Workload Deployment for Optimal Data Center Selection
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Solution Overview
Problem
Cloud service providers face challenges in selecting the optimal data center for workload deployment due to the dynamic nature of cloud services and the lack of existing systems to determine the best fit based on client needs and performance metrics.
Innovation Solution
A method involving simultaneous deployment of a workload across multiple data centers to collect performance metrics, identify optimal data centers, and remove sub-optimal deployments to free resources, utilizing a cloud controller to select candidate data centers and monitor performance metrics to determine the best fit for workload execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If workloads are deployed to multiple data centers simultaneously for performance evaluation, then the accuracy of selecting the optimal data center is improved, but the consumption of computing resources and time increases
Solution Approach 1:
The system performs preliminary actions by deploying workloads to multiple candidate data centers simultaneously before making the final selection. This allows performance metrics to be collected and compared in advance, ensuring the most suitable data center is chosen based on actual measured performance rather than estimates or single-point evaluations.
Solution Approach 2:
The system employs partial action by selecting a limited number of candidate data centers (e.g., top 3) for simultaneous workload deployment rather than evaluating all available data centers. This approach provides sufficient measurement precision for optimal selection while avoiding the excessive consumption of resources that would result from evaluating every possible location.
2Speed
If workloads are deployed to multiple data centers simultaneously, then the speed of identifying the optimal data center is improved, but the complexity of the deployment system increases
Solution Approach 1:
The system executes preliminary workload deployments to multiple candidate data centers in parallel, collecting performance metrics simultaneously rather than sequentially. This approach significantly accelerates the identification of the optimal data center by performing all necessary evaluations before the selection decision is made, rather than requiring iterative testing.
Solution Approach 2:
The deployment system is segmented into distinct functional modules: a workload deployment module that distributes workloads to candidate data centers, a performance metric collection module that gathers data from each location, and a selection module that determines the optimal data center based on collected metrics. This modular architecture manages system complexity by organizing the multi-data center evaluation process into independent, manageable components.
3Reliability
If performance metrics are collected from multiple data centers, then the quality of service is improved, but the time required for workload deployment and evaluation increases
Solution Approach 1:
The system performs preliminary workload deployments and performance evaluations to multiple candidate data centers before final selection. By collecting performance metrics in advance through simultaneous deployments, the system ensures high service quality based on actual measured performance while reducing the time required for post-selection optimization.
Solution Approach 2:
The system collects performance metrics from a partial set of candidate data centers (e.g., top 3 candidates) rather than all available data centers. This partial evaluation approach maintains sufficient service quality for making an optimal selection while significantly reducing the time and resources required compared to comprehensive evaluation of every possible data center.
Data Source
AI summary
Embodiments provide optimized deployment of workloads. A first workload to be deployed in at least one data center of a plurality of data centers is received. A first plurality of candidate data centers is selected from the plurality of data centers, and the first workload is deployed to each of the first plurality of candidate data centers. A first performance metric is collected from each respective data center of the first plurality of candidate data centers based on execution of the first workload deployed at the respective data centers, and a first optimal data center from the first plurality of candidate data centers is identified based on the first performance metrics. The first workload is removed from each of the first plurality of candidate data centers, other than the identified first optimal data center.