Private Cloud Workload Scheduling from Historical Configurations
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Solution Overview
Problem
Private clouds face challenges with resource constraints, leading to issues of over-provisioning or under-provisioning workloads, which result in increased costs, inefficient resource usage, and performance degradation, as existing auto-scaling solutions are ineffective in managing limited infrastructure.
Innovation Solution
A workload scheduler that recommends optimal configurations for new workloads based on historical deployments, intelligently identifying suitable compute and memory specifications to avoid over- or under-provisioning, thereby improving resource utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workloads are over-provisioned in a private cloud, then resource availability for future deployments is maintained, but resource utilization efficiency deteriorates and costs increase
Solution Approach 1:
The system performs preliminary analysis of workload requirements and historical deployment data before provisioning resources. The workload scheduler analyzes similar workloads and their resource consumption patterns in advance, allowing optimal resource allocation to be determined before deployment, thus avoiding both over-provisioning and under-provisioning.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring resource usage of deployed workloads and using this information to improve future provisioning decisions. The workload scheduler learns from historical deployment outcomes and resource utilization patterns, adjusting recommendations to optimize the balance between availability and efficiency.
2Loss of energy
If workloads are under-provisioned to optimize resource utilization, then cost efficiency improves, but workload performance deteriorates
Solution Approach 1:
The system determines optimal resource allocation in advance by analyzing workload characteristics, historical performance data, and similar deployments before the workload is provisioned. This preliminary action ensures that sufficient resources are allocated to meet performance requirements while avoiding excessive provisioning.
Solution Approach 2:
The workload scheduler adjusts resource allocation parameters based on analyzed workload requirements and historical data. It dynamically determines appropriate compute, memory, and storage parameters for each workload, optimizing the balance between performance and resource utilization efficiency.
3Loss of energy
If manual resource allocation is used to avoid over-provisioning, then resource utilization efficiency improves, but system complexity and time for deployment increase
Solution Approach 1:
The workload scheduler enables automated self-service provisioning by analyzing workload requirements and automatically generating optimal resource allocation recommendations. This eliminates the need for manual intervention while maintaining high resource utilization efficiency, as the system autonomously makes intelligent provisioning decisions based on historical data and analysis.
Solution Approach 2:
The system automatically determines and adjusts resource allocation parameters through analysis of workload characteristics and historical deployments, replacing manual configuration processes. This automation maintains efficiency while reducing deployment complexity and time.
4Productivity
If resources are increased to handle surge utilization, then workload performance is maintained, but resource availability for other purposes is reduced
Solution Approach 1:
The workload scheduler performs preliminary analysis of resource requirements and historical surge patterns before deployment, allowing optimal resource allocation to be determined in advance. This enables the system to handle surge utilization effectively without permanently over-provisioning resources, maintaining availability for other purposes.
Data Source
AI summary
Examples described herein relate to a workload scheduler for deploying a workload in a private cloud. The workload scheduler receives a request to deploy a workload specifying a workflow to be executed. The workload scheduler then identifies a set of pre-deployed configurations based on one or more pre-deployed workloads and an identity of the workflow. Then, the workload scheduler recommends a target configuration from the set of pre-deployed configurations, and deploys the workload with the target configuration in the private cloud.