Edge Server Load Evacuation via Hybrid Particle Swarm
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Solution Overview
Problem
Existing edge computing networks face challenges in load balancing, particularly when sudden bursts of traffic occur, as they often overlook resource constraints of edge servers, leading to increased response times and delayed task execution.
Innovation Solution
A network burst load evacuation method using a hybrid particle swarm algorithm to optimize load evacuation strategies by determining task sets for overloaded edge servers, considering time, resource availability, and queuing times, ensuring efficient task distribution across edge servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If load evacuation strategies are optimized using hybrid particle swarm algorithm, then system response speed and task execution efficiency are improved, but computational complexity and algorithm implementation difficulty increase
Solution Approach 1:
The load evacuation problem is segmented into multiple optimization dimensions including task migration timing, target server selection, and resource allocation. The hybrid particle swarm algorithm divides the search space into manageable components that can be optimized independently and then combined, making the complex optimization problem more tractable while maintaining high execution efficiency
Solution Approach 2:
The patent introduces a centralized controller as an intermediary that manages the hybrid particle swarm algorithm execution. This intermediary coordinates between edge servers, collects status information, and implements the optimized evacuation strategies, thereby reducing the implementation complexity at individual server levels while maintaining overall system efficiency
2Reliability
If tasks are migrated to target edge servers during burst load, then load balancing is improved, but network traffic and communication overhead increase
Solution Approach 1:
The patent implements selective task migration based on local server conditions. Instead of migrating all tasks uniformly, the system evaluates each task's characteristics, urgency, and resource requirements against target server capabilities. This localized optimization approach achieves effective load balancing while minimizing unnecessary network traffic by only migrating tasks that truly need relocation
Solution Approach 2:
The system performs preliminary evaluation of target server status and task compatibility before initiating migration. By pre-assessing network conditions, server capacity, and task priorities, the system可以避免 unnecessary migrations and reduces communication overhead while maintaining effective load balancing
3Reliability
If resource constraints of edge servers are considered in optimization, then task execution feasibility is improved, but calculation complexity increases
Solution Approach 1:
The patent transforms the complex multi-constraint optimization problem into a more manageable form by changing key parameters. The hybrid particle swarm algorithm uses standardized fitness functions that incorporate resource constraints (CPU, memory, storage) as weighted parameters rather than hard constraints, allowing the optimizer to navigate the solution space more efficiently while ensuring task execution feasibility
4Reliability
If multiple edge servers are involved in load evacuation, then system capacity and fault tolerance are improved, but coordination complexity and management overhead increase
Solution Approach 1:
The patent merges the coordination functions of multiple edge servers into a centralized control framework. The hybrid particle swarm algorithm operates under unified coordination that consolidates decision-making while distributing execution. This merging approach maintains fault tolerance through redundancy but reduces coordination complexity by providing a single point of control that manages server interactions
Data Source
AI summary
The present invention discloses a network burst load evacuation method for edge servers, which takes a time and average penalty function of all tasks performed by the edge system as a minimum optimization goal. This method not only takes into account the fairness of all users in the system, but also ensures that the unloading tasks of all users in the system can be completed in a relatively shortest time, and a new quantitative measure is proposed for improving user QoS response. In the implementation process of the algorithm in the present invention, a particle swarm algorithm is used to solve an optimal target of the system, This algorithm has a fast execution speed and high efficiency, and is especially suitable for a scene of an edge computing network system, so that when a sudden load occurs, an edge computing network system can respond in a very short time and complete the evacuation of the load, which greatly improves the fault tolerance and stability of the edge network environment.