Cloud Workload Scheduling via Value Increase Scheme
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Solution Overview
Problem
Current cloud computing services face challenges in cost optimization due to disparate incentives across the XaaS stack, leading to inefficient server resource utilization and lack of incentives for statistical multiplexing, resulting in volatility in resource demand and management complexity.
Innovation Solution
A value increase scheme is implemented, using processor calculations based on nominal equivalent resource usage data, infrastructure usage data, effective production capacity, and demand elasticity curves to schedule workload service operations, providing universal pricing and sustained usage discounts across all services, thereby smoothing resource consumption and reducing volatility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate incentives are provided across different XaaS services, then each service can be optimized independently, but resource utilization becomes inefficient and management complexity increases
Solution Approach 1:
The patent implements a universal incentive scheme that applies across all XaaS services (IaaS, PaaS, SaaS) rather than having separate incentive structures for each service type. This unified approach allows the system to maintain adaptability for service-specific optimization while reducing management complexity through a single standardized incentive framework that works across the entire cloud service portfolio.
2Ease of operation
If cloud services provide on-demand resource allocation, then user flexibility is improved, but resource demand volatility increases and reduces overall utilization efficiency
Solution Approach 1:
The patent implements dynamic incentive schemes that adjust pricing and discounts based on real-time resource utilization patterns and demand conditions. The system dynamically modifies incentive levels to encourage workload scheduling during underutilized periods while maintaining user flexibility to access resources on-demand, thereby smoothing demand volatility and improving overall resource utilization without sacrificing operational ease.
3Reliability
If cloud providers maintain excess capacity to handle demand spikes, then service reliability is improved, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor resource utilization patterns, demand trends, and incentive effectiveness. This feedback loop allows the system to adjust incentive schemes in real-time to encourage workload scheduling that better utilizes existing capacity, reducing the need for excess provisioning while maintaining service reliability through optimized resource allocation based on actual utilization data.
Data Source
AI summary
An example method includes receiving a nominal equivalent resource usage data, an infrastructure usage data, an effective production capacity, a demand elasticity curve, and workload scheduling constraints across a plurality of accounts. The method includes calculating an equivalent resource utilization based on the nominal equivalent resource usage data, the infrastructure usage data, and the effective production capacity. The method includes calculating a potential value increase for a service based on the workload scheduling constraints, the nominal equivalent resource usage data, the effective production capacity, and the demand elasticity curve. The method includes calculating a value increase scheme for the service based on the potential value increase and sending the value increase scheme to a user workload device. The method includes receiving a workload constraint from the user workload device and scheduling a workload service operation based on the infrastructure usage data, the value increase scheme, and the workload constraint.