Temporal Workload Partitioning for Cloud Container Nodes
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Solution Overview
Problem
Static deployment of application programs in cloud environments leads to uneven workload distribution, resulting in overutilization or underutilization of resources, particularly during peak usage times, and increased cross-traffic between network nodes, which existing technologies fail to effectively address.
Innovation Solution
A time-aware, recursive partitioning algorithm is implemented to dynamically allocate computing and network resources based on temporal performance metrics, organizing nodes in a hierarchical tree structure to maximize resource usage and minimize traffic flow, using techniques such as k-way partitioning or Fiduccia Mattheyses algorithms to optimize workload distribution across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static deployment of application programs is used, then deployment simplicity is maintained, but workload distribution becomes uneven causing overutilization or underutilization of resources
Solution Approach 1:
The patent implements dynamic workload assignment that adapts to changing temporal patterns. The system continuously monitors performance metrics and recalculates optimal container node assignments based on observed usage patterns, transforming the static deployment model into a dynamic one that automatically adjusts to peak and off-peak periods.
Solution Approach 2:
The system performs preliminary analysis of temporal patterns in performance metrics to predict future workload demands. By identifying seasonal or periodic patterns in advance, the system can pre-position container nodes on optimal host nodes before peak demand occurs, preventing resource bottlenecks rather than reacting to them.
2Device complexity
If static deployment is used, then system complexity is reduced, but cross traffic between network nodes increases
Solution Approach 1:
The patent segments the network topology analysis into hierarchical levels (e.g., rack level, switch level, host level). The recursive partitioning algorithm works through these segments systematically, assigning container nodes to host nodes while considering the hierarchical network structure, thereby reducing cross-traffic at each level of the hierarchy.
Solution Approach 2:
The system optimizes workload assignment locally at each host node based on its specific characteristics and current load conditions. Rather than applying a uniform global assignment strategy, the patent tailors assignments to local network conditions, placing containers on host nodes that minimize local traffic generation while maintaining overall system efficiency.
3Productivity
If temporal pattern analysis is implemented, then workload distribution is optimized, but computational overhead increases
Solution Approach 1:
The patent applies partial analysis by focusing on the most significant temporal patterns and performance metrics rather than analyzing all possible data. The recursive partitioning algorithm processes performance metrics selectively, identifying key patterns that drive workload optimization while ignoring less relevant variations, thereby reducing computational overhead.
Solution Approach 2:
The system uses the existing performance monitoring infrastructure to gather metrics, turning existing operational data into optimization insights without requiring separate measurement systems. The same monitoring mechanisms that track performance for operational purposes also provide the data needed for temporal pattern analysis and optimization.
Data Source
AI summary
For each node in a plurality of nodes corresponding to a particular computer network element, the performance metric data regarding the node based on a first time interval is received. The plurality of nodes is organized in a tree structure which comprises a plurality of spine nodes, a plurality of leaf nodes, a plurality of host nodes, and a plurality of container nodes. The metric data is applied for a recursive partitioning algorithm on the plurality of nodes to generate an allocation strategy for the plurality of container nodes. The allocation strategy defines a topology of the tree structure that maximizes usage of computing resources on each node based on the first time interval.