172 results about "Workflow scheduling" patented technology

A graphical user interface (GUI) scheduler program is provided for modeling business workflow processes. The GUI scheduler program includes tools to allow a user to create a schedule for business workflow processes based on a set of rules defined by the GUI scheduler program. The rules facilitate deadlock not occurring within the schedule. The program provides tools for creating and defining message flows between entities. Additionally, the program provides tools that allow a user to define a binding between the schedule and components, such as COM components, script components, message queues and other workflow schedules. The scheduler program allows a user to define actions and group actions into transactions using simple GUI scheduling tools. The schedule can then be converted to executable code in a variety of forms such as XML, C, C+ and C++. The executable code can then be converted or interpreted for running the schedule.

Increased efficiency and lower cost job scheduling is provided by a system and method that optimizes for cost instead of makespan, and minimizes fluctuations in resource utilization. A schedule is constructed using a workflow scheduling system that includes a load leveler, a cost minimizer, and a manpower planner. The load leveler minimizes makespan, subject to resource limits, and then lowers the resource limits, repeating the process in order to create a flatter schedule. The cost minimizer generates an initial solution, and then incrementally improves it, using the manpower planner to evaluate the different possible solutions. The manpower planner calculates, for a given schedule, the optimal hire / fire decisions to minimize total cost.

The invention discloses a quantum-behaved particle swarm optimization (QPSO) based multi-objective dynamic workflow scheduling method, and belongs to the technical field of cloud computing. The method includes the steps: inputting a workflow and a QoS (quality of service) request; acquiring state information of virtual machines and transmission information among the virtual machines; setting a to-be-executed task set V', and setting objective functions of time, cost and reliability for a task schedule in the V'; allocating optimal resources to the to-be-executed tasks by the aid of QPSO, and judging whether total time, total cost and total reliability of task execution meet the QoS request of a user or not after the tasks are executed; dynamically updating the V', transmission speed among the virtual machines and operating speeds of the virtual machines. By means of dynamically partitioning the workflow and dynamically updating network bandwidth information, the optimal resources are allocated to the workflow tasks accurately, errors between the calculated time and actual execution time and the calculated cost and actual execution cost are reduced, time can be shortened, and cost is reduced while reliability is enhanced.

External beam radiotherapy treatment workflow scheduling and optimization. The system and method provides for determining a workflow metamodel, receiving a treatment plan, determining a treatment schedule corresponding to the workflow metamodel and treatment plan, taking into account saftety by determining alternative treatment schedules, computing a safety index, and selecting one alternative treatment schedule that satisfies a safety threshold and that provides an optimal schedule.

Methods and apparatus are disclosed for workflow scheduling. A workflow scheduling system, such as a computer system, is used to coordinate and schedule tasks in complex scheduling environments. The system accepts incoming orders and dynamically schedules them according to the constraints of the resources required for the order and any constraints of the orders themselves.

The invention provides a task cluster scheduling management method and apparatus. The method comprises a workflow scheduling step of performing scheduling on pre-defined workflow which comprises multiple groups of tasks with an executive dependence sequence, a task allocation step of allocating a group of tasks without a forward dependence relationship to at least one task execution terminal for execution, a monitoring step of monitoring an executive state of the group of tasks without the forward dependence relationship in the at least one task execution terminal, and a task scheduling step of scheduling residual tasks according to the executive dependence relationship of the tasks when it is monitored that the execution of the group of tasks without the forward dependence relationship is finished, and repeating the task allocation step, the monitoring step and the task scheduling step until the execution of all the tasks in the workflow is finished. A task execution sequence is established by setting the executive dependence relationship among the tasks, so that the technical problems of resource waste and low task execution efficiency caused by disordered task allocation can be effectively avoided.

The invention relates to a workflow task scheduling method, a multi-workflow scheduling method and a system of the multi-workflow scheduling method. The task scheduling method includes the steps that firstly, the upward weight of each task in a workflow is worked out, and the tasks are put into a list of the tasks to be scheduled according to the descending order of the upward weights; secondly, the task with the largest upward weight is selected, the time when the task can be completed fastest on each processing machine is traversed, and task is deployed to the processing machine where the task is fastest completed; thirdly, other tasks are deployed to corresponding processing machines according to the sequence of the tasks in the list of the tasks to be scheduled through the method in the second step. The main principle of the multi-workflow scheduling method includes the steps that the workflow priority received in real time is compared with the priority of a workflow being scheduled, a fairness strategy or a backfill strategy is implemented according to comparison results, and then task scheduling is carried out according to the WSS strategy. Based on the cloud computing environment, multi-priority multi-workflow scheduling can be effectively carried out.

The present invention systems and methods enable configuration of functional components in integrated circuits. A present invention system and method can flexibly change the operational characteristics of functional components in an integrated circuit die based upon a variety of factors. In one embodiment, manufacturing yields, compatibility characteristics, performance requirements, and system health (e.g., the number of components operating properly) are factored into changes to the operational characteristics of functional components. In one exemplary implementation, the changes to operational characteristics of a functional component are coordinated with changes to other functional components. Workflow scheduling and distribution is also adjusted based upon the changes to the operational characteristics of the functional components. For example, a functional component configuration controller changes the operational characteristics settings and provides an indication to a workflow distribution component. The workflow distribution component changes the workflow schedule based upon the operational characteristics settings (e.g., work flow is diverted to or away from functional components).

The invention discloses a method for constraining workflow scheduling in a cloud environment based on the ant colony optimization algorithm through deadline. The method comprises the steps: employing an ant colony system with a candidate list; respectively searching a workflow scheduling scheme in the cloud environment through a plurality of ants; carrying out the communication of a workflow scheduling result among the ants in a pheromone mode, thereby guiding the deciding of the subsequent search direction of ants and the workflow scheduling scheme. Compared with a conventional workflow scheduling method, the method can reduce the cost of the workflow scheduling in the cloud environment and improves the service quality for cloud users under the condition of meeting the deadline time QoS requirements of users.

The invention discloses a workflow unloading optimization algorithm in a mobile edge environment, comprising the following steps: step S1, constructing a workflow scheduling model under the mobile edge environment; step S2, accord to that topology structure, searching a critical path base on the local calculation amount of the workflow, respectively storing nodes and remaining node on the criticalpath into a key node queue and a remaining node queue and waiting for scheduling; step S3, after that eNB node is abstracted into a topology structure, accord to a user history moving path and combining with a prediction model, obtaining a qualified eNB set, and accord to the prediction probability, selecting an optimal unloadable eNB according to the prediction speed and the prediction direction, and providing the eNB to a user for performing task unloading; step S4, dynamically dividing that tasks accord to the scheduling strategy into local execution or unloading to the edge side for execution, and dynamically updating the critical path during the scheduling process; step S5, according to the scheduling result, assigning tasks to the devices under the mobile edge environment until thescheduling of the entire workflow is completed.

The invention mainly relates to a cloud workflow scheduling method supporting any flow structure, belongs to the field of information technologies and computers, and particularly relates to a cloud workflow scheduling algorithm which has universal applicability for various workflow models in a cloud computing environment and considers service quality indexes. According to the method, a task is modeled through a directed graph by taking a minimized cloud workflow execution expense as a scheduling target under a deadline constraint, so that simple and visual modeling advantages of a DAG graph are reserved and the method is suitable for workflow scheduling problems including logic structure selection and circulation; and static matching and dynamic adjustment of resources are subjected to task scheduling, so that the resource selection is optimized and very high universality is achieved.

The invention discloses a visual ETL flow management and scheduling monitoring method comprising the following steps: a workflow is created graphically through flow chart drawing using an interface drag assembly; a workflow chart is drawn according to a data processing unit needing to be dragged, flow dependency and related branch, and a combined flow assembly; for storage of the flow in a drawing board, all objects in the drawing board, the attributes of the objects, the X-axis and Y-axis positions of the objects and the connecting lines between the objects are stored in the form of JSON objects and sent to the background, and the background converts the JSON objects into a JBPM flow and converts flow attributes into JBPM attributes; and ETL workflow scheduling monitoring is implemented in an automatic on-time scheduling mode. An interactive, graphical and draggable design is adopted to make an ETL workflow easy to create and maintain. Based on the characteristic of 'what you see is what you get', cumbersome manual intervention is reduced, the use threshold of an ETL system is lowered, the labor cost of enterprises is reduced, and the work efficiency is improved.

The invention discloses a multi-workflow scheduling method based on a genetic algorithm under a cloud environment. The method comprises the following steps that a previous workflow scheduling state isreserved, the genetic algorithm and a new workflow are initialized, the adaptability degree of each individual of the new workflow is calculated, and two parent individuals are selected; according tothe genetic algorithm, the parent individuals are subjected to cross operation and single-point variation, progeny individuals are obtained, the adaptability degrees of the progeny individuals are calculated, the adaptability degrees of the progeny individuals and the corresponding parent individuals are compared, and two smaller progeny individuals are selected and added to the progeny population; if the size of the progeny population is equal to that of the parent population, the progeny population and the parent population are merged, the individuals which accord with the genetic algorithmare selected from the merged population to form the new population, and otherwise, the step of selecting the parent individuals again is skipped to; finally, according to the iteration frequency, optimal scheduling is output. According to the multi-workflow scheduling method based on the genetic algorithm under the cloud environment, the situation is avoided that previous workflow scheduling is damaged so that additional communication cost can be generated, and the utilization rate of computing resources of a virtual machine is further increased.

The invention relates to the technical field of cloud computing, in particular to a workflow scheduling and data distribution method under a cloud environment. The method comprises the following stepsthat current workflow of a cloud platform is obtained; the height of each task in the workflow is calculated according to a scheduling model, and the tasks are arranged in an ascending order according to the heights; a particle swarm optimization algorithm is used, the execution time and execution cost of the workflow serve as an optimization objective, and a globally optimal particle code is obtained; the globally optimal particle code is decoded into a workflow scheduling and data distribution scheme and output. Accordingly, data storage is considered, the time of across-node transmission is shortened, and the task execution efficiency and execution cost of the workflow in the cloud platform are optimized.

The invention discloses a multitask scheduling method. The method comprises the steps that first connection between a data platform and at least one data source is established, and second connection between the data platform and an application server is established; a source table which is selected by a user and needs to be synchronized and a data source type are received, a table establishing task and a data synchronizing task corresponding to all the data sources are generated, and the table establishing task and the data synchronizing task are published to a preset workflow scheduling engine; when synchronization starting time selected by the user is up, a preset task scheduling interface template is called through the preset workflow scheduling engine, and synchronization parameters are transmitted to the task scheduling interface template; and according to the synchronization parameters, corresponding task execution scripts in the task scheduling interface template are called to execute the table establishing task and the data synchronizing task corresponding to all the data sources. Through the method, API calling can be parameterized, and the data synchronizing task can be completed just by transmitting the synchronization parameters during data synchronization.

The invention discloses a multi-target cloud workflow scheduling method based on a reinforcement learning strategy. A reinforcement learning Agent is improved by using a pointer network to form an improved deep reinforcement learning algorithm so as to construct a workflow scheduling model based on a reinforcement learning strategy, so that the workflow scheduling model can be suitable for cloud workflow scheduling problems of different sizes and different types, and the generalization ability of the model is improved while relatively high timeliness is ensured.

The invention discloses an energy consumption-oriented cloud workflow scheduling optimization method. The method comprises the following steps: (1) establishing energy consumption-oriented cloud workflow process model and resource model; (2) calculating a task priority; (3) taking out a task t with highest priority from a task set T, finding out a virtual machine set VMt capable of executing the task t, and calculating energy consumption for distributing the task t to each virtual machine in the VMt and completing all distributed tasks; (4) finding out a vm with minimal energy consumption, if only one vm has the minimal energy consumption, distributing the task t to the vm, and if a plurality of vms have the minimal energy consumption, distributing the task t to the vm with characteristics that the vm has the minimal energy consumption and a host in which the vm is located has highest performance per watt; deleting the task t from the task set T, and if the task set T is not null, going to the step (3), or otherwise, going to the step (5); and (5) outputting a workflow scheduling scheme. According to the scheduling optimization method provided by the invention, an energy consumption factor is considered, so that the energy consumption for task processing by the host is effectively reduced while workflow execution time efficiency is kept.

The invention discloses a scheduling method for a cloud service workflow and belongs to the technical field of cloud computation. The scheduling method disclosed by the invention comprises the following steps of step A, sequentially allocating feasible services for all tasks in the workflow according to a rule that cost means of the tasks are minimized to obtain an initial scheduling scheme of the workflow, wherein the cost mean of any one task is determined according to the following method that one task selects one feasible service; immediate successor tasks of the task respectively select feasible services with minimum costs, and a mean value of costs of the services selected by the task and the immediate successor tasks of the task is a cost mean of the task; step B, by taking meeting of the default trust requirement and the workflow deadline requirement as constraint conditions and a least rental fee as an objective, adjusting an initial scheduling scheme to obtain a final workflow scheduling scheme. According to the scheduling method disclosed by the invention, the least service rental fee is realized while the service trust, an available service range and the constraint of the workflow deadline are met.

The invention provides a workflow scheduling method and device. The workflow scheduling method comprises the steps of: respectively creating a task queue for each processor, wherein each task queue comprises one or more subqueues with different priorities; inserting a task activity in a workflow example as a task item into the subqueues; extracting a task item from the task queue corresponding to the task item by the processor, wherein the extraction subsequence is realized by extracting the subqueue with high priority; and in the extracted subqueues, extracting the task items at the heads of the subqueue. According to the invention, concurrency efficiency of the workflow system is increased.

The invention relates to a strategy of scientific workflow scheduling under a hybrid cloud. The strategy is based on the idea of task probability clustering and multi-constraint workflow division, effectively reduces system overhead, shortens execution time, reduces execution cost of a public cloud, and improves execution efficiency of a research organization. The strategy comprises the steps of: firstly, utilizing the task probability clustering idea, carrying out task probability clustering on a scientific workflow, increasing task scheduling granularity and reducing system overhead; secondly, utilizing the multi-constraint workflow division idea, adopting multi-constraint workflow division when a private cloud cannot meet time demand required by the research institution, balancing resource demand of each child workflow, and reducing communication traffic among the child workflows; thirdly, and allocating appropriate cloud resource for each child workflow according to a deadline, increasing resource utilization rate of the private cloud, and reducing cost of the public cloud.

The invention provides a self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling. The overall detection and local mining capability of the multi-object evolution method can be improved. The multi-object evolution method comprises the steps that S1, the evolution states of populations in the evolution process are detected according to the number of Pareto solutions and Pareto entropies, and corresponding individual evaluation strategy processing constraint conditions are self-adaptively utilized to sort individuals in the populations according to the detected evolution states of the populations in the evolution process, wherein a constraint violation processing method is adopted to process the constraint conditions in individual evaluation strategies; S2, according to individual sorting results, individuals are selected from the populations to perform genetic manipulation, and sub-populations are obtained, wherein genetic manipulation parameters are self-adaptively adjusted according to the evolution states of the populations in the evolution process during genetic manipulation. The self-adaptive multi-object evolution method is suitable for solving the multi-object evolution problem having constraints and can be applied to the technical field of workflow scheduling in a cloud computing environment.

A system and method for resource workflow scheduling providing a resource workflow scheduling interface where a resource is defined along a first axis and time is defined along a second axis. Tasks are represented as movable objects which are scheduled and may be rescheduled according to constraints stored in a database. Tasks that must be scheduled over multiple days are represented as visually and structurally connected objects. Constraints stored in the database comprise rules and restrictions defining the sequence of tasks required to complete a job, the association of tasks to resources, the time a particular resource requires to complete the task, and the date that task must be completed.

The invention provides a method and a device for distributed workflow dispatching. The method comprises the following steps: acquiring an operation execution instruction supplied by a user, a workflow topological relation and an inter-operation dependency; searching for a node corresponding to the operation execution instruction on the basis of the workflow topological relation and the inter-operation dependency; acquiring a prepared node; confirming a resource quota required by the prepared node by a resource management module; adopting a colony resource scheduling system for scheduling the resource corresponding to a colony resource quota to the resource management module; sending a starting instruction by the resource management module which has acquired the resource through a resource scheduling agent, thereby starting a remote operation executing module for executing the operation execution instruction. According to the invention, the topology for traversing Flow on the basis of the complex inter-operation dependency is adopted for realizing the distributed running of the workflow, the scheduling logic and the executing logic in the workflow are separated and the workflow scheduling and the resource management logic are separated.

The invention discloses a workflow scheduling method for multi-target particle swarm optimization based on real-time state monitoring and relates to the field of cloud computing workflow scheduling. According to the method, first, workflow pre-scheduling is performed through a BHEFT algorithm, and the feasibility of the algorithm is improved; and a Pareto variance is introduced to perform real-time monitoring on an evolution state, wherein when the evolution state is in diversification, an external elite swarm self-optimization strategy is adopted to improve local search capability of the algorithm, and when the evolution state is in stagnation, an escape strategy is adopted, so that workflow scheduling solution spaces are diversified. In this way, development and exploitation of the algorithm in the evolution process are effectively balanced, and the convergence of workflow scheduling solutions and the diversity of scheduling solution space distribution are realized.

The invention relates to big data calculation and discloses a cloud platform MapReduce workflow scheduling optimizing method. The platform MapReduce workflow scheduling optimizing method approximately comprises the steps of conducting reconstruction, wherein existing workflow is reconstructed, so that new workflow is obtained; conducting optimization, wherein the workflow is optimized according to the genetic algorithm; obtaining historical data, wherein the historical data are reserved by recoding the historical data or by recording relevant data of a regression model after the regression model is established. In this way, different individuals can be generated through a part of the historical data during optimization. The platform MapReduce workflow scheduling optimizing method has the advantages that the operating time of the workflow is considered, cost for cluster renting required when calculation is conducted on a cloud platform is also considered, the optimization effect is good, and the problem that the efficiency is not high when workflow scheduling is conducted on a large cloud calculation platform can be solved fundamentally.

Increased efficiency and lower cost job scheduling is provided by a system and method that optimizes for cost instead of makespan, and minimizes fluctuations in resource utilization. A schedule is constructed using a workflow scheduling system that includes a load leveler, a cost minimizer, and a manpower planner. The load leveler minimizes makespan, subject to resource limits, and then lowers the resource limits, repeating the process in order to create a flatter schedule. The cost minimizer generates an initial solution, and then incrementally improves it, using the manpower planner to evaluate the different possible solutions. The manpower planner calculates, for a given schedule, the optimal hire / fire decisions to minimize total cost.

Methods and apparatus are disclosed for workflow scheduling. A workflow scheduling system, such as a computer system, is used to coordinate and schedule tasks in complex scheduling environments. The system accepts incoming orders and dynamically schedules them according to the constraints of the resources required for the order and any constraints of the orders themselves.

A workflow schedule authoring tool enables a user to author a workflow by arranging building blocks in a particular order. Building blocks may correspond to events, conditions or actions. Each building block is associated with source code that defines an action to be taken when the building block is processed. The order of the building blocks determines the workflow schedule process. The building blocks allow a user to establish logic conditions for how the tasks in the workflow schedule are executed. A building block may require a user to input parameters before the building block may be executed. The workflow schedule is associated with an event such that the workflow schedule triggers when the event occurs. The building blocks simplify workflow schedule authoring because the user does not need to write any code.

The invention discloses a multi-data-source data synchronizing method. The method comprises the following steps: establishing a first connection between a data platform and at least one data source, and establishing a second connection between the data platform and an application server; receiving a source table which is selected by a user and needs to be synchronized and data source types, and generating table establishing tasks and data synchronizing tasks corresponding to data sources according to pre-resolved source table structures of the data sources; and publishing the generated table establishing tasks and data synchronizing tasks corresponding to the data sources to a preset workflow scheduling engine, and executing the table establishing tasks and the data synchronizing tasks corresponding to the data sources through the preset workflow scheduling engine. Through adoption of the multi-data-source data synchronizing method, full-automatic operation of a whole data synchronizing flow can be realized, and a development flow, a testing flow and a deployment flow are omitted for developers.