Large-Scale Network Probing with Frank-Wolfe Budget Optimization
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Solution Overview
Problem
Monitoring large-scale networks, such as cloud infrastructure, is challenging due to the infeasibility of reserving fixed resources for monitoring, leading to inaccurate metric estimation and computational inefficiencies in existing frameworks.
Innovation Solution
A framework that optimizes the probing strategy using A- and E-optimal experimental designs, combined with the Frank-Wolfe algorithm, to determine an optimal probe allocation vector, minimizing estimation error while adhering to a fixed probe budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a fixed budget for monitoring is reserved, then monitoring resources are allocated, but measurement accuracy deteriorates due to insufficient probes for large-scale networks
Solution Approach 1:
The patent changes the parameters of probe allocation by using optimal experimental designs (A-optimal and E-optimal) to determine the best probe placement and sampling frequencies. This transforms the fixed budget approach into a dynamic allocation strategy that maximizes measurement accuracy for any given budget level, resolving the contradiction between limited resources and accurate metric estimation.
Solution Approach 2:
The patent applies partial action by selecting a subset of critical network paths for monitoring rather than attempting to monitor all paths uniformly. By identifying and prioritizing the most important paths based on network topology and traffic patterns, the system achieves accurate metrics with a fraction of the total possible probes, making the fixed budget feasible for large-scale networks.
2Productivity
If conventional monitoring frameworks are used, then monitoring is implemented, but computational efficiency deteriorates due to scalability issues
Solution Approach 1:
The patent performs preliminary action by pre-computing optimal probe allocation strategies based on network topology and traffic matrices before actual monitoring begins. The optimal experimental designs are calculated in advance, and the resulting probe placement and sampling schedules are stored for efficient execution during runtime, avoiding computationally intensive calculations during time-critical monitoring operations.
Solution Approach 2:
The patent segments the monitoring problem into independent path-level optimizations rather than treating the entire network as a single complex system. By formulating the problem as separate optimization tasks for different network paths and using decomposition techniques, the system reduces computational complexity and enables scalable monitoring of large networks without excessive computation time.
Data Source
AI summary
Systems and techniques are provided for monitoring of a large-scale network, such as a large-scale network supporting cloud infrastructures. A minimum fixed probe allocation and/or a sampling budget for monitoring may be set. A probing and/or sampling strategy may be optimized in order to measure network metrics, such as error metrics associated with the latency of a network, with a known accuracy, given a particular probe allocation. The systems and techniques provided may leverage particular designs to determine efficient probing strategies for the network, while simultaneously conserving computing resources. In some examples, instead of using these frameworks and techniques directly in production networks, a scalable and near optimal approximation technique based on the Frank-Wolfe algorithm may be used.