Examples with Resource edu.iu.dsc.tws.api.compute.schedule.elements.Resource used on opensource projects

Example 11 with Resource

use of edu.iu.dsc.tws.api.compute.schedule.elements.Resource in project twister2 by DSC-SPIDAL.

the class DataLocalityBatchTaskScheduler method schedule.

/**
 * This is the base method for the data locality aware task scheduling for scheduling the batch
 * task instances. It retrieves the task vertex set of the task graph and send the set to the
 * data locality aware scheduling algorithm to allocate the batch task instances which are closer
 * to the data nodes.
 */
@Override
public TaskSchedulePlan schedule(ComputeGraph graph, WorkerPlan workerPlan) {
    LinkedHashMap<Integer, WorkerSchedulePlan> containerPlans = new LinkedHashMap<>();
    for (int i = 0; i < workerPlan.getNumberOfWorkers(); i++) {
        dataLocalityAwareAllocation.put(i, new ArrayList<>());
    }
    LinkedHashSet<Vertex> taskVertexSet = new LinkedHashSet<>(graph.getTaskVertexSet());
    TaskVertexParser taskVertexParser = new TaskVertexParser();
    List<Set<Vertex>> taskVertexList = taskVertexParser.parseVertexSet(graph);
    for (Set<Vertex> vertexSet : taskVertexList) {
        Map<Integer, List<TaskInstanceId>> containerInstanceMap;
        if (vertexSet.size() > 1) {
            containerInstanceMap = dataLocalityBatchSchedulingAlgorithm(graph, vertexSet, workerPlan);
        } else {
            Vertex vertex = vertexSet.iterator().next();
            containerInstanceMap = dataLocalityBatchSchedulingAlgorithm(graph, vertex, workerPlan);
        }
        TaskInstanceMapCalculation instanceMapCalculation = new TaskInstanceMapCalculation(this.instanceRAM, this.instanceCPU, this.instanceDisk);
        Map<Integer, Map<TaskInstanceId, Double>> instancesRamMap = instanceMapCalculation.getInstancesRamMapInContainer(containerInstanceMap, taskVertexSet);
        Map<Integer, Map<TaskInstanceId, Double>> instancesDiskMap = instanceMapCalculation.getInstancesDiskMapInContainer(containerInstanceMap, taskVertexSet);
        Map<Integer, Map<TaskInstanceId, Double>> instancesCPUMap = instanceMapCalculation.getInstancesCPUMapInContainer(containerInstanceMap, taskVertexSet);
        for (int containerId : containerInstanceMap.keySet()) {
            double containerRAMValue = TaskSchedulerContext.containerRamPadding(config);
            double containerDiskValue = TaskSchedulerContext.containerDiskPadding(config);
            double containerCpuValue = TaskSchedulerContext.containerCpuPadding(config);
            List<TaskInstanceId> taskTaskInstanceIds = containerInstanceMap.get(containerId);
            Map<TaskInstanceId, TaskInstancePlan> taskInstancePlanMap = new HashMap<>();
            for (TaskInstanceId id : taskTaskInstanceIds) {
                double instanceRAMValue = instancesRamMap.get(containerId).get(id);
                double instanceDiskValue = instancesDiskMap.get(containerId).get(id);
                double instanceCPUValue = instancesCPUMap.get(containerId).get(id);
                Resource instanceResource = new Resource(instanceRAMValue, instanceDiskValue, instanceCPUValue);
                taskInstancePlanMap.put(id, new TaskInstancePlan(id.getTaskName(), id.getTaskId(), id.getTaskIndex(), instanceResource));
                containerRAMValue += instanceRAMValue;
                containerDiskValue += instanceDiskValue;
                containerCpuValue += instanceDiskValue;
            }
            Worker worker = workerPlan.getWorker(containerId);
            Resource containerResource;
            if (worker != null && worker.getCpu() > 0 && worker.getDisk() > 0 && worker.getRam() > 0) {
                containerResource = new Resource((double) worker.getRam(), (double) worker.getDisk(), (double) worker.getCpu());
            } else {
                containerResource = new Resource(containerRAMValue, containerDiskValue, containerCpuValue);
            }
            WorkerSchedulePlan taskWorkerSchedulePlan;
            if (containerPlans.containsKey(containerId)) {
                taskWorkerSchedulePlan = containerPlans.get(containerId);
                taskWorkerSchedulePlan.getTaskInstances().addAll(taskInstancePlanMap.values());
            } else {
                taskWorkerSchedulePlan = new WorkerSchedulePlan(containerId, new HashSet<>(taskInstancePlanMap.values()), containerResource);
                containerPlans.put(containerId, taskWorkerSchedulePlan);
            }
        }
    }
    TaskSchedulePlan taskSchedulePlan = new TaskSchedulePlan(0, new HashSet<>(containerPlans.values()));
    Map<Integer, WorkerSchedulePlan> containersMap = taskSchedulePlan.getContainersMap();
    for (Map.Entry<Integer, WorkerSchedulePlan> entry : containersMap.entrySet()) {
        Integer integer = entry.getKey();
        WorkerSchedulePlan workerSchedulePlan = entry.getValue();
        Set<TaskInstancePlan> containerPlanTaskInstances = workerSchedulePlan.getTaskInstances();
        LOG.fine("Task Details for Container Id:" + integer + "\tsize:" + containerPlanTaskInstances.size());
        for (TaskInstancePlan ip : containerPlanTaskInstances) {
            LOG.fine("TaskId:" + ip.getTaskId() + "\tTask Index" + ip.getTaskIndex() + "\tTask Name:" + ip.getTaskName());
        }
    }
    return taskSchedulePlan;
}

Example 12 with Resource

use of edu.iu.dsc.tws.api.compute.schedule.elements.Resource in project twister2 by DSC-SPIDAL.

the class Container method assertHasSpace.

private void assertHasSpace(Resource resourceValue) throws TaskSchedulerException {
    Resource usedResources = this.getTotalUsedResources();
    double newRam = usedResources.getRam() + resourceValue.getRam() + paddingPercentage;
    double newDisk = usedResources.getDisk() + resourceValue.getDisk() + paddingPercentage;
    double newCpu = usedResources.getCpu() + resourceValue.getCpu() + paddingPercentage;
    if (newRam > this.resource.getRam()) {
        throw new TaskSchedulerException(String.format("Adding %s bytes of ram to existing %s " + "bytes with %d percent padding would exceed capacity %s", resourceValue.getRam(), usedResources.getRam(), paddingPercentage, this.resource.getRam()));
    }
    if (newDisk > this.resource.getDisk()) {
        throw new TaskSchedulerException(String.format("Adding %s bytes of disk to existing %s " + "bytes with %s percent padding would exceed capacity %s", resourceValue.getDisk(), usedResources.getDisk(), paddingPercentage, this.resource.getDisk()));
    }
    if (newCpu > this.resource.getCpu()) {
        throw new TaskSchedulerException(String.format("Adding %s cores to existing %s " + "cores with %d percent padding would exceed capacity %s", resourceValue.getCpu(), usedResources.getCpu(), paddingPercentage, this.resource.getCpu()));
    }
}

Example 13 with Resource

use of edu.iu.dsc.tws.api.compute.schedule.elements.Resource in project twister2 by DSC-SPIDAL.

the class FirstFitStreamingTaskScheduler method initialize.

/**
 * This method initialize the config values received from the user and assign the default
 * task instance value and container instance values.
 */
@Override
public void initialize(Config cfg) {
    this.config = cfg;
    double instanceRAM = TaskSchedulerContext.taskInstanceRam(this.config);
    double instanceDisk = TaskSchedulerContext.taskInstanceDisk(this.config);
    double instanceCPU = TaskSchedulerContext.taskInstanceCpu(this.config);
    this.paddingPercentage = TaskSchedulerContext.containerPaddingPercentage(this.config);
    this.defaultResourceValue = new Resource(instanceRAM, instanceDisk, instanceCPU);
    int defaultNoOfTaskInstances = TaskSchedulerContext.defaultTaskInstancesPerContainer(this.config);
    instanceRAM = this.defaultResourceValue.getRam() * defaultNoOfTaskInstances;
    instanceDisk = this.defaultResourceValue.getDisk() * defaultNoOfTaskInstances;
    instanceCPU = this.defaultResourceValue.getCpu() * defaultNoOfTaskInstances;
    this.maxContainerResourceValue = new Resource((double) Math.round(TaskScheduleUtils.increaseBy(instanceRAM, paddingPercentage)), (double) Math.round(TaskScheduleUtils.increaseBy(instanceDisk, paddingPercentage)), (double) Math.round(TaskScheduleUtils.increaseBy(instanceCPU, paddingPercentage)));
    LOG.fine("Instance default values:" + "RamValue:" + instanceRAM + "\t" + "DiskValue:" + instanceDisk + "\t" + "CPUValue:" + instanceCPU);
    LOG.fine("Container default values:" + "RamValue:" + this.maxContainerResourceValue.getRam() + "\t" + "DiskValue:" + this.maxContainerResourceValue.getDisk() + "\t" + "CPUValue:" + this.maxContainerResourceValue.getCpu());
}

Example 14 with Resource

use of edu.iu.dsc.tws.api.compute.schedule.elements.Resource in project twister2 by DSC-SPIDAL.

the class FirstFitStreamingTaskScheduler method getSortedRAMInstances.

/**
 * This method sort the task instances in an increasing order based on the required ram
 * configuration values.
 */
private ArrayList<RequiredRam> getSortedRAMInstances(Set<String> taskNameSet) {
    ArrayList<RequiredRam> ramRequirements = new ArrayList<>();
    TreeSet<Vertex> orderedTaskSet = new TreeSet<>(new VertexComparator());
    orderedTaskSet.addAll(this.taskVertexSet);
    Map<String, Double> taskRamMap = taskAttributes.getTaskRamMap(this.taskVertexSet);
    for (String taskName : taskNameSet) {
        Resource resource = TaskScheduleUtils.getResourceRequirement(taskName, taskRamMap, this.defaultResourceValue, this.maxContainerResourceValue, this.paddingPercentage);
        ramRequirements.add(new RequiredRam(taskName, resource.getRam()));
    }
    ramRequirements.sort(Collections.reverseOrder());
    return ramRequirements;
}

Example 15 with Resource

use of edu.iu.dsc.tws.api.compute.schedule.elements.Resource in project twister2 by DSC-SPIDAL.

the class RoundRobinTaskScheduler method schedule.

/**
 * This is the base method which receives the dataflow taskgraph and the worker plan to allocate
 * the task instances to the appropriate workers with their required ram, disk, and cpu values.
 *
 * @return TaskSchedulePlan
 */
@Override
public TaskSchedulePlan schedule(ComputeGraph computeGraph, WorkerPlan workerPlan) {
    // Allocate the task instances into the containers/workers
    Set<WorkerSchedulePlan> workerSchedulePlans = new LinkedHashSet<>();
    // To get the vertex set from the taskgraph
    Set<Vertex> taskVertexSet = new LinkedHashSet<>(computeGraph.getTaskVertexSet());
    // Allocate the task instances into the logical containers.
    Map<Integer, List<TaskInstanceId>> roundRobinContainerInstanceMap = roundRobinSchedulingAlgorithm(computeGraph, workerPlan.getNumberOfWorkers());
    TaskInstanceMapCalculation instanceMapCalculation = new TaskInstanceMapCalculation(this.instanceRAM, this.instanceCPU, this.instanceDisk);
    Map<Integer, Map<TaskInstanceId, Double>> instancesRamMap = instanceMapCalculation.getInstancesRamMapInContainer(roundRobinContainerInstanceMap, taskVertexSet);
    Map<Integer, Map<TaskInstanceId, Double>> instancesDiskMap = instanceMapCalculation.getInstancesDiskMapInContainer(roundRobinContainerInstanceMap, taskVertexSet);
    Map<Integer, Map<TaskInstanceId, Double>> instancesCPUMap = instanceMapCalculation.getInstancesCPUMapInContainer(roundRobinContainerInstanceMap, taskVertexSet);
    for (int containerId : roundRobinContainerInstanceMap.keySet()) {
        double containerRAMValue = TaskSchedulerContext.containerRamPadding(config);
        double containerDiskValue = TaskSchedulerContext.containerDiskPadding(config);
        double containerCpuValue = TaskSchedulerContext.containerCpuPadding(config);
        List<TaskInstanceId> taskTaskInstanceIds = roundRobinContainerInstanceMap.get(containerId);
        Map<TaskInstanceId, TaskInstancePlan> taskInstancePlanMap = new HashMap<>();
        for (TaskInstanceId id : taskTaskInstanceIds) {
            double instanceRAMValue = instancesRamMap.get(containerId).get(id);
            double instanceDiskValue = instancesDiskMap.get(containerId).get(id);
            double instanceCPUValue = instancesCPUMap.get(containerId).get(id);
            Resource instanceResource = new Resource(instanceRAMValue, instanceDiskValue, instanceCPUValue);
            taskInstancePlanMap.put(id, new TaskInstancePlan(id.getTaskName(), id.getTaskId(), id.getTaskIndex(), instanceResource));
            containerRAMValue += instanceRAMValue;
            containerDiskValue += instanceDiskValue;
            containerCpuValue += instanceDiskValue;
        }
        Worker worker = workerPlan.getWorker(containerId);
        Resource containerResource;
        // Create the container resource value based on the worker plan
        if (worker != null && worker.getCpu() > 0 && worker.getDisk() > 0 && worker.getRam() > 0) {
            containerResource = new Resource((double) worker.getRam(), (double) worker.getDisk(), (double) worker.getCpu());
        } else {
            containerResource = new Resource(containerRAMValue, containerDiskValue, containerCpuValue);
        }
        // Schedule the task instance plan into the task container plan.
        WorkerSchedulePlan taskWorkerSchedulePlan = new WorkerSchedulePlan(containerId, new LinkedHashSet<>(taskInstancePlanMap.values()), containerResource);
        workerSchedulePlans.add(taskWorkerSchedulePlan);
    }
    return new TaskSchedulePlan(0, workerSchedulePlans);
}