50results about How to "Solve the unreasonable distribution" patented technology

The invention discloses a cloud market multi-type resource allocation pricing mechanism and an implementation algorithm thereof. The cloud market multi-type resource allocation pricing mechanism relates to a cloud market multi-type resource model, cloud market mechanism design and the implementation algorithm of a cloud market mechanism. According to the cloud market multi-type resource allocation pricing mechanism and the implementation algorithm thereof, user benefits and social benefits in the cloud service market are considered, the cloud market multi-type resource allocation pricing mechanism based on a mechanism theory is provided, the problem that cloud market resources are allocated unreasonably because design is not conducted from the perspective of users is solved, effective allocation of multi-type resources is achieved under the request of complex user tasks, and unification of user utility, resource provider utility and market and social benefits is shown. Besides, the cloud market multi-type resource allocation pricing mechanism meets the requirements for individual rationality, budget equilibrium and stimulation compatibility, and finally the implementation algorithm for guiding the cloud market mechanism is provided.

An embodiment of the invention provides a resource scheduling method and a resource scheduling system and relates to the field of cloud calculation. The resource scheduling system comprises a management node and physical nodes. According to the resource scheduling method, by the aid of the management node, a resource set corresponding to each application program is acquired according to the scheduling policy of a user when an objection of a scheduling policy is achieved and then is sent to the each physical node; by the aid of each physical node, the minimum quantity of resources distributed to each virtual machine on the corresponding physical node are determined, the left quantity of resources are distributed to every virtual machine on the corresponding physical node proportionally, and the total quantity of resources obtained by each virtual machine after distribution are sent to the management node; by the aid of the management node, resource utilization rate of each physical node is acquired according to the corresponding total quantity of resources of each virtual machine and the current resource utilization amount of each virtual machine on the corresponding physical node, and resource load of each physical node is scheduled according to the resource utilization rate. The resource scheduling method and the resource scheduling system are applied to virtualized resource scheduling and can solve the problem of unreasonable resource distribution caused by an incompatible scheduling technology.

The embodiment of the present invention discloses a method, device, server, and medium for allocating seeding positions, wherein the method includes: obtaining the type of container for the items associated with the order task when picking; The target seeding position that matches the type of the container; the target seeding position is assigned to the order task, and the target seeding position is used to carry the container. The embodiment of the present invention can solve the problem of unreasonable allocation of seeding positions in the prior art, and can achieve the effect of rationally allocating seeding positions according to the type of container, thereby reducing the impact on picking efficiency and improving the convenience and safety of picking operations sex.

The invention discloses a service chain parallel deployment method and device based on deep reinforcement learning, and the method comprises the steps: carrying out the mathematical modeling of an offline service chain deployment problem, so as to obtain a mathematical formula of the service chain deployment problem; selecting a placement server position for the shared VNF in all the service chains according to the mathematical formula, and selecting the server position of the shareable VNF through the DQN in deep reinforcement learning to generate a plurality of sub-service chains; and connecting the plurality of sub-service chains into a complete service chain through a shortest path principle, and selecting to deploy a server for the VNF without the appointed placement position. According to the method, the problem that the VNF in the service chain is ignored due to serial deployment and the unreasonable distribution is caused by the correlation between the service chains is solved,the sharing rate and the utilization rate of resources are effectively improved, deep reinforcement learning is adopted, and the calculation complexity is reduced.

The invention discloses a rear-mounted rear-drive transmission system for a bus chassis. The rear-mounted rear-drive transmission system comprises an engine, a clutch, a first speed reducer, a speed changer, a first transmission shaft, a second transmission shaft, a third transmission shaft and a rear axle, wherein a transmission shaft supporting base and a main speed reducer are arranged on the rear axle; the engine is connected with the input end of the clutch and is arranged behind the rear axle; the output end of the clutch is connected with the input end of the first speed reducer; the output end of the first speed reducer is connected with one end of the transmission shaft supporting base through the first transmission shaft; the other end of the transmission shaft supporting base is connected with the input shaft of the speed changer located at the front end of the bus chassis through the second transmission shaft; the output shaft of the speed changer is connected with the input end of the main speed reducer through the third transmission shaft; and the output end of the main speed reducer is connected with the wheels through a suspension control arm. According to the rear-mounted rear-drive transmission system disclosed by the invention, the problems that an engine is mounted on the rear part of a shorter bus, the load distribution of a front axle and a rear axle is unreasonable and long-distance operation of the speed changer is difficult are solved, and the operating mechanism of the speed changer is simplified.

The invention discloses a link load balancing method for multi-service flow QoS guarantee in a software-defined network, and belongs to the technical field of software-defined network architecture load balancing calculation. In the SDN environment, the present invention utilizes the global advantage of SDN centralized control, combines the improved K shortest path algorithm with QoS guarantee, takes the load balance degree of the link as the objective function, and takes the QoS parameters of the service as the constraint condition, in the Choose the best path among the K shortest paths. The present invention realizes the highly differentiated processing of the network for service flow processing, provides resources for different services on demand, and increases the flexibility of the service for network requirements; it can effectively improve the network throughput and flow acceptance rate, and at the same time reduce the selection path The delay and packet loss rate of the link. Therefore, the present invention has obvious advantages in terms of the utilization efficiency of network bandwidth resources; in terms of service quality, it can guarantee the basic service requirements of services requiring service guarantee.