VM Colocation via Dynamic Workload Sharing Points
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Solution Overview
Problem
Existing methods for optimal virtual machine resource allocation in virtualized environments assume a fixed threshold for workload sharing, leading to sub-optimal solutions as they do not fully exploit the time-varying nature of co-located workloads.
Innovation Solution
Determining a dynamic workload capacity threshold and optimal sharing point for each pair of virtual machines based on service requirements, allowing for percentage compatibility assessment and efficient co-location on physical infrastructures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed threshold is used for workload sharing, then the allocation method is simple and easy to implement, but the resource allocation becomes sub-optimal as it does not exploit the time-varying nature of co-located workloads
Solution Approach 1:
The patent applies the dynamics principle by replacing the fixed threshold with a dynamic sharing point that varies over time. The sharing point is determined based on the actual workload characteristics and time-series data, allowing the threshold to adapt to changing workload patterns. This enables the system to exploit time-varying sharing opportunities while maintaining reasonable implementation complexity through automated detection and adjustment mechanisms.
2Productivity
If a dynamic sharing point is determined for each workload pair, then the resource allocation efficiency is improved by exploiting time-varying sharing opportunities, but the system complexity increases due to the need to analyze time-series data and calculate optimal sharing points
Solution Approach 1:
The patent applies the self-service principle by enabling the system to automatically detect workload characteristics, analyze time-series data, and determine optimal sharing points without manual intervention. The workload manager autonomously performs capacity analysis, identifies sharing opportunities, and adjusts allocation decisions based on actual workload behavior. This automation reduces the operational burden while managing the computational complexity through systematic algorithms.
3Measurement precision
If the sharing point is determined based on time-series data and workload variability, then the accuracy of capacity threshold determination is improved, but the measurement and detection complexity increases
Solution Approach 1:
The patent applies the preliminary action principle by pre-defining the methodology for analyzing time-series data and determining workload characteristics before actual capacity allocation decisions are made. The system establishes frameworks for measuring workload variability, identifying patterns, and calculating sharing points in advance. This preparation simplifies the real-time measurement process and enables more accurate capacity threshold determination through structured analysis procedures.
Data Source
AI summary
This technology relates to a device and method for determining co-locatability of a plurality of virtual machines on one or more physical infrastructures. The plurality of virtual machines hosts a plurality of workloads. This involves identifying workloads which have high variability from the time series data and determining the workload capacity threshold of the identified workloads. Thereafter, the candidate workloads are selected among the identified workloads to colocate on a virtual machine based on the workload variability. After that, the total capacity required by each candidate workload pair to meet the service requirement is determined based on the workload capacity threshold. Then, an optimal sharing point of each workload of the pair with respect to the other workload of the pair is identified. Further, percentage compatibility of each workload pair is determined and finally, the candidate workloads are colocated based on the optimal sharing point and percentage compatibility.