Dynamic Resource Allocation via Time Series Workload Simulation
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Solution Overview
Problem
Public cloud resource allocation systems face inefficiencies in predicting future resource demands, leading to suboptimal resource reservation and increased costs, as they lack effective methods for accurately simulating future workloads and ensuring quality of service.
Innovation Solution
A device and method that utilize time series analysis, such as vector auto-regression, to predict resource usage patterns, simulate future workloads, and automatically reserve resources based on predicted demands, ensuring a target quality of service by identifying the necessary resources to meet predetermined service levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource allocation methods are used, then resource reservation is simple, but prediction accuracy of future resource demands is low
Solution Approach 1:
The system performs resource demand prediction and workload simulation in advance before the actual resource allocation is needed. By using historical data to predict future resource demands and simulating multiple possible workload scenarios beforehand, the system prepares optimal resource reservation strategies proactively, thereby improving prediction accuracy without compromising system simplicity during execution.
2Reliability
If more resources are reserved to ensure quality of service, then service reliability is improved, but resource allocation efficiency decreases
Solution Approach 1:
The system dynamically adjusts resource reservation levels based on simulated workload scenarios and predicted resource demands. Instead of statically over-provisioning resources, the system uses time-series analysis and Monte Carlo simulations to determine optimal dynamic reservation levels that adapt to changing conditions, thereby maintaining high service reliability while improving resource allocation efficiency by avoiding both over-provisioning and under-provisioning.
3Loss of energy
If resource reservation is optimized based on accurate predictions, then cost is reduced, but computational complexity increases
Solution Approach 1:
The system uses Monte Carlo simulation to create multiple virtual copies of possible future workload scenarios based on historical data patterns. By simulating thousands of hypothetical workload instances rather than analyzing every possible real-world scenario, the system achieves accurate cost optimization through representative sampling, reducing computational complexity while maintaining prediction accuracy.
Data Source
AI summary
A device comprises a memory storage comprising instructions and one or more processors in communication with the memory. The one or more processors execute the instructions to access data for a plurality of previous time periods for a client of a plurality of clients, the data for each previous time period of the plurality of previous time periods including resource usage data for a plurality of jobs of the client, apply a time series analysis algorithm to the accessed data to predict a distribution of characteristics of resource usage for a future time period for the client, based on the predicted distribution of characteristics of resource usage, simulate a number of future predicted workloads for the future time period for the client, and based on the simulated number of future predicted workloads, automatically reserve a set of resources for the future time period for the client.