Distributed Big Data Optimization via Segmented Linear Programming
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Solution Overview
Problem
In distributed big data environments, optimizing the distribution of resources across products is complicated by large and complex data sets with numerous constraints, making it difficult to determine the optimal distribution efficiently.
Innovation Solution
The method involves partitioning the data into manageable pieces, generating mathematical representations of each partition, combining these representations into a single linear programming file, and using a solver to obtain an optimized solution, allowing for parallel processing to increase efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data are stored and analyzed together in a distributed big data environment, then complete resource distribution optimization can be achieved, but the data volume becomes too large to be handled efficiently
Solution Approach 1:
The patent divides the large-scale resource distribution optimization problem into multiple smaller sub-problems by partitioning the network into clusters and further dividing constraints and decision variables. Each sub-problem can be solved independently and then combined to form the complete solution, making the computation tractable while maintaining optimization accuracy.
2Reliability
If all constraints and decision variables are included in the analysis, then optimal resource distribution is achieved, but the computational complexity increases significantly
Solution Approach 1:
The patent segments the complete set of constraints into multiple subsets assigned to different clusters, and divides decision variables into groups that can be optimized independently. This segmentation reduces the computational complexity of each individual optimization problem while ensuring all constraints are satisfied through coordinated solving of sub-problems.
Solution Approach 2:
The patent introduces a hierarchical dimension to the optimization problem by organizing clusters at one level and constraint subsets at another level. This multi-dimensional structure allows the system to manage complexity by solving problems in layers rather than attempting to process all constraints simultaneously in a single dimension.
3Productivity
If data are partitioned into smaller pieces for processing, then computational efficiency improves through parallel processing, but the overall system coordination becomes more complex
Solution Approach 1:
The patent partitions the network into multiple clusters that can process optimization sub-problems in parallel. Each cluster independently solves its assigned sub-problem using local constraints and decision variables, then results are aggregated to form the complete solution, enabling efficient parallel processing while maintaining systematic coordination.
Data Source
AI summary
Systems and methods for network optimization in a distributed big data environment are provided. According to an aspect of the invention, a processor performs an optimization method by dividing a data set into a plurality of partitions. For each of the partitions, the processor generates a mathematical representation of a model by associating input data with elements of the model, wherein the mathematical representation includes an objective and at least one constraint. The processor forms a master objective by combining the objectives for the partitions, and forms a set of master constraints by combining the constraints for the partitions. The processor then generates an optimized solution based on the master objective and the master constraints.