10 results about "Cloud workflow" patented technology

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 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 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.

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.