Adaptive MapReduce Job Profiles for Dynamic Data Scaling
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Solution Overview
Problem
Existing infrastructures face challenges in efficiently processing large volumes of unstructured data, as they often fail to adapt to varying dataset sizes, leading to changes in job profiles and performance parameters, which can result in suboptimal resource allocation and execution times in MapReduce frameworks.
Innovation Solution
The implementation of scaling parameters to modify job profiles in response to changing dataset sizes, allowing for the computation of performance parameters and resource allocation to meet specific performance goals, such as target completion times, within distributed processing frameworks like MapReduce.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing infrastructures process large volumes of unstructured data using fixed job profiles, then the processing can be performed, but the resource allocation becomes suboptimal and execution times increase due to inability to adapt to varying dataset sizes
Solution Approach 1:
The patent applies dynamics by making the job profile adaptive rather than fixed. The system dynamically adjusts job profile characteristics (such as number of map tasks, reduce tasks, and resource allocation) based on the actual dataset size. This allows the infrastructure to optimize resource allocation and execution time in response to varying data volumes, resolving the contradiction between processing efficiency and execution time.
Solution Approach 2:
The patent changes parameters of the job profile based on dataset size. Specifically, it adjusts parameters like the number of map tasks, reduce tasks, and resource distribution according to the detected data volume. This parameter adaptation enables optimal resource utilization and reduced execution time for different data sizes, addressing the suboptimal performance of fixed profiles.
2Device complexity
If the job profile is fixed regardless of dataset size, then the infrastructure complexity is reduced, but the adaptability to varying data sizes deteriorates leading to suboptimal resource allocation
Solution Approach 1:
The system implements self-service by automatically detecting dataset size and autonomously adjusting job profile parameters without manual intervention. The infrastructure self-adapts to varying data volumes by automatically modifying task allocation and resource distribution, maintaining simplicity while gaining adaptability. This resolves the contradiction by enabling the system to adjust complexity levels based on actual needs.
3Productivity
If resource allocation is not adapted to dataset size, then the allocation simplicity is maintained, but the processing efficiency deteriorates due to suboptimal resource distribution
Solution Approach 1:
The patent changes resource allocation parameters dynamically based on detected dataset size. It adjusts the number of map tasks, reduce tasks, and resource distribution proportions according to data volume. This parameter adaptation optimizes processing efficiency by allocating resources proportionally to the actual workload, resolving the contradiction between efficiency and allocation complexity.
Data Source
AI summary
A job profile is received that includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job. The map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. The characteristics of the job profile include at least one particular characteristic that varies according to a size of data to be processed. The at least one particular characteristic of the job profile is set based on the size of the data to be processed.