Cache Node Per-Job Data Consumption Tracking
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Solution Overview
Problem
In virtual data processing environments, managing data consumption rates across multiple virtual machines and processing jobs is challenging due to latency issues exacerbated by competition for data access and high traffic levels, making it difficult to determine accurate data consumption rates and allocate resources effectively.
Innovation Solution
A cache node system that tracks read completions for virtual machines and allocates them to individual processing jobs, allowing for the determination of data consumption rates on a per-job basis, thereby facilitating resource allocation and improving data throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machines share computing resources in a virtualized environment, then resource utilization efficiency is improved, but data access latency increases due to competition for data access and high traffic levels
Solution Approach 1:
The patent segments data access operations by implementing per-job read completion tracking and allocation. The job manager divides data access latency measurement into individual job segments, allowing each processing job to be monitored and managed separately. This segmentation enables the system to identify and prioritize critical job data access operations, reducing latency for important tasks while maintaining overall resource sharing efficiency.
Solution Approach 2:
The patent introduces a job manager as an intermediary component between virtual machines and data storage systems. This intermediary tracks read completions, allocates data access resources, and manages traffic flow on behalf of multiple virtual machines. By acting as a mediator, the job manager coordinates data access requests, reduces competition-induced latency, and maintains efficient resource utilization across the virtualized environment.
2Productivity
If multiple virtual machines process multiple processing jobs simultaneously, then processing throughput is improved, but it becomes difficult to determine accurate data consumption rates for individual jobs
Solution Approach 1:
The patent implements segmentation of data consumption measurement by tracking read completions at the individual job level. The job manager maintains separate counters and metrics for each processing job, allowing precise measurement of data consumption rates even when multiple virtual machines and jobs operate simultaneously. This per-job tracking enables accurate resource allocation and cost accounting in multi-tenant virtualized environments.
Solution Approach 2:
The patent establishes a feedback mechanism where the job manager continuously monitors read completion events and updates data consumption rate metrics for each processing job. This real-time feedback allows the system to dynamically adjust resource allocation based on actual data consumption patterns, ensuring both high processing throughput and accurate measurement of individual job performance.
3Ease of operation
If read completions are tracked at the virtual machine level, then monitoring simplicity is improved, but resource allocation precision deteriorates because individual job data consumption cannot be distinguished
Solution Approach 1:
The patent segments read completion tracking from the virtual machine level down to the individual processing job level. The job manager maintains separate tracking structures for each job within virtual machines, enabling precise resource allocation decisions. This fine-grained segmentation allows the system to allocate resources based on actual job requirements rather than virtual machine averages, improving both monitoring capability and allocation precision.
Solution Approach 2:
The patent implements a universal job manager component that handles multiple functions: tracking read completions, measuring data consumption rates, allocating resources, and generating metrics. This multi-functional approach maintains monitoring simplicity by providing a single management interface while delivering precise per-job resource allocation through its comprehensive tracking and allocation capabilities.
Data Source
AI summary
Embodiments disclosed herein provide systems, methods, and computer readable media for managing data consumption rate in a virtual data processing environment. In a particular embodiment, a method provides, in a cache node of a host system, identifying read completions for one or more virtual machines instantiated in the host system, with the one or more virtual machines processing one or more processing jobs. The method further provides allocating the read completions to individual processing jobs of the one or more processing jobs and accumulating the read completions on a per-job basis, with the cache node determining a data consumption rate for each processing job of the one or more processing jobs.