Task Allocation Matrix for Cloud Node Load Balancing

Solution Overview

Problem

Current container scheduling technologies face challenges in achieving balanced task allocation, leading to resource wastage due to idle or overloaded nodes in cloud computing environments, with existing solutions failing to effectively address this unbalanced allocation issue.

Innovation Solution

A task allocation method that involves initializing allocation parameters, performing simulated allocation to generate a candidate scheduling matrix, and determining a target scheduling matrix based on load balancing parameters to optimize task allocation across node devices, ensuring balanced resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If container scheduling methods allocate nodes for applications, then task allocation is performed, but some nodes become idle resulting in resource waste while other nodes become overloaded

Engineering Contradiction:

Improvetask allocation efficiencyVSAvoidresource waste due to idle nodes

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The patent transforms the discrete task allocation problem into a continuous optimization problem by introducing a scheduling matrix with continuous variables. This allows the use of simulated annealing algorithm to iteratively adjust allocation parameters, transitioning from static discrete allocation to dynamic continuous optimization, thereby achieving better load balancing and reducing resource waste.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements a feedback mechanism through the simulated annealing algorithm that continuously evaluates the scheduling matrix based on load balancing objectives. The algorithm iteratively adjusts the allocation scheme based on feedback from previous iterations, converging to an optimal or near-optimal solution that minimizes idle nodes and balances workload across the cluster.

Inventive Principle:

Principle #23Feedback

2Productivity

If container scheduling methods allocate nodes for applications, then task allocation is performed, but some nodes become overloaded due to excessive application tasks

Engineering Contradiction:

Improvetask allocation efficiencyVSAvoidnode overload

Core Design Contradiction:

ProductivityVSStress or pressure

Solution Approach 1:

The patent transforms the discrete task allocation problem into a continuous optimization problem by introducing a scheduling matrix with continuous variables. This allows the use of simulated annealing algorithm to iteratively adjust allocation parameters, transitioning from static discrete allocation to dynamic continuous optimization, thereby achieving better load balancing and reducing resource waste.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements a feedback mechanism through the simulated annealing algorithm that continuously evaluates the scheduling matrix based on load balancing objectives. The algorithm iteratively adjusts the allocation scheme based on feedback from previous iterations, converging to an optimal or near-optimal solution that minimizes idle nodes and balances workload across the cluster.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If traditional scheduling allocates entire applications as task units, then scheduling is simplified, but fine-grained resource allocation is lost leading to imbalance

Engineering Contradiction:

Improvescheduling simplicityVSAvoidallocation precision

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent segments the application task allocation into fine-grained units by introducing a scheduling matrix where each element represents a specific task-node assignment. This segmentation allows precise control over individual task allocations rather than treating entire applications as atomic units, enabling fine-grained resource allocation while maintaining scheduling manageability through matrix-based representation.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentEP4281863B1Task allocation method, apparatus, storage medium, and electronic device

Publication Date: 2024.12.04 ZHEJIANG DAHUA TECH CO LTD

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AI summary

Disclosed are task allocation method, an apparatus, a storage medium and an electronic device. The method includes: initializing allocation parameters, wherein the allocation parameters include the number of idle node devices in a target device cluster and the number of to-be-allocated task elements; performing simulated allocation on the to-be-allocated task elements to each idle node device for processing to generate a candidate scheduling matrix, wherein the candidate scheduling matrix is configured to indicate a simulated allocation result; obtaining a load balancing parameter corresponding to the candidate scheduling matrix, and determining a target scheduling matrix according to the load balancing parameter; andin response to the target scheduling matrix satisfying an allocation condition, allocating the to-be-allocated task elements to the idle node devices according to the target scheduling matrix. The present disclosure can solve the technical problem of unbalanced task allocation.