Probabilistic Task Hierarchy for Production Event Simulation
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Solution Overview
Problem
The development, testing, and demonstration of monitoring systems for distributed computer clusters are hindered by the lack of data and the impracticality of using real production systems for testing, as replaying logged events is limited to actual sequences and cannot simulate various scenarios like normal or atypical behavior effectively.
Innovation Solution
A simulation system that uses a task hierarchy with probabilistic task commands to simulate production events in a controlled environment, allowing for the creation of scenarios such as moderate load, hardware failures, or underutilization, enabling the testing and demonstration of monitoring systems without occupying production resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real production systems are used for testing monitoring systems, then authentic production data can be obtained, but production resources are occupied and testing cannot be performed independently
Solution Approach 1:
The patent creates a simulated production environment that copies the essential characteristics and event structures of real production systems. Instead of using actual production systems for testing, the invention generates synthetic event data that mimics production behavior, allowing independent testing while preserving data authenticity for validation purposes.
Solution Approach 2:
The patent segments the testing function from the production system by creating a separate simulation environment. The monitoring system under test operates independently on simulated event streams, while production systems continue their normal operations. This segmentation allows parallel operation without resource conflicts.
2Adaptability or versatility
If logged events are replayed for testing, then real production sequences can be analyzed, but various scenarios like normal or atypical behavior cannot be simulated effectively
Solution Approach 1:
The patent implements dynamic scenario generation where the simulation can adapt its event sequences based on configured scenarios. Instead of statically replaying fixed logged events, the system dynamically generates varied event streams that can represent normal operations, atypical conditions, failure scenarios, and edge cases, providing both versatility and information diversity.
Solution Approach 2:
The patent changes key parameters of event generation to create different scenarios. By adjusting parameters such as event frequency, event types, error rates, and load conditions, the simulation can effectively model various operational scenarios while maintaining realistic event structures, thus achieving scenario adaptability without losing event sequence diversity.
3Productivity
If a simulation environment is created to test monitoring systems, then production resources remain available, but the complexity of setting up and maintaining the simulation increases
Solution Approach 1:
The patent introduces an event generation component as an intermediary between the simulation needs and the monitoring system under test. This intermediary generates synthetic events that can be configured to match production patterns without requiring a full production clone. The intermediary abstracts the complexity, providing simple controls for scenario configuration while maintaining realistic test conditions.
4Reliability
If monitoring systems are tested in production environments, then real-world performance can be validated, but testing costs increase and development cycles are extended
Solution Approach 1:
The patent enables preliminary testing in the simulation environment before deploying to production. Monitoring systems can be developed, configured, and validated against various scenarios including failure conditions in the low-cost simulation phase. This preliminary action catches issues early in the development cycle, reducing the need for extensive production testing and accelerating overall development timelines.
Data Source
AI summary
Techniques to simulate production events are described. Some embodiments are particularly directed to techniques to simulate production events based on randomization across a distribution of production events. In one embodiment, for example, an apparatus may comprise a simulation application operative to simulate one or more commands in a simulated environment using a task hierarchy, the simulation application comprising a configuration component, a command generation component, and an execution component, wherein simulating the one or more commands comprises executing one or more task commands. The configuration component may be operative to receive the task hierarchy from the data store, the task hierarchy comprising a plurality of task entries, each task entry comprising a list of task entries or a task command, the list of task entries comprising probabilities associated with each task entry in the list of task entries, wherein task commands correspond to the simulated environment representing a production environment. The command generation component may be operative to determine the one or more task commands by traversing through the task hierarchy based on the associated probabilities until the one or more task commands are reached. The execution component may be operative to execute the one or more task commands as one or more simulated commands in the simulated environment. Other embodiments are described and claimed.