characteristics and resource conditions
Solution Approach 2:
The system incorporates feedback loops where performance metrics and resource usage data are continuously collected from consolidated workloads, analyzed to assess consolidation effectiveness, and used to adjust future consolidation decisions, creating a closed-loop control system that improves workload performance while managing complexity
2Productivity
If more workloads are consolidated, then resource utilization improves, but performance interference between workloads worsens QoS
Solution Approach 1:
The patent applies local quality by tailoring consolidation strategies to specific workload characteristics, resource types, and performance requirements, rather than applying uniform consolidation rules, allowing different consolidation approaches for different workload groups to maintain QoS while improving resource utilization
Solution Approach 2:
The system dynamically changes consolidation parameters such as the number of consolidated workloads, resource allocation ratios, and consolidation timing based on real-time monitoring of performance metrics and resource usage, adjusting these parameters to optimize the balance between resource utilization and QoS guarantees
3Measurement precision
If manual profiling and resource adjustment are used, then workload understanding is thorough, but automation level deteriorates reducing efficiency
Solution Approach 1:
The patent implements self-service automation where the system automatically performs workload profiling, resource allocation, and consolidation decisions by monitoring its own state and making adjustments without manual intervention, while maintaining thorough workload characterization through automated metric collection and analysis
Solution Approach 2:
The system performs preliminary automated profiling and analysis of workload characteristics before consolidation decisions are made, preparing consolidation strategies in advance based on pre-collected performance data and resource usage patterns, enabling automated decision-making with thorough workload understanding
Data Source
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AI summary
The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.