I/O Profiling in Distributed Infrastructure
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Solution Overview
Problem
In distributed computing systems, measuring and improving input/output (I/O) workload efficiency is challenging due to the complexity of distributed infrastructure, especially in multi-tenant scenarios where service level agreements (SLAs) require guaranteed quality-of-service (QoS) criteria, making it difficult to ensure efficient I/O performance.
Innovation Solution
A method to profile I/O behavioral characteristics by executing workloads in a distributed infrastructure, using a computer program product to analyze and predict QoS behavior, thereby improving workload efficiency and system design for better QoS delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If distributed infrastructure is implemented on a computing platform with tens to hundreds of physical processing elements, then processing power and system capability are improved, but I/O measurement becomes more challenging and workload efficiency improvement becomes more difficult
Solution Approach 1:
The patent segments the distributed infrastructure into multiple virtual machines or containers, each with isolated I/O measurement capabilities. This allows I/O behavior to be measured and analyzed at the virtualization layer rather than attempting to measure across the entire distributed system, simplifying the measurement process while maintaining processing power.
Solution Approach 2:
The patent introduces an intermediary I/O measurement tool or agent that sits between the physical processing elements and the workloads. This intermediary captures and analyzes I/O behavior without interfering with the actual processing operations, making measurement feasible in complex distributed environments.
2Reliability
If service level agreements with guaranteed quality-of-service criteria are implemented, then tenant performance requirements are satisfied, but I/O workload efficiency improvement becomes more difficult due to compliance requirements
Solution Approach 1:
The patent performs preliminary I/O behavioral characterization and QoS requirement analysis before deploying workloads. By pre-configuring measurement parameters and QoS thresholds, the system can automatically monitor and adjust to maintain compliance without requiring complex real-time intervention, thus managing complexity while ensuring reliability.
Solution Approach 2:
The patent implements continuous feedback loops that monitor I/O performance against QoS criteria and automatically adjust workload distribution or resource allocation. This closed-loop control maintains service level agreements while simplifying management by automating compliance enforcement rather than requiring manual complexity.
3Measurement precision
If comprehensive I/O behavioral characteristics are extracted and analyzed, then QoS prediction accuracy is improved, but measurement time and computational overhead increase
Solution Approach 1:
The patent applies partial measurement by focusing on the most critical I/O behavioral characteristics rather than measuring all possible parameters. By identifying and measuring only the key performance indicators that significantly impact QoS, the system achieves sufficient measurement accuracy while reducing measurement time and computational overhead.
Solution Approach 2:
The patent performs preliminary profiling of I/O workloads to identify characteristic behavioral patterns before actual measurement. This pre-characterization allows the system to use simplified measurement models during operation, achieving accurate QoS prediction without the need for comprehensive continuous measurement, thus reducing time loss.
Data Source
AI summary
Techniques for profiling input/output (I/O) characteristics in a computing system implemented with distributed infrastructure. In one example, a method comprises the following steps. Behavioral characteristics are extracted for a set of factors associated with a targeted system in a distributed infrastructure by causing execution of one or more input/output workloads in accordance with one or more entities and the targeted system. At least a portion of the extracted behavioral characteristics is utilized to determine whether one or more subsequent workloads satisfy one or more quality-of-service criteria when executed in accordance with at least one of the one or more entities and the targeted system.