14 results about "Workflow scheduling" patented technology

The technology described herein is directed towards workload prioritization (ranking) in a cloud (e.g., private / hybrid) environment based on multi-criteria decision analysis using Mahalanobis distance-based variance-covariance matrix that takes into account the pair-wise correlation between criteria attributes from a dataset. The technology described herein calculates relative closeness values for alternatives (e.g., virtual machines), and can perform a final ranking of a group of virtual machines based on their associated relative closeness values. The workload prioritization process can be incorporated into a decision engine. The decision engine can be used in various use cases, including backup prioritization, shutdown prioritization during power failures, workflow scheduling prioritization, and the like.

This invention belongs to the field of cloud computing, specifically relating to a cost-based workflow scheduling method in an IaaS environment. The method includes: obtaining the execution time of each task in the workflow on different virtual machines, and the data communication time between different virtual machines; constructing a dynamic budget allocation model based on user-defined total cost constraints; merging the workflow into single parent-child task pairs to obtain a new directed acyclic graph (DAG); calculating the priority score of each task in the new DAG and sorting them to obtain a sorted list; calculating the cost constraint of each task sequentially according to the sorted list, and obtaining the currently allowed budget for scheduling the task based on the dynamic budget allocation model; calculating the earliest completion time of the task before and after copying the critical parent task on different virtual machines, and selecting the virtual machine with the smallest earliest completion time to place the task; this invention effectively reduces the workflow completion time.

The application discloses a geological survey project whole-process management system based on a workflow engine, relates to the field of automatic control of geological and mineral exploration, and comprises a central management control platform, a central workflow scheduling center, a cross-agency collaboration gateway and a distributed business database which are internally arranged, a field operation sensing subsystem, a portable geological survey terminal which is integrated with a double-frequency multi-constellation GNSS receiver board card, an intelligent checking calculation base which comprises a geological semantic checking engine and a multi-source spatial data fusion module, a resource and monitoring feedback subsystem which comprises a laboratory resource scheduler, and asynchronous communication between the central management control platform and the intelligent checking calculation base through a message queue bus.The application realizes real-time strong constraints of a process on quality through an evaluation system, solves the problem of inconsistent field positioning accuracy by using a BursaWolf model, and realizes optimal allocation of resources in the whole life cycle of a geological survey project through an incentive type scheduling index.

This application discloses a workflow processing system and method. It relates to the fields of cloud computing, automated task scheduling, and container orchestration. The system includes: a server, used to interact with users through a graphical interface, create workflow template files, and send the workflow template files to a scheduler; a scheduler, used to receive workflow instances, extract a directed acyclic graph (DAG) from the workflow instances, and control clients to execute workflow instances according to the DAG; and a client, used to execute tasks of N task nodes through N containers, updating the status of each task node to the server. This application solves the problem of low resource utilization in workflow scheduling in cloud-native environments in related technologies.

The application provides a workflow scheduling method and device, an execution engine, equipment and a program product. The method comprises the following steps: determining a current layer node based on a topological relationship between nodes in a target workflow and execution states and / or execution results of the nodes; the current layer node comprises a start node of the target workflow or a post-node of a previous layer node in each branch path of the target workflow; when the current layer node is not empty, executing each current layer node based on target attribute information of each current layer node. The above-mentioned workflow scheduling method can effectively improve the execution efficiency of the target workflow while ensuring the correct execution of the target workflow.

The application discloses a kind of based on improved ascidian algorithm's workflow optimization scheduling method.This method considers the time delay and reliability problem existing in workflow scheduling, utilizes reliability model to define the scheduling failure factor of workflow, and the weighted sum of time delay and failure factor is used as fitness function to evaluate workflow scheduling scheme, and the ascidian algorithm is improved, in population initialization, the standard deviation of population is used in the optimal population selected in multiple populations generated.In the updating of individual in population, chaos theory and updating algorithm are combined, the different method of using chaos mapping to replace pseudo-random sequence, and the leader and follower are updated using chaos mechanism, the global search speed in potential search area is improved, the speed of searching the optimal workflow scheduling scheme is improved, the edge server time delay is minimized, and the task scheduling reliability is improved, and finally the optimal workflow scheduling scheme is obtained.

The application provides an ERP intelligent workflow scheduling method based on multi-agent deep reinforcement learning, which draws on the idea of hierarchical control and distributed problem solving, and designs a collaborative decision-making framework of "expert consensus priority, conflict hierarchical arbitration" by using multi-agent cooperation. In addition, multi-objective optimization scheduling should avoid strategy conflicts between different optimization objectives, which may lead to system decision paralysis or serious performance decline. Therefore, the application decomposes the complex global multi-objective optimization problem into multiple parallel single-objective optimization sub-problems, which are learned by independent expert agents, ensuring the depth of decision-making in each optimization dimension, and then a higher-level comprehensive decision-making agent arbitrates the decision conflicts between experts, thereby effectively resolving strategy conflicts and improving the overall decision-making quality and robustness of the system.

This invention relates to the field of enterprise digital collaboration and workflow management, specifically to an enterprise-level, full-scenario, one-stop intelligent workflow collaboration service platform and method. The platform includes a full-scenario workflow semantic modeling module, a cross-system collaboration adaptation engine, a dynamic permission game control module, a workflow intelligent scheduling and optimization module, and a one-stop collaborative interaction front-end module. The method of this invention achieves closed-loop management of the entire enterprise workflow chain through core steps such as standardized semantic modeling across all business scenarios, establishment of cross-system collaborative links, dynamic permission allocation and conflict resolution, intelligent workflow scheduling optimization, and one-stop collaborative service output. Through core algorithms such as adaptation scoring, semantic similarity calculation, multi-role permission game, and constrained task scheduling, this invention can be widely applied to workflow collaboration management in various enterprise full-business scenarios, significantly improving the efficiency of enterprise business process execution and the level of digital management.

This application provides a job scheduling method, apparatus, electronic device, storage medium, and computer program product. The method includes: constructing a first directed acyclic graph (DAG) based on a first job flow, wherein the data input, task execution process, and data output of each computing task in the first job flow are represented by a first node in the first DAG, and the edge between every two first nodes in the first DAG represents the corresponding data flow direction; merging one or more first nodes in the same community of the first DAG into a second node based on a community detection algorithm to obtain a second DAG, wherein the edge between every two second nodes in the second DAG is used to indicate the dependency relationship between different communities of the first DAG; and scheduling one or more computing tasks corresponding to the same second node in the first job flow to the same computing cluster based on the second DAG.

The application provides a distributed workflow scheduling system and method, which is applied to the technical field of distribution and comprises at least one working node, a registration node and a control node, wherein the working node is used for sending a service interface registration instruction to the registration node, submitting a workflow task to the control node, receiving a subtask distributed by the control node and executing; the registration node is used for registering the service interface of the working node after receiving the service interface registration instruction, and sending the service interface information of the registered working node to the control node; the control node is used for receiving the service interface information of the working node, receiving the workflow task of the working node, determining the working node for executing each subtask, sending the subtask to the working node through the service interface of the working node, and feeding back through the service interface of the working node corresponding to the workflow task after obtaining the execution result of the workflow task. The application can realize distributed workflow scheduling and improve the processing speed of complex business.

The application provides a distributed workflow task scheduling method and system, and relates to the technical field of computers. The scheduling task nodes and dependency relationships are defined and stored in a drag-and-drop manner through a visual canvas. The Worker nodes with a normal ZooKeeper center hop state are screened, the node weights are calculated based on dynamic load balancing, and the tasks are distributed to the Worker nodes with the highest weights for execution. When a Worker node fails, the ZooKeeper node deletion event is listened to, the IP and port of the faulty Worker are identified, the task states of the un-terminated nodes of the faulty node are marked as needing fault tolerance and are re-allocated. When a Master node fails, the surviving Master takes over the workflow instance of the faulty Master based on a slot allocation mechanism. The task reporting address in the Worker is updated to the current Master, and the uncompleted tasks are re-dispatched. The overall usability, efficiency and stability of the workflow scheduling are improved, and the method is suitable for diversified business requirements.

This invention discloses a multi-workflow scheduling optimization method based on containers in a cloud-edge environment, belonging to the field of intelligent technology scheduling optimization. The method includes: establishing a multi-workflow scheduling optimization model; determining the model's optimization objectives, establishing an encoding scheme, and generating initialization codes; creating a dynamic allocation algorithm for containers and virtual machines; creating an enhanced multi-objective African Vulture algorithm based on the above steps; and applying the enhanced multi-objective African Vulture algorithm to solve the model. This invention, by establishing a multi-workflow scheduling optimization model and proposing a novel enhanced multi-objective African Vulture algorithm for solving the model, effectively addresses multiple optimization objectives in a two-layer architecture composed of virtual machines and containers. The algorithm provides an appropriate encoding scheme and proposes a dynamic allocation algorithm for containers and virtual machines based on it, optimizing resource allocation and service deployment for containers and virtual machines. The algorithm's archiving mechanism and adaptive grid mechanism can solve the problems of slow convergence speed and susceptibility to local optima in heuristic algorithms.

This invention relates to the field of underwater engineering construction technology and discloses an intelligent optimization method for the hoisting path of a hinged submerged raft unit. The method includes dividing the expected total hoisting cycle into multiple discrete time slices and constructing a three-dimensional predictive environment model based on multi-level water flow profile data; calculating the predicted tension of each grid node of the submerged raft by combining the hydrodynamic resistance model and the three-dimensional predictive environment model, generating a flexible deformation energy state matrix; inspecting the flexible deformation energy state matrix to determine local tension peaks; introducing rigid hanger tilt angle control variables and assigning non-equivalent rate commands to slices with peaks, generating a globally optimal equipment workflow scheduling table; executing according to the scheduling table, extracting the total predicted tension, verifying the deviation between the actual physical load equivalent mean calculated from the frequency converter driver feedback data and the total predicted tension; and truncating commands and triggering resampling to replan the remaining time slices when the deviation exceeds the limit.