Dynamic Cloud Resource Provisioning for Data Feed Efficiency
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Solution Overview
Problem
Traditional cloud computing platforms rely on static provisioning rules, which are inadequate for dynamic processing jobs and unscheduled requests, as they fail to adapt to changes in data feeds and computational requirements, leading to inefficiencies in resource allocation.
Innovation Solution
The system dynamically provisions and re-provisions computing devices based on data feed attributes and historic processing job data to optimize resource allocation, allowing for real-time adjustments to meet changing demands and execute multiple processing jobs simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static provisioning rules are used to allocate computational resources, then resource allocation is simple and predictable, but the system cannot adapt to dynamic data processing jobs and changes in data feed attributes
Solution Approach 1:
The system transitions from static provisioning rules to dynamic provisioning that automatically adjusts computational resources based on real-time monitoring of data feed attributes and processing job characteristics. The provisioning system continuously adapts resource allocation to match actual workload demands, enabling the system to handle dynamic data processing jobs effectively.
Solution Approach 2:
The system implements feedback mechanisms that monitor data feed attributes, processing job performance, and resource utilization. This feedback is used to automatically adjust resource provisioning decisions, allowing the system to adapt to changing conditions while maintaining efficient resource allocation through continuous optimization loops.
2Productivity
If computational resources are dynamically provisioned and re-provisioned based on data feed attributes and historic processing jobs, then computational efficiency and throughput are improved, but the provisioning system becomes more complex
Solution Approach 1:
The system analyzes historic processing jobs in advance to establish baselines and patterns of resource consumption. This preliminary analysis enables the dynamic provisioning system to make informed resource allocation decisions without requiring complex real-time calculations, thereby improving computational efficiency while managing system complexity through pre-computed insights.
3Productivity
If the system re-provisions computing devices to carry out multiple processing jobs simultaneously, then resource utilization and throughput increase, but the difficulty of managing and coordinating resources increases
Solution Approach 1:
The system implements self-service mechanisms where computing devices automatically monitor their own workload, resource utilization, and performance metrics. Devices can autonomously request additional resources or release unused resources without manual intervention, enabling efficient multi-job processing while simplifying resource management through automated self-regulation.
Data Source
AI summary
Systems and methods for improving computational efficiency of data processing and storage are disclosed. The system can identify computing devices capable of performing a data transformation process on a data feed of a data repository, and determine an amount of computational resources needed to perform the data transformation process on the data feed based on attributes of the data feed and computational resources used to process historic processing jobs associated with the data feed. The system can dynamically provision, while performing the data transformation process, a subset of the computing devices based on the amount of computational resources, and execute the data transformation process at the subset of the plurality of computing devices to process the data feed. The system can dynamically re-provision the subset of the plurality of computing devices based on a change in the attributes of the data feed.