Task Timeout Determination Based on Input Data Characteristics
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Solution Overview
Problem
Managing and optimizing the provisioning, administration, and utilization of resources in large-scale data centers with varying demand and supply dynamics, while ensuring customer satisfaction and resource utilization efficiency, is complex due to fluctuating pricing and diverse customer needs.
Innovation Solution
A resource management system that schedules tasks based on need-by time, determines timeout durations based on input data characteristics, and optimizes resource allocation and pricing policies to minimize costs and meet deadlines, using dynamic cluster optimization and configurable workflow services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource allocation is optimized dynamically based on demand and supply, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic resource allocation by continuously adjusting resource provisioning based on real-time demand and supply conditions. The system monitors resource utilization metrics and automatically scales resource allocation up or down to optimize efficiency while adapting to changing workloads, thereby resolving the contradiction between improved productivity and increased system complexity through intelligent automation.
Solution Approach 2:
The system employs feedback mechanisms where resource utilization data is collected, analyzed, and used to inform subsequent allocation decisions. This closed-loop control enables the system to learn from past performance and continuously optimize resource distribution, achieving high utilization efficiency without requiring manual intervention in the complex allocation process.
2Adaptability or versatility
If pricing policies are made flexible to accommodate diverse customer needs, then customer satisfaction is improved, but management complexity increases
Solution Approach 1:
The patent implements flexible pricing policies by dynamically adjusting pricing parameters based on resource utilization, demand conditions, and customer profiles. The system can offer variable pricing models including pay-per-use, reserved capacity, and tiered pricing structures, allowing it to adapt to diverse customer needs while automating the complex pricing management through algorithmic decision-making.
3Reliability
If resource provisioning is increased to meet peak demand, then service reliability is improved, but resource waste increases during low demand periods
Solution Approach 1:
The system resolves this contradiction by implementing dynamic provisioning that automatically scales resource allocation based on real-time demand monitoring. During peak demand periods, resources are provisioned to ensure service reliability, while during low demand periods, excess resources are released or scaled down to eliminate waste, maintaining reliable service without continuous over-provisioning.
Data Source
AI summary
Methods and systems for task timeouts as a function of input data size are disclosed. A definition of a task is received. The definition of the task indicates a set of input data for the task. A timeout duration for the task is determined based on the set of input data. The timeout duration varies with one or more characteristics of the set of input data. The execution of the task is initiated. The execution of the task is stopped if the execution of the task exceeds the timeout duration.