Metadata-Driven Cluster Caching for Queue Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage caching techniques in computer clusters are inefficient due to limited capacity in high-performance cache tiers, leading to challenges in managing large data lakes and optimizing data storage and processing costs.
Innovation Solution
A method that leverages enriched metadata to determine job scores, storage footprints, and group jobs for processing, optimizing cache utilization by sorting queues based on job scores and storage footprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is moved into a high-performance cache tier for immediate manipulation, then processing speed is improved, but cache capacity is limited and storage costs increase
Solution Approach 1:
The patent changes the parameter of data prioritization by using job scores to dynamically determine which data should be cached. Instead of caching data based on fixed rules or first-come-first-served, the system calculates job scores based on multiple factors (data reuse potential, job priority, storage footprint) and prioritizes caching decisions accordingly, allowing the limited cache capacity to be allocated to the most valuable jobs.
Solution Approach 2:
The patent applies local quality by differentiating treatment of different jobs based on their characteristics. Each job receives a unique job score that reflects its specific needs and importance, allowing the cache to provide high-speed access to data for high-priority jobs while lower-priority jobs can access data from slower, cheaper storage tiers.
2Quantity of substance
If data is stored in different storage classes and tiers, then storage costs are reduced, but data access time and complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating job scores and determining optimal cache allocation before jobs actually execute. The system analyzes job metadata, predicts storage requirements, and prepares caching strategies in advance, so that when jobs need data, it is already in the appropriate storage tier, minimizing access delays.
Solution Approach 2:
The patent implements feedback by continuously monitoring cache usage patterns, job completion status, and storage tier performance. This feedback informs dynamic adjustments to job scoring and caching decisions, allowing the system to learn from past performance and optimize data placement across storage tiers over time.
3Productivity
If more data is processed in high-performance cache, then processing efficiency is improved, but storage costs and cache management complexity increase
Solution Approach 1:
The patent applies self-service by enabling the caching system to automatically manage itself through algorithmic job scoring and automated cache allocation decisions. The system autonomously determines which jobs should receive cache resources based on their metadata and performance characteristics, eliminating the need for manual cache management and reducing operational complexity.
Solution Approach 2:
The patent creates a universal job scoring framework that can evaluate and prioritize any type of data-intensive job regardless of its specific characteristics. The same scoring mechanism and caching strategy work across diverse workloads, making the cache management system universally applicable and simplifying operations by providing a single, unified approach to cache allocation.
Data Source
AI summary
A method, computer system, and a computer program for managing computer jobs in a queue is provided. This comprises extracting metadata from a new job received for processing and upon determining when a similar enriched metadata exists in a database. A job score and storage footprint may then be determined for the new job from the extracted metadata. It is then determined whether the new job can be grouped for processing with any other jobs already placed on a queue. The new job is then added to the queue based on the new job's score and footprint, and whether it can be grouped with other jobs. The queue is then updated and sent to a scheduler for further processing.