Workload Placement Optimization via Dynamic Priority Factors
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Solution Overview
Problem
Cloud service providers face inefficiencies in workload placement due to limited physical resources, leading to queued workloads and suboptimal business operations, as existing methods fail to prioritize workloads effectively based on profitability, penalty, and resource availability.
Innovation Solution
A method that determines a profitability factor, penalty factor, and preference factor for each workload, using contract and billing information, and resource availability, to assign priority ordering, optimizing workload placement and resource allocation for maximum profitability and minimal losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workloads are placed in a queue due to limited infrastructure resources, then resource overload is avoided, but productivity and business profitability deteriorate due to delayed workload execution
Solution Approach 1:
The patent changes the parameter of workload prioritization from static to dynamic by calculating preference factors based on real-time profitability and penalty information. This allows the system to adapt resource allocation parameters according to business value, executing high-value workloads first to maximize productivity within limited resource constraints
Solution Approach 2:
The system implements dynamic workload prioritization where the priority ordering of workloads changes based on current profitability factors and penalty factors. This dynamic approach allows the cloud service provider to continuously optimize the mix of executed workloads to maximize business profitability rather than using fixed priority queues
2Productivity
If workloads are executed without priority-based placement, then infrastructure resources are utilized, but business profitability deteriorates due to suboptimal workload selection
Solution Approach 1:
The patent implements a feedback mechanism where profitability information and penalty information from executed workloads are fed back into the priority ordering system. This feedback loop allows the system to learn from past execution outcomes and continuously improve workload selection to maximize business profitability while maintaining high resource utilization
Solution Approach 2:
The system changes the parameter of workload selection from arbitrary or first-come-first-served to value-based selection using calculated preference factors. By changing this parameter, the system ensures that executed workloads contribute maximally to business profitability rather than simply filling available resource capacity
3Ease of operation
If priority ordering is based solely on first-come-first-served, then operational simplicity is maintained, but business profitability deteriorates due to inability to prioritize high-value workloads
Solution Approach 1:
The patent implements self-service prioritization where the workload placement system automatically calculates preference factors, determines priority orderings, and executes workloads without manual intervention. This maintains operational simplicity while dramatically improving profitability by having the system autonomously optimize workload selection based on business value
Solution Approach 2:
The system changes the prioritization parameter from simple queue position to calculated preference factor based on profitability and penalty information. This parameter change transforms the system from operationally simple but profit-poor to both operationally simple (automated) and profit-optimized
Data Source
AI summary
A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.