Network Resource Allocation for Large-Scale Max-Min Fairness
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Solution Overview
Problem
Existing network-resource allocators face challenges in achieving real-time max-min fair resource allocation due to significant operational latency and complexity, leading to under-utilization and unfairness, especially in large-scale cloud environments with dynamic workloads.
Innovation Solution
A suite of network-resource allocators is developed, including geometric binner, approximate waterfiller, adaptive waterfiller, and equi-depth binner, which formulate multi-resource max-min fair allocation as a single-shot optimization, allowing for fast and efficient allocation with varying trade-offs between fairness, efficiency, and speed, using sorting networks and convex optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sophisticated optimization technologies are used for network-resource allocation, then fairness of prioritization among clients is improved, but operational latency increases due to computation complexity
Solution Approach 1:
The patent segments the network resources into discrete units and processes allocation demands sequentially in a controlled manner. By dividing the complex optimization problem into manageable steps (processing each demand individually, allocating resources incrementally), the system achieves fair prioritization while reducing computational latency through structured problem decomposition.
Solution Approach 2:
The patent implements preliminary actions by pre-establishing resource allocation policies and priority frameworks before actual allocation occurs. The system prepares allocation strategies in advance and executes them efficiently during runtime, reducing the need for complex real-time computations while maintaining fairness in resource distribution.
2Productivity
If real-time resource allocation is implemented, then productivity is improved, but fairness of allocation deteriorates due to computational constraints
Solution Approach 1:
The patent employs dynamic resource allocation strategies that adapt to changing network conditions in real-time. The system dynamically adjusts allocation decisions based on current demand patterns and resource availability, achieving both speed and fairness by continuously optimizing allocation policies without requiring complex re-computation from scratch.
Solution Approach 2:
The patent changes key allocation parameters such as priority weights, allocation thresholds, and resource sharing factors to balance real-time performance requirements with fairness constraints. By adjusting these parameters dynamically, the system achieves productive real-time allocation while maintaining equitable distribution among clients.
3Reliability
If complex optimization computations are performed, then allocation fairness is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complex optimization computations from the main allocation process and handles them through simplified, rule-based mechanisms. By separating the allocation decision logic from complex calculations, the system achieves fair allocation outcomes through straightforward policies rather than computationally intensive optimization algorithms.
Solution Approach 2:
The patent implements self-service allocation mechanisms where the system automatically manages resource distribution based on predefined policies without requiring complex external optimization computations. The allocation process serves itself by applying simple, maintainable rules that inherently produce fair outcomes, reducing overall system complexity while preserving allocation equity.
Data Source
AI summary
A method for allocating a plurality of network resources to a plurality of network-access demands of a plurality of network guests comprises (a) receiving the plurality of network-access demands; (b) for each of the plurality of network-access demands (i) dynamically computing, from among the plurality of network resources, a resorted order of resources associated with the network-access demand, and (ii) for each network resource associated with the network-access demand, increasing, in the re-sorted order, an allocation of the network resource to the network-access demand until the network-access demand is saturated, and freezing the allocation of each of the plurality of network resources to the saturated demand; and (c) outputting the frozen allocation of each of the plurality of network resources for each of the plurality of network-access demands.