Tensor-Based Resource Scheduling for Distributed Computing Systems
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Solution Overview
Problem
Current resource management and scheduling in distributed computing systems face inefficiencies due to the large and increasing number of work requests and resources, requiring improved parallelization techniques to effectively match resource requests with consumable resources across multiple time intervals.
Innovation Solution
The method employs tensors to represent resource availability, requests, and capacity, using resource tensors, request tensors, and capacity tensors to schedule resources efficiently, with updates based on request placement, completion, or resource release, and determines the best fit through suitability scores calculated by cosine similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource scheduling methods are used to manage large numbers of work requests and resources, then the system can handle basic scheduling tasks, but the scheduling efficiency and processing speed become insufficient due to computational challenges
Solution Approach 1:
The patent replaces traditional mechanical resource scheduling algorithms with tensor-based mathematical operations. Resource states, requests, and scheduling decisions are represented as tensors, enabling parallel computation through matrix operations. This substitution transforms sequential scheduling logic into vectorized operations that can be executed simultaneously across multiple processors, dramatically improving scheduling efficiency and reducing processing time for large-scale distributed systems.
Solution Approach 2:
The patent introduces tensor dimensions to represent resource hierarchies and scheduling states, adding mathematical dimensions to the scheduling problem. By representing resources, requests, and scheduling decisions as multi-dimensional tensors, the system can leverage linear algebra operations to process scheduling information across multiple dimensions simultaneously, enabling efficient handling of complex resource allocation scenarios that would be computationally intensive using traditional methods.
2Speed
If parallelization techniques are applied to improve resource scheduling performance, then processing speed increases, but system complexity and power consumption increase
Solution Approach 1:
The patent creates a universal tensor-based scheduling framework that can handle multiple resource types, scheduling policies, and system configurations through a single mathematical model. The tensor operations provide a unified approach that works across different hardware architectures and scaling scenarios, reducing the need for specialized parallelization code for each case. This universality allows the system to achieve high processing speeds through efficient, general-purpose linear algebra operations rather than complex, architecture-specific parallel algorithms.
3Quantity of substance
If more resources are allocated to handle increasing work requests, then system capacity increases, but scheduling complexity and computational overhead increase
Solution Approach 1:
The patent changes the parameter representation from individual resource states to tensor-based mathematical structures. By representing resource capacities, usage states, and scheduling decisions as tensors with specific dimensionalities, the system can manage large numbers of resources through compact mathematical representations. Linear algebra operations on these tensors automatically handle the complexity of coordinating numerous resources, transforming what would be exponentially complex scheduling logic into manageable matrix operations that scale gracefully with system size.
Data Source
AI summary
There is provided a method and apparatus for vectorized resource scheduling using multi-dimensional hierarchical arrays with a uniform data type. According to embodiments, the hierarchy of resources may be represented as a multi-dimensional array of a uniform data type in a scheduling context. This data structure is known as a tensor. In various embodiments, a tensor may be used to model each of resource usage, requested resources, resource capabilities, tenant resource usage, etc. There is provided tensor scheduling operations (e.g. tensor scheduling algorithms) to make use of this resource model. Some embodiments may exploit specialized hardware, such as GPUs and tensor-processing units, in order to accelerate the tensor scheduling operations.