Task Execution Caching via Signature Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face inefficiencies in processing and re-executing jobs that involve dependent tasks or datasets, leading to increased node utilization and slowdowns, especially in the context of 'Big Data' applications where timely information processing is critical.
Innovation Solution
The system divides jobs into tasks, assigns signatures to each task and dataset, searches for cached results, and sends tasks to task executors for processing, utilizing a directed acyclic graph (DAG) to manage dependencies and avoid redundant calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If jobs are processed in a straightforward manner without caching, then task execution is simple and direct, but processing efficiency deteriorates and node utilization increases when redundant tasks are encountered
Solution Approach 1:
The system performs preliminary action by caching task results and their signatures before they are needed. When a task is executed, its signature is stored along with the result. Upon subsequent job execution, the system checks the cache for matching signatures before performing computation, thereby avoiding redundant processing and improving productivity without proportionally increasing system complexity.
Solution Approach 2:
The system uses copying by creating signature representations of tasks and datasets. Instead of comparing entire datasets or task definitions, the system generates compact signature copies that uniquely represent each task's input parameters and dependencies. This allows efficient cache lookup and comparison while maintaining processing efficiency and managing system complexity.
2Speed
If tasks are divided and executed on separate nodes, then processing speed improves for independent tasks, but coordination overhead increases when tasks have dependencies
Solution Approach 1:
The system introduces an intermediary caching layer that mediates between task execution nodes. The cache stores task signatures and results, allowing nodes to independently execute tasks while the intermediary handles dependency resolution and result retrieval. This maintains high processing speed for independent tasks while reducing coordination complexity through centralized cache management.
Solution Approach 2:
The system implements feedback by checking the cache for existing task results before executing new tasks. The cache provides feedback about previously computed results, allowing the system to avoid redundant execution and coordinate dependencies efficiently. This feedback mechanism maintains processing speed while managing coordination complexity through intelligent task scheduling.
3Reliability
If all tasks are recalculated for each job execution, then result accuracy is ensured, but processing time increases significantly for repeated jobs
Solution Approach 1:
The system performs preliminary computation and caches results with their signatures before they are needed again. When the same task parameters are encountered, the cached results are reused, ensuring accuracy matches the original computation while dramatically reducing processing time for repeated jobs with identical or overlapping datasets.
Solution Approach 2:
The system creates signature copies of task parameters and compares them to determine if cached results are valid. This copying approach ensures result accuracy by verifying parameter matches while avoiding redundant computation, thereby reducing processing time significantly for repeated jobs without sacrificing reliability.
4Quantity of substance
If the system stores and processes large amounts of data, then information availability increases, but processing efficiency decreases due to increased data volume
Solution Approach 1:
The system extracts only the essential signature information from large datasets and tasks, storing these compact representations in the cache. When processing jobs, the system compares signatures rather than processing the entire data volume, thereby maintaining high information availability while dramatically improving processing efficiency by working with extracted key features rather than complete datasets.
Data Source
AI summary
Certain disclosed embodiments provide improved systems and methods for processing jobs. The method comprises steps including receiving, from a client device over a network, information representing a job and generating at least two tasks representative of the job. The method further comprises, for each task, assigning, by a processor, a signature to the task representative of whether the task has been processed, determining at least one dataset related to the task, and assigning a signature to the determined at least one dataset. The method further comprises searching, by the processor, a data structure for the task signature, and based on the searching, sending the task over a network to a task executor for processing or locating results associated with the task. The method further comprises sending, over a network, a job result to the client device. Systems and computer-readable media are also provided.