Image Matching using Weighted Graph Matching Algorithm

Pavithra, R.; P, Sugantha Priyadharshini.; Hemanandhini, I. G.

doi:10.1109/icaccs51430.2021.9442037

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The instantiated tasks of network slicing can be decomposed into network function instantiation sub-tasks. Based on the knowledge space, each sub-task can be mapped to a computing node in space or ground using the dynamic graph matching technology 9 , which ensures that its computing capability and transmission capacity match the resource requirements of the sub-tasks, and the network load is balanced. Each sub-task is connected as needed in the form of a service chain 10 to construct a logical sub-network that meets the requirements of different application scenarios, realizing the high dynamic and multidomain coordinative orchestration for network slicing in the satellite-terrestrial integrated network.…”

Section: High Dynamic End-to-end Network Slicing Management Technologymentioning

confidence: 99%

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

LIU,

SONG,

et al. 2023

Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manu

View full text Add to dashboard Cite

Section: High Dynamic End-to-end Network Slicing Management Technologymentioning

confidence: 99%

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

LIU,

SONG,

et al. 2023

Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manu

View full text Add to dashboard Cite

“…The distribution policy provided by the proposed algorithm can be summarized as an optimal assignment problem [26]: There are n servers and one management server in the system (set S ). The management server has a set of task queues of length m (set J), from which n tasks are taken out.…”

Section: A Problem Statementmentioning

confidence: 99%

Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

Hou

Meng

et al. 2022

IEEE Access

View full text Add to dashboard Cite

When the server cluster is processing concurrent task requests, if the performance difference among servers is not fully considered, task allocation will be unreasonable, which will lead to an increase in task making span and a decrease in cluster resource utilization. As one of the core technologies of server cluster, load balancing is used to balance the load of each server by allocating tasks to each server through an algorithm before the task processing. Therefore, this paper proposes a dynamic load balancing algorithm based on optimal matching of weighted bipartite graph. First, we constructed a bipartite graph with servers and tasks as vertices. The management server collects the load indicators of each server in the cluster in real time, using the real-time processing rate of each server as the load indicator. Each edge of the bipartite graph is determined by comparing the expected completion time of the tasks with the load of each server. The degree of matching between each task amount and each server load capacity is defined as the weight matrix of the edges, and the bipartite graph is weighted to construct a weighted bipartite graph. The Kuhn-Munkres algorithm was used to solve the optimal matching of the weighted bipartite graph, and the optimal assignment of tasks to servers was achieved based on the result of the optimal matching. The proposed algorithm fully considers the differentiation of each task amount and each server load capacity. By building a server cluster example and conducting comparison experiments, it is demonstrated that the algorithm can achieve load balancing of the server cluster and improve the resource utilization efficiency of the cluster, while offsetting the extra time overhead caused by the algorithm.

show abstract

Graph Matching Networks Meet Optimum-Path Forest: How to Prune Ensembles Efficiently

Jodas,

Passos,

Rodrigues

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Image Matching using Weighted Graph Matching Algorithm

Cited by 4 publications

References 9 publications

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

Resource management architecture and technology for coordination of computing and networking in satellite-terrestrial integrated network

Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

Graph Matching Networks Meet Optimum-Path Forest: How to Prune Ensembles Efficiently

Contact Info

Product

Resources

About