17 results about "Cloud workflow" patented technology

The invention discloses a hybrid heterogeneous cloud workflow scheduling method based on reinforcement learning, and belongs to the technical field of cloud computing. The method comprises the following steps: aiming at a cloud workflow scheduling problem, by taking minimization of completion time as a target and taking cost and resources as constraints, a three-dimensional collaborative constraint model is constructed, and the cost constraints comprise server-free function budget and virtual machine budget; integrating the hyper-heuristic framework into a reinforcement learning algorithm; an improved reinforcement learning algorithm is adopted to solve the cloud workflow scheduling problem, optimal execution resources are selected, and an optimal scheduling scheme is obtained; and performing real-time scheduling according to the optimal scheduling scheme, and introducing a deviation feedback mechanism to monitor an execution error in real time. According to the method, the workflow scheduling problem is decomposed into a closed-loop optimization process of state perception and action decision, dynamic environment perception, multi-target tradeoff and online strategy optimization are deeply fused, the global search function is achieved, local optimization can be achieved, the algorithm complexity is low, and robustness is high.

The invention relates to the technical field of cloud native software engineering, in particular to an intelligent cloud workflow generation management platform fused with a large language model, and the platform comprises a working condition modeling module which is used for automatically constructing a system protocol model comprising a formalized design protocol and an expected performance target based on stock codes, real-time traffic and telemetry data; the adversarial refining module is used for realizing automatic generation, verification and correction of candidate workflow codes through an adversarial iterative loop of a generative language model and a verification engine until no new counter example is generated; and the deployment decision module is used for performing quantitative risk assessment based on the iteration history and executing automatic deployment decision and authorization. According to the invention, the problems of dependence on manpower, low efficiency and unstable quality in the traditional development process are solved, and automation, quantification and high reliability of the whole workflow generation and management process are realized.

The invention discloses a double-target cloud workflow scheduling method under an edge heterogeneous cluster, which constructs a double-target optimization model aiming at an edge heterogeneous cluster application scene, and maximizes the service quality while satisfying the energy consumption constraint. A scheduling algorithm based on distributed mixed integer programming is designed on the basis of decomposition of an original problem, energy consumption and service quality are optimized according to processor node characteristics and load conditions, and scheduling of an edge heterogeneous cluster is achieved. The edge heterogeneous computing cluster scheduling can be effectively balanced in the aspects of multi-objective optimization, heterogeneous resource adaptation, real-time performance, dynamic performance and the like, so that an efficient computing solution is provided for the fields.

The invention belongs to the technical field of cloud workflow scheduling, and particularly relates to a reliable cloud workflow scheduling method with uncertain task execution time, which specifically comprises the following steps of: (1) setting a scheduling priority for each task in a workflow; (2) searching available active virtual machines in the cloud system for the ready tasks according to the scheduling sequence; (3) finding a plurality of available active virtual machines, and deciding to use a certain virtual machine; (4) if the task of the available active virtual machine is not found, activating a new virtual machine for the task; (5) calculating the prediction reliability for the task of which the appropriate virtual machine is found, and ensuring that the prediction reliability meets the task sub-reliability requirement, otherwise, repeating each step of the task allocation stage; and (6) for the completed task, updating the sub-reliability requirement and the sub-deadline of the subsequent task, and updating the active virtual machine set. On one hand, the reliability requirement of the workflow instance is ensured, and on the other hand, the utilization efficiency of cloud computing resources is improved.

The invention discloses a cloud workflow scheduling method based on dynamic deadline and reliability constraint. The method comprises the following steps: step 1, obtaining a task set of workflow, a workflow deadline and a workflow reliability constraint; 2, calculating an upward sorting value of each task in the task set, and performing descending sorting on the tasks according to the upward sorting values to generate an initial task scheduling sequence; 3, determining a dynamic deadline of the to-be-scheduled task based on the initial task scheduling sequence; 4, determining a target reliability constraint of the to-be-scheduled task based on the initial task scheduling sequence; 5, arranging the virtual machines in a descending order to form a candidate queue; traversing the virtual machines and idle time slots thereof in the candidate queue, and distributing the tasks to be scheduled to the idle time slots; 6, judging whether an unscheduled task exists or not, and if so, returning to the step 3 to repeatedly execute; and if not, adjusting the task scheduling sequence based on a variable neighborhood descent algorithm and a simulated annealing algorithm to complete cloud workflow scheduling.

The application discloses a cloud workflow scheduling method based on an improved snake optimizer, applies the snake optimization algorithm to a discrete optimization problem, realizes scheduling of multiple workflow tasks in a cloud environment, provides a new solution path for a workflow scheduling problem in the cloud environment, and simultaneously modifies parameters of different stages of the SO algorithm on the basis of analyzing the SO algorithm and characteristics of the workflow scheduling problem, so as to take into account exploration and development, speed up convergence of the algorithm, and improve calculation efficiency.

This paper proposes a cloud workflow scheduling method based on an improved Battle Royale optimization algorithm. This method can optimize workflow execution time while satisfying user budget constraints, solving workflow application scheduling problems in cloud data center environments. The method introduces the concept of a soldier clustering index. By comparing fitness values, the clustering index of each soldier is calculated and compared with a pre-set threshold to determine whether the soldiers are clustered together, allowing timely measures to be taken to avoid the search from falling into a local optimal solution. For soldiers whose clustering index reaches the threshold, the element value of a certain dimension in their position is mutated based on probability. This mutation introduces randomness, allowing for the search to be conducted near clustered individuals for better solutions. Furthermore, after multiple iterations of accumulated mutations, some individuals can be forced to escape the local optimal solution, enhancing search diversity and effectively improving the algorithm's optimization speed and the quality of finding the optimal solution.

Cross-Domain Metadata Management Graphical User Interface for Business Intelligence, and Cloud Workflows

This invention discloses a cloud workflow scheduling method with dynamic deadlines and reliability constraints, comprising: Step 1, obtaining the task set, workflow deadlines, and workflow reliability constraints of the workflow; Step 2, calculating the upward sorting value of each task in the task set, arranging the tasks in descending order according to the upward sorting value, and generating an initial task scheduling sequence; Step 3, determining the dynamic deadlines of the tasks to be scheduled based on the initial task scheduling sequence; Step 4, determining the target reliability constraints of the tasks to be scheduled based on the initial task scheduling sequence; Step 5, arranging the virtual machines in descending order to form a candidate queue; traversing the virtual machines and their idle time slots in the candidate queue, and assigning the tasks to be scheduled to the idle time slots; Step 6, determining whether there are any unscheduled tasks. If so, returning to Step 3 and repeating the process; if not, adjusting the task scheduling sequence based on the variable neighborhood descent algorithm and the simulated annealing algorithm to complete the cloud workflow scheduling.

This invention provides a hybrid cloud workflow task scheduling method based on topology analysis and privacy quantification. It calculates task allocation time, data transmission time, backup time, encryption time, and decryption time for a second set of tasks to be allocated to the public cloud, calculates the total cost of each particle, and outputs the optimal scheduling scheme with the goal of minimizing the total cost. The second set of tasks to be allocated to the public cloud includes tasks with encryption level allocation and proactive fault-tolerant backup. Highly sensitive tasks are allocated to the private cloud through privacy cost estimation. Based on breadth-first search to determine node levels and betweenness centrality to identify critical task nodes, proactive backup is implemented to cut off cascading fault propagation links. This invention obtains a globally optimal task scheduling scheme while satisfying privacy and reliability constraints, balancing resource consumption and data security, improving the reliability of task scheduling in a hybrid cloud environment, and reducing overall scheduling costs.

The application discloses a data-driven cloud control method and system based on a container workflow structure. The method converts data-driven predictive control into a workflow form of a directed acyclic graph, fully utilizes parallel computing capability of cloud computing, can adapt to distributed requirements of cloud workflow processing, and greatly improves processing efficiency of data-driven predictive control tasks.

The invention discloses a cloud workflow engine deployment device, a workflow execution method, equipment and a medium, and belongs to the technical field of data processing. The method comprises the following steps: creating a workflow for processing a target service and a target engine function corresponding to the workflow; and when the workflow is triggered, calling the target engine function to execute the workflow. And obtaining execution result data of the workflow, and returning the execution result data to the target user associated with the workflow. Through the embodiment of the invention, each workflow can be configured with an independent target engine function, and the target engine function is started to execute the workflow only when the execution of the workflow is triggered, so that the task overhead is reduced. And task requirements of different tenants can be met.

The invention discloses an architecture design method for a cloud control platform of an unmanned system, which adopts a cloud native technology, deeply integrates related technologies such as cloud computing, storage, communication, scheduling and the like with a hardware platform and specific services, and faces four functional tasks of planning control, sensing, terminal state monitoring and cloud side resource state monitoring of the unmanned system. According to the method, the cloud computing load is used as the cloud computing load to be deployed in the Pod, specific task mirroring and atomized cloud deployment are achieved, Pod dynamic management and resource scheduling can be achieved, the method can adapt to the distributed requirements of cloud workflow processing, and the processing efficiency of terminal tasks is greatly improved.

The invention discloses a sampling data model prediction control method under a cloud workflow architecture, and the method comprises the steps: taking a given continuous system model as a control object, and building a sampling data model prediction control problem for the control object; a sample data model prediction control problem is subjected to equivalent transformation to be improved into an optimization problem suitable for ADMM algorithm solving, the optimization problem established by ADMM algorithm solving is improved, and a calculation task involved in each iteration when the ADMM algorithm iteratively solves the optimization problem is used as a workflow task to form a workflow. And then the workflow is deployed to a cloud computing distributed environment to complete the computing of each iteration, so that the continuous system model is controlled in a cloud workflow form, and the processing efficiency of the cloud control task of the continuous system model is effectively improved.

Systems, methods, and devices disclosed herein include an application deployment platform comprising a web client application, a backend control system, and a backend migration system. The web client application provides a graphical user interface (GUI) configured to receive one or more user inputs identifying a target application and a plurality of deployment parameters for the target application. The backend control system is integrated with a cloud workflow management platform API, an issue tracking platform API, a source data analytics and management platform API, and a developer platform API. Also, the backend control system performs one or more verification operations including determining whether the one or more user inputs are associated with an IT personnel or a business unit personnel. Furthermore, the backend migration system is configured to migrate the target application from a developer platform associated with the developer platform API to a destination environment.

The application relates to the technical field of cloud native software engineering, in particular to an intelligent cloud workflow generation and management platform fusing a large language model, which comprises: a working condition modeling module, which is used for automatically constructing a system specification model containing formal design specifications and expected performance targets based on inventory code, real-time traffic and telemetry data; an adversarial refinement module, which is used for realizing automatic generation, verification and correction of candidate workflow code through an adversarial iterative cycle of a generative language model and a verification engine until no new counterexample is generated; and a deployment decision module, which is used for performing quantitative risk assessment based on iteration history and executing automatic deployment decision and authorization. The application solves the problems of dependence on manual work, low efficiency and unstable quality in a traditional development process, and realizes automation, quantification and high reliability of the whole process of workflow generation and management.