Online multi-target resource allocation method combining bipartite graph matching method and constraint solver

Abstract

The invention discloses an online multi-target resource allocation method combining a bipartite graph matching method and a constraint solver. The online multi-target resource allocation method specifically comprises the following steps: reading a resource holder set and a resource requester set; enabling the newly arrived entity set and the overdue entity set to update the current available entity set holding resource entity set and the request resource entity set; if all newly arrived request resource entities can be directly allocated to nearby held resource entities by using a greedy method, and when the overdue entities do not need to be optimally allocated again at the moment, directly performing on-site allocation; if the number of the overdue entities exceeds a set threshold value, obtaining a maximum allocation score by adopting a bipartite graph matching method; and performing multi-objective optimization by using a constraint solver to obtain a distribution result.

Description

technical field [0001] The invention relates to the technical field of resource allocation, in particular to an online multi-objective resource allocation method combining a bipartite graph matching method and a constraint solver. Background technique [0002] The reason for using the allocation algorithm is that the resources to be allocated are limited, and resource allocation must be done reasonably. To illustrate with an example, for example, to allocate server resources to users, the memory, bandwidth, and computing resources of the server are limited, and the location information of the user and the server can only be allocated when the user is within a certain distance from the server. A method needs to be found to use Fewer servers serve more users. This type of problem can be understood as a variable size vector binning problem (VSVBP), which is not necessarily the same as the "capacity" of each box compared with the ordinary vector binning problem (VBP). When sol...

AI Technical Summary

Problems solved by technology

Second, this method does not consider multi-objective optimization

That is, in addition to the allocation quantity, other features are not optimized, and a single target result may be better, but the overall allocation result is not ideal

For example, in a specific problem, the optimization of location information is not added, which may lead to the situation that when the allocation is completed, the number of allocations is large but the total distance is too large

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