Data Center Scheduler Dynamic Pricing
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Solution Overview
Problem
The infrequent and lengthy negotiations between data center operators and web-service managers for computing resources lead to inflexible and inefficient market operations, resulting in high risks and costs due to sunk costs and stale pricing models.
Innovation Solution
A data streaming service that provides real-time value information on computing resources, allowing for incremental and frequent purchases by transforming raw data into meaningful metrics using algorithms, enabling timely decision-making and efficient market operations through a feedback loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lengthy negotiations are used to provision computing resources, then pricing models can account for sunk costs and power costs, but the terms become inflexible and the market operations become inefficient
Solution Approach 1:
The patent implements dynamic pricing models that continuously adjust resource allocation based on real-time demand patterns, power costs, and sunk cost recoveries. The system transitions from static long-term contracts to dynamic short-term provisioning, allowing the market to adapt pricing and allocation decisions to current conditions while maintaining accuracy in accounting for historical costs.
Solution Approach 2:
The system incorporates feedback mechanisms where real-time data about resource consumption, power costs, and demand patterns continuously informs pricing adjustments. This feedback loop enables the market to learn from actual usage patterns and refine pricing models, improving both accuracy and efficiency simultaneously.
2Reliability
If long-term contracts are signed for computing resources, then sunk costs and power costs can be recovered, but the resources become bound and the infrastructure loses flexibility
Solution Approach 1:
The patent segments long-term contracts into multiple short-term provisioning cycles. Instead of binding resources to single long-term agreements, the system creates a series of flexible short-term contracts that can be independently adjusted, terminated, or renewed. This segmentation maintains cost recovery mechanisms while restoring operational flexibility to the infrastructure.
Solution Approach 2:
The system implements dynamic contract terms that can adjust in real-time based on demand conditions. Resources are provisioned through flexible terms that allow modification without penalty, enabling the infrastructure to adapt to changing requirements while still recovering sunk costs through dynamic pricing adjustments.
3Device complexity
If infrequent negotiations are conducted for resource provisioning, then negotiation complexity is reduced, but the pricing information becomes stale and market inefficiencies increase
Solution Approach 1:
The patent establishes continuous negotiation and pricing adjustment mechanisms that operate automatically based on predefined criteria. Rather than infrequent manual negotiations, the system maintains continuous monitoring and adjustment of pricing and allocation decisions, ensuring pricing information remains current while automating the process to manage complexity.
Solution Approach 2:
The system implements self-service pricing mechanisms where automated algorithms continuously adjust resource allocation and pricing based on real-time data. This eliminates the need for frequent manual negotiations while maintaining fresh pricing information, as the system autonomously responds to changing conditions without human intervention.
4Productivity
If real-time data streaming is implemented for computing resources, then timely decision-making is enabled and market efficiency improves, but the system complexity increases
Solution Approach 1:
The patent implements a universal data streaming framework that handles multiple types of information (pricing, demand, power costs, sunk costs) through a single integrated system. This multi-functional approach enables timely decision-making across various aspects of resource provisioning while consolidating complexity into a unified platform rather than requiring separate systems for each data type.
Data Source
AI summary
An exemplary data stream includes value information for use by consumers of global computing resources in making requests for global computing resources. An exemplary method includes receiving information about data center resources from one or more data centers, based at least in part on the information estimating value information for consumption of computing resources of the one or more data centers and streaming the value information via a network. An exemplary medium or media includes instructions to instruct a computing device to receive, from a data stream, value information for computing resources of one or more data centers, to format the value information for display and to issue requests for consumption of at least some of the computing resources. Other methods, devices and systems are also disclosed.