17 results about "Cloud computing resource allocation" patented technology

The invention discloses an unmanned aerial vehicle agent end side cloud hierarchical decision-making method and system, and the method comprises the steps: S1, deploying a compressed lightweight deep learning model at an unmanned aerial vehicle terminal, and executing a real-time preliminary decision of a simple or emergency task; s2, constructing a dynamic task unloading mechanism, and unloading the complex task from the unmanned aerial vehicle terminal to an edge computing node; s3, carrying out preprocessing and local decision making on the task by the edge computing node, and further judging whether the task needs to be forwarded to the cloud data center or not; s4, the cloud data center carries out global optimization decision making for calculation-intensive or data-intensive tasks; and S5, monitoring network and computing node states in real time, and dynamically adjusting end, edge and cloud computing resource allocation to realize dynamic balance of decision precision and real-time response. According to the technical scheme disclosed by the invention, the computing power and energy consumption bottleneck of the unmanned aerial vehicle terminal can be effectively relieved, the computing resource allocation is dynamically optimized, and the real-time response capability and decision-making precision of complex task processing of the unmanned aerial vehicle are improved.

The invention relates to the technical field of data management, and discloses a cloud computing-based technical service integrated management system, which comprises a resource management module, a service monitoring module, a security control module, a data processing module and a user interface module, when dynamic allocation of cloud computing resources is carried out, resource load indexes are monitored in real time through an elastic telescopic unit in the resource management module, and resource demand estimation is carried out in combination with the prediction model, so that the system can actively adjust resource supply according to the fluctuation trend of technical service demands, the hysteresis of a resource allocation strategy is avoided, and the resource allocation efficiency is improved. Dynamic matching between cloud computing resource allocation and a real load is ensured, the resource utilization efficiency is improved, multi-dimensional monitoring data is collected and associated through a fault detection unit and a log analysis unit in the service monitoring module, so that the system can quickly position a core problem from an isolated alarm event, and the system reliability is improved. And the troubleshooting complexity and the time cost are reduced.

The present application relates to a satellite / cloud computing resource allocation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring a target computing task and a real-time resource status; inputting the target computing task and the real-time resource status into a resource allocation model; and by means of the resource allocation model, outputting a satellite / cloud resource allocation strategy, the resource allocation model comprising a resource estimation module and a resource optimization module, wherein the resource estimation module is used for determining pre-estimated resources to be occupied for the target computing task, and the resource optimization module is used for determining the satellite / cloud resource allocation strategy on the basis of the real-time resource status and the pre-estimated resources to be occupied. In the present application, inputting a target computing task and a real-time resource status into a resource allocation model, determining, by means of a resource estimation module, pre-estimated resources to be occupied, and in full consideration of a dynamic resource environment, outputting a satellite / cloud resource allocation strategy by means of a resource optimization module and on the basis of the real-time resource status and the pre-estimated resources to be occupied can effectively improve the utilization rate of computing resources.

The invention relates to the field of intelligent scheduling allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning, which adopts a space-time prediction algorithm based on multi-head attention and gating mechanism optimization to design time coding and space coding. The spatial relationship and interaction between time sequence characteristics of the computing power load and edge server nodes are captured, and meanwhile, a multi-head attention mechanism and expansion causal convolution are combined, so that instantaneous computing power load fluctuation can be captured, and the long-term trend of the computing power load can be mined; therefore, a reliable basis is provided for subsequent computing power scheduling by predicting an accurate computing power load. The invention designs an alternating direction multiplier method based on genetic algorithm optimization, which is not only suitable for a nonlinear and multi-constraint optimization problem, but also can be expanded to a larger-scale distributed edge node cloud computing system, and meanwhile, a global optimal solution is quickly approached through the genetic algorithm, so that the quality of an initial solution is improved, and model convergence is accelerated; and the distributed collaborative allocation scheduling efficiency is improved.

The invention relates to the technical field of cloud server computing, and particularly discloses a cloud computing resource allocation method based on multi-source data optimization, which comprises the following steps: S01, a resource requested by a user responds to a cloud server, and first-batch pre-allocation information of multiple groups of resources is generated; determining initial evaluation information of the user; s02, a multi-source data optimization self-adaptive model is built, resources requested by the user firstly respond to the multi-source data optimization self-adaptive model, a self-adaptive request then responds to the cloud server, and the multi-source data optimization self-adaptive model conducts self-adaptive adjustment on the user information and the resources requested by the user; s03, accessing a real-time pricing and resource pool state interface of the cloud platform based on the generated multi-group resource first batch pre-distribution information or multi-group resource second batch pre-distribution information, and obtaining supply cost information; the invention aims to adjust the priority based on a multi-source data optimization scheme, so that not only can the requirements of most users be met, but also the requirements of specific users can be met.

The invention provides a software development method and system based on Internet and cloud computing. According to the method, time series data fed back by software development requirements are collected in real time through the Internet, multi-dimensional analysis is conducted through a cloud computing platform, the association strength of time distribution characteristics of requirement change frequency and user emotion parameters is extracted, and an evolution graph containing requirement priority distribution characteristics and change path topology is generated; dynamically distributing an optical signal transmission channel matched with the priority based on the weight of the map node, and carrying out priority weight marking on the original time sequence data; and finally, dynamically binding a cloud computing resource allocation proportion in combination with the weight value of the marked data and the topological node connection strength, and generating a task scheduling instruction. Through cross-layer cooperation of the demand evolution graph and the photon integrated circuit, accurate dynamic matching of the software development demand priority and the cloud computing resources is realized. According to the technical scheme provided by the invention, the efficiency and real-time performance of software development can be improved.

The application relates to the technical field of cloud computing resource allocation, and discloses a heterogeneous computing power cooperative allocation method and system, which comprises the following steps: S1, characteristic modeling is performed on all-network heterogeneous computing power nodes in advance, a heterogeneous computing power characteristic matrix M containing computing power architecture types, available computing power scales, current load rates, available bandwidths, local storage reserves and real-time power consumptions is constructed, wherein the heterogeneous computing power nodes include four types of heterogeneous resource nodes, namely, general-purpose CPUs, general-purpose GPUs, AI acceleration NPUs and edge computing nodes; the application constructs a multi-dimensional quantitative heterogeneous adaptation model, three quantitative calculation formulas are introduced to respectively realize the calculation of an adaptation score, a total allocation cost and a dynamic correction coefficient, multiple optimization targets, such as architecture adaptability, power consumption, time delay, migration overhead and computing power fragmentation, are included in the allocation model, the accurate matching of the heterogeneous computing power nodes and to-be-allocated tasks can be realized, the performance advantages of different architecture heterogeneous computing powers can be fully exerted, and the overall computing power resource utilization rate can be effectively improved.

Embodiments of the present specification provide a resource allocation method, a cloud computing resource allocation method, a computing device, a computer readable storage medium and a computer program product, the resource allocation method is applied to a resource allocation platform, the resource allocation platform is provided with a plurality of resources, and the method comprises: acquiring a resource acquisition request sent by a client; according to resource attribute information of the multiple resources and the resource acquisition request, at least one to-be-allocated resource is screened out from the multiple resources, and the resource attribute information is obtained through configuration based on historical allocation information of the resources, a first historical configuration attribute and a second historical configuration attribute; the first historical configuration attribute refers to attribute information of the server for resource configuration, and the second historical configuration attribute refers to attribute information of the historical client for resource configuration; and allocating the at least one to-be-allocated resource to the client. The resource attribute information is determined based on the historical allocation condition of the resources, so that the resource utilization rate in the allocation process is improved.

Techniques for event driven harvesting and analysis of cloud computing resources in a cloud computing environment, comprising: obtaining information about at least one cloud computing event in the cloud computing environment; determining if the at least one event is related to the allocation of storage to a cloud computing resource; in response to determining the at least one event is related to the allocation of storage to a cloud computing resource, requesting data from the cloud computing resource; and analyzing the data for the presence of security risks and vulnerabilities.

The application relates to the field of intelligent scheduling and allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning. The application adopts a time-space prediction algorithm based on multi-head attention and gate mechanism optimization, designs time encoding and space encoding, captures the time sequence characteristics of computing power load and the spatial relationship and interaction between edge server nodes, simultaneously combines the multi-head attention mechanism with the expansion causal convolution, can capture the instantaneous computing power load fluctuation, can mine the long-term trend of the computing power load, and thus predicts the accurate computing power load to provide a reliable basis for subsequent computing power scheduling. The application designs an alternating direction multiplier method based on genetic algorithm optimization, is suitable for nonlinear and multi-constraint optimization problems, can be extended to a larger-scale distributed edge node cloud computing system, simultaneously rapidly approaches the global optimal solution through the genetic algorithm, improves the initial solution quality, accelerates model convergence, and improves the distributed collaborative allocation and scheduling efficiency.

The present invention discloses a method and system for the end-edge-cloud layered decision-making of an intelligent drone. The decision-making method includes: S1, deploying a compressed lightweight deep learning model on the drone terminal to perform real-time preliminary decisions on simple or urgent tasks; S2, building a dynamic task offloading mechanism to offload complex tasks from the drone terminal to the edge computing node; S3, the edge computing node pre-processing and making local decisions on the task, and further determining whether the task needs to be forwarded to the cloud data center; S4, the cloud data center making global optimization decisions for computationally intensive or data-intensive tasks; S5, real-time monitoring of network and computing node status, and dynamic adjustment of end, edge, and cloud computing resource allocation to achieve a dynamic balance between decision-making accuracy and real-time response. The technical solution disclosed in the present invention can effectively alleviate the computing power and energy consumption bottlenecks of the drone terminal, dynamically optimize computing resource allocation, and improve the real-time response capability and decision-making accuracy of the drone in processing complex tasks.

The embodiment of the present application relates to the technical field of cloud computing, and discloses a cloud computing resource management method, which comprises the following steps: obtaining tenant behavior data of a target tenant in a cloud platform; determining cloud platform use capability and cloud resource utilization efficiency of the target tenant according to the tenant behavior data; and adjusting cloud computing resource allocation of the target tenant according to the cloud platform use capability and the cloud resource utilization efficiency. Through the above method, the embodiment of the present application realizes personalized management of tenants of a multi-tenant cloud platform and improves the private cloud service experience of the tenants.

The invention discloses a cloud computing big data real-time processing method and system fused with an AI technology, and relates to the technical field of big data real-time processing methods, and the method comprises the steps: collecting original data from a plurality of heterogeneous data sources through employing a data access interface, and obtaining an original data set; preprocessing the original data set by adopting a distributed computing framework, and obtaining a preprocessed data set; constructing a load state prediction model based on a machine learning algorithm and the preprocessed data set, inputting the preprocessed data set, and outputting a load state prediction result; adjusting the cloud computing resource allocation quantity in the original data set based on the load state prediction result to obtain a resource allocation strategy; establishing a data processing channel by adopting a stream processing technology based on a resource allocation strategy, and executing an identification task in combination with an AI algorithm to obtain an analysis result; generating a decision report of the specific application scene based on the analysis result; the Kafka Connect connector is configured to be connected with a plurality of heterogeneous data sources, so that efficient integration of multi-source data is realized.

The application discloses a resource allocation method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining a current computing task, a task attribute of the current computing task, a current load capacity of a computing device in an edge-cloud network, a historical computing task and a task attribute of the historical computing task; determining a target historical computing task with the highest similarity to the current computing task according to the task attributes of the current computing task and the historical computing task; dividing computing resources required by the target historical computing task into a plurality of computing resource packages according to the target historical computing task and the current load capacity; and distributing the plurality of computing resource packages to the computing device in the edge-cloud network by using an iterative algorithm until a preset iteration stop condition is met, so as to obtain a target resource allocation strategy. According to the embodiment of the application, the speed of cloud computing resource allocation optimization can be improved, and the efficiency of resource allocation optimization can be improved.

The embodiment of the invention provides a resource allocation method, a resource application method, a cloud platform and computing equipment, and the resource allocation method comprises the steps: receiving a resource allocation request sent by a terminal, the resource allocation request carries account information of a target account logging in the terminal and resource information requesting to allocate target cloud computing resources; according to the account information and the resource information, determining a preset time length and an accumulated time length of the target account for the target cloud computing resource, the accumulated time length being obtained by performing time length accumulation on a time length in which a resource use state of the target cloud computing resource is an enabled state; and under the condition that the accumulated duration does not reach the preset duration, allocating the target cloud computing resource to the target account. Whether the target cloud computing resources are allocated to the target account or not is determined according to the accumulated duration and the preset duration, a set of clear resource allocation standards is established, and reasonable cloud computing resource allocation is achieved.

The embodiment of the invention provides a self-adaptive distribution method and device for cloud computing resources, equipment and a medium, and relates to the technical field of cloud computing, and the method comprises the following steps: obtaining a cloud computing task set and a virtual machine set; determining a priority corresponding to each cloud computing task; determining an initial resource allocation scheme corresponding to each cloud computing task according to the priority; constructing a corresponding task dependency relationship graph according to the initial resource allocation scheme; and searching an optimal path from the task dependency graph, obtaining a target computing resource allocation scheme corresponding to the cloud computing task, and executing a resource allocation operation corresponding to the target computing resource allocation scheme, thereby improving resource utilization efficiency, reducing resource waste, better meeting user requirements, and improving user experience. And the accuracy and rationality of cloud computing resource allocation are improved.

The present disclosure discloses a computer implemented method and system to evaluate adequacy of cloud computing resources allocation. The method includes extracting a set of features and a set of target variables from a historic dataset stored in a database. Further, the method includes generating a plurality of simple linear regression (SLR) model and a multiple linear regression (MLR) model. Furthermore, a respective value of each target variable of the set of target variables is determined based on the SLR model and MLR model. Thereafter, the method includes calculating a new value of each feature of the set of features followed by generating adequacy score by comparing the new value of each feature of the set of features with a desired value of each feature of the set of features.