Pattern-Driven Scheduling for Network Graph Workloads
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Solution Overview
Problem
Existing graph processing frameworks lack adaptable techniques for partitioning heterogeneous graph workloads, leading to inefficient resource utilization and increased processing time.
Innovation Solution
A pattern-driven scalable scheduling framework that partitions network graphs into subsections based on user-defined patterns, using algorithms to identify elementary paths, apply rules, generate labels, and assign weights to allocate workloads efficiently across resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If partitions are measured or treated equally for processing, then the processing approach is simple and uniform, but the overall processing time of the graph increases
Solution Approach 1:
The graph is divided into multiple partitions or subgraphs, allowing parallel processing across different computing resources. This segmentation enables simultaneous processing of different graph portions, reducing overall processing time while maintaining manageable complexity through systematic division
Solution Approach 2:
Different partitioning strategies are applied to different regions of the graph based on their specific characteristics. High-degree nodes receive different treatment compared to low-degree nodes, optimizing processing efficiency for each local region while adapting to heterogeneous graph structures
2Adaptability or versatility
If conventional techniques are used for homogeneous graph workloads, then the implementation is straightforward, but the utilization or allocation of available resources is inefficient for heterogeneous graph workloads
Solution Approach 1:
The scheduling framework dynamically adapts to heterogeneous graph workloads by adjusting partitioning strategies and resource allocation based on real-time graph characteristics. The system modifies its behavior according to the specific properties of each workload, enabling efficient resource utilization across diverse graph types
Solution Approach 2:
The system changes key parameters such as partition size, processing depth, and resource allocation based on graph properties like node degree distribution and edge density. This parameter adaptation allows the framework to optimize performance for different heterogeneous graph workloads while maintaining a unified scheduling architecture
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a graph, applying a first algorithm to the graph to obtain at least one elementary path, applying a rule to each of the at least one elementary path to obtain a respective sanitized elementary path, applying a second algorithm, based on the respective sanitized elementary path, to obtain a respective labeled elementary path, applying a third algorithm to the respective labeled elementary path to identify at least one pattern, mapping a respective pattern of the at least one pattern to a respective graph subsection, applying a fourth algorithm to the respective graph subsection to assign a weight to the respective graph subsection, and allocating a processing of a workload associated with the respective graph subsection to a resource based on the weight. Other embodiments are disclosed.