Multi-Core Load Balancer for 5G Task Scheduling
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Solution Overview
Problem
Current load balancing techniques for multi-core processors in 5G and B5G networks are inefficient, requiring significant effort to optimize task allocation and energy consumption, especially when upgrading from single-core to multi-core architectures, and lack effective automated methods for software partitioning and data synchronization.
Innovation Solution
An energy-aware load balancing method that divides tasks into task-jobs, determines optimal core configurations based on profile data and execution metrics, and schedules these task-jobs to minimize energy consumption and balance computational load across cores, using a load balancer integrated with a multi-core processor system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If automated task allocation and core configuration methods are implemented, then energy consumption is reduced and processing time is decreased, but device complexity increases due to the need for load balancer integration and real-time monitoring capabilities
Solution Approach 1:
The load balancer automatically performs task allocation and core configuration without requiring manual intervention or extensive software development efforts. The system self-monitors workload characteristics and dynamically adjusts task distribution across cores, enabling energy-efficient operation while reducing the complexity burden on developers and users.
Solution Approach 2:
The load balancer implements real-time monitoring of workload characteristics and system performance metrics, using this feedback to dynamically optimize task allocation and core configuration. This closed-loop control enables the system to adapt to changing conditions and maintain energy efficiency automatically.
2Productivity
If multi-core architectures are adopted for 5G and B5G networks, then computational capacity and throughput are improved, but software partitioning and data synchronization challenges increase
Solution Approach 1:
The load balancer automatically segments tasks into appropriate units and allocates them to specific cores based on workload characteristics. This automated partitioning eliminates the need for manual software restructuring, allowing developers to leverage multi-core architectures without extensive software modification efforts.
Solution Approach 2:
The load balancer serves multiple functions simultaneously: task allocation, core configuration, performance monitoring, and optimization. This multi-functional approach consolidates complexity into a single component that works across different multi-core configurations and workloads.
3Productivity
If real-time load balancing is implemented, then task allocation efficiency is improved and energy consumption is reduced, but processing overhead and system complexity increase
Solution Approach 1:
The load balancer performs preliminary analysis of workload characteristics and pre-configures optimal task allocation strategies before execution. By anticipating workload patterns and pre-establishing allocation rules, the system reduces real-time decision-making overhead while maintaining high allocation efficiency.
Data Source
AI summary
A method for load balancing in a multi-core processor of an electronic device, the method including dividing, by the electronic device, a first task and at least one second task into a plurality of task-jobs, determining, by the electronic device, at least one core configuration for each of the plurality of task-jobs, scheduling, by the electronic device, each of the plurality of task-jobs on at least one core of the multi-core processor based on the at least one core configuration to generate an execution schedule, and executing, by the multi-core processor, the plurality of task-jobs according to the execution schedule.