Ready Tasks Research Articles

The hospital costs of high emergency department pediatric readiness.

We estimate annual hospital expenditures to achieve high emergency department (ED) pediatric readiness (HPR), that is, weighted Pediatric Readiness Score (wPRS)≥88 (0-100 scale) across EDs with different pediatric volumes of children, overall and after accounting for current levels of readiness. We calculated the annual hospital costs of HPR based on two components: (1) ED pediatric equipment and supplies and (2) labor costs required for a Pediatric Emergency Care Coordinator (PECC) to perform pediatric readiness tasks. Data sources to generate labor cost estimates included: 2021 national salary information from U.S. Bureau of Labor Statistics, detailed patient and readiness data from 983 EDs in 11 states, the 2021 National Pediatric Readiness Project assessment; a national PECC survey; and a regional PECC survey. Data sources for equipment and supply costs included: purchasing costs from seven healthcare organizations and equipment usage per ED pediatric volume. We excluded costs of day-to-day ED operations (ie, direct clinical care and routine ED supplies). The total annual hospital costs for HPR ranged from $77,712 (95% CI 54,719-100,694) for low volume EDs to $279,134 (95% CI 196,487-362,179) for very high volume EDs; equipment costs accounted for 0.9-5.0% of expenses. The total annual cost-per-patient ranged from $3/child (95% CI 2-4/child) to $222/child (95% CI 156-288/child). After accounting for current readiness levels, the cost to reach HPR ranged from $23,775 among low volume EDs to $145,521 among high volume EDs, with costs per patient of $4/child to $48/child. Annual hospital costs for HPR are modest, particularly when considered per child.

Comparison of the Execution Step Strategies on Scheduling Data-intensive Workflows on IaaS Cloud Platforms

The IaaS platforms of the Cloud hold promise for executing parallel applications, particularly data-intensive scientific workflows. An important challenge for users of these platforms executing scientific workflows is to strike the right trade-off between the execution time of the scientific workflow and the cost of using the platform. In a previous article, we proposed an efficient approach that assists the user in finding this compromise. This approach requires an algorithm aimed at minimizing the execution time of the workflow once the platform configuration is set. In this article, we compare two different strategies for executing a workflow after its offline scheduling using an algorithm. The algorithm that we proposed in the previous study has outperform the HEFT algorithm.
 The first strategy allows some ready tasks to execute earlier than other higher-priority tasks that are ready later due to data transfer times. This strategy is justified by the fact that although our scheduling algorithm attempts to minimize data transfers between tasks running on different virtual machines, this algorithm does not include data transfer times in the planned execution dates for the various tasks of the workflow. The second strategy strictly adheres to the predetermined order among tasks scheduled on the same virtual machine.
 The results of our evaluations show that the best execution strategy depends on the characteristics of the workflow. For each evaluated workflow, our results demonstrate that our scheduling algorithm combined with the best execution strategy surpasses HEFT. The choice of the best strategy must be determined experimentally following realistic simulations, such as the ones we conduct here using the WRENCH framework, before conducting simulations to find the best compromise between cost and execution time of a workflow on an IaaS platform..

A multi-class workflow ensemble management system using on-demand and spot instances in cloud

Nowadays, cloud computing is an attractive and competitive market, and many computational jobs have migrated to cloud resources. Scheduling a workflow is a common issue in cloud computing. In some applications, a group of interrelated workflows with the same structure but different sizes and inputs is considered as a single job called an ensemble. Executing such applications in the cloud is expensive, and designing a cost-efficient approach is still challenging. The current paper proposes a new algorithm for scheduling workflow ensembles that are subject aware to of budget and deadline constraints. To achieve this aim, the present study takes advantage of Amazon spot instances. These instances are offered by bid at a lower price but with lower reliability. In the proposed algorithm, workflow tasks are classified according to deadlines and priorities, and the requested spot instances are classified based on bid prices. More reliable instances with higher bid prices are assigned to more important classes of tasks. Furthermore, an efficient workflow acceptance and instance provisioning procedure are proposed to launch and terminate instances based on the number of ready tasks and their deadlines. The present research’s simulation results show that the proposed system increases the number of completed workflows and reduces the number of partially executed ones.

AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing

Emerging edge computing (EC) systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. To this end, existing EC research has proposed several multiple drone-based edge deployments for various purposes, such as data caching, task offloading, real-time video analytics, and computer vision. However, none of them consider the ability of seamlessly integrating edge resources running across multiple drones in a single pool, to holistically manage and control these resources as well as to eliminate vendor lock-in situations. This paper presents an intelligent resource scheduling solution for a federated aerial EC system, called AirEdge, which jointly considers task dependencies, heterogeneous resource demand and drones’ flight time. We propose a multi-task execution time estimation and a dispatching policy, to select the closest drone deployment having congruent flight time and resource availability to execute ready tasks at any given time. For the utilization of the drones’ attached edge resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent tasks that co-locates tasks tightly on nodes to fully utilize available resources. Experiments on real-world data-trace from Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) and resource demands show the effectiveness, fast executions, and resource efficiency of our approach.

Real-Time Multiple-Workflow Scheduling in Cloud Environments

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors that strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

Ready-time partitioning algorithm for computation offloading of workflow applications in mobile cloud computing

To augment the capabilities of mobile devices, application partitioning solutions in mobile cloud computing have emerged to decide the execution location of each application part between mobile device and cloud servers. To adapt to the network and server status changes during application execution, we propose a cloudlet-assisted ready-time partitioning technique, which partitions each task of the users’ workflow exactly when it is ready to run. It aims at minimizing the energy consumption of each mobile user’s device, while meeting user-defined deadlines. The proposed deadline distribution–ready-time partitioning algorithm has two phases: deadline distribution phase, which distributes each user deadline among its workflow tasks before application execution, and ready-time partitioning phase, which partitions each ready task by effectively considering the network and cloudlet status. Using real mobile applications, the experimental results prove its adaptability to environmental changes, its energy-efficiency, and its ability to satisfy the deadline over other methods.

Dynamic DAG Scheduling Under Memory Constraints for Shared-Memory Platforms

This work focuses on dynamic DAG scheduling under memory constraints. We target a shared-memory platform equipped with $p$ parallel processors. The goal is to bound the maximum amount of memory that may be needed by any schedule using p processors to execute the DAG. We refine the classical model that computes maximum cuts by introducing two types of memory edges in the DAG, black edges for regular precedence constraints and red edges for actual memory consumption during execution. A valid edge cut cannot include more than $p$ red edges. This limitation had never been taken into account in previous works, and dramatically changes the complexity of the problem, which was polynomial and becomes NP-hard. We introduce an Integer Linear Program (ILP) to solve it, together with an efficient heuristic based on rounding the rational solution of the ILP. In addition, we propose an exact polynomial algorithm for series-parallel graphs. We further study the extension of the approach where the scheduler is dynamically constrained to select tasks (among ready tasks) so that the total memory used does not exceed some threshold. We provide an extensive set of experiments, both with randomly-generated graphs and with graphs arising from practical applications, which demonstrate the impact of resource constraints on peak memory usage.

Digital picture watermarking technique for security applications

Digital picture watermarking is employed to stay secret the proprietary info as a watermark within a digital image, to spot the possession. This method has relevancy to any or all pictures. Separate trigonometric function transform (DCT) is employed before inserting the watermark within the host image. The host picture is isolated into 8×8 non-covering hinders preceding DCT application, and therefore the watermark bit is inserted by dynamic distinction between DCT coefficients of contiguous squares. Arnold transform is employed even so disorderly encoding to feature 2-fold block safeguard to the watermark. 3 distinctive variations of the planned calculation are tried and stone-broke down. The reenactment results demonstrate that the planned set up is powerful to the overwhelming majority of the image making ready tasks like JPEG pressure, honing, trimming, middle separating, and so on. To approve the proficiency of the planned technique, the recreation results are contrasted and sure condition of-workmanship systems. The examination results represent that the planned set up performs higher as so much as power, security and imperceptivity. Given the advantages of the planned set up, it alright could also be used in applications like e-social insurance and telemedicine to smartly hide out electronic eudemonia records in therapeutic footage.

Novel efficient on-chip task scheduler for multi-core hard real-time systems

This article presents an efficient hardware architecture of EDF-based task scheduler, which is suitable for hard real-time systems due to the constant response time of the scheduler. The proposed scheduler contains a queue of ready tasks that is based on a new MIN/MAX queue architecture called Heap Queue, which is inspired by Shift Registers, Systolic Arrays, heapsort algorithm, the Rocket Queue architecture and dual-port RAMs. The instructions of the proposed scheduler have throughput of one instruction per two clock cycles regardless of the actual number of tasks managed by the scheduler, and regardless of the scheduler capacity. The developed task scheduler is optimized for low chip area costs, which leads to lower energy consumption. The Heap Queue-based architecture has constant time complexity due to two clock-cycle response time of the instructions and therefore, the architecture is highly deterministic. The scheduler supports CPUs that can execute 1, 2 or 4 tasks simultaneously, and contains an implementation of clever and efficient logic that can handle conflicts caused by the fact that the scheduler is used by all CPU cores at the same time. The proposed scheduler was verified through SystemVerilog UVM-like simulations that applied billions of randomly generated test instructions. Achieved ASIC (28 nm) and FPGA synthesis results are presented and compared. More than 86% of the chip area and 93% of the total power consumption can be saved if Heap Queue architecture is used in hardware implementations of EDF algorithm. Advantages and disadvantages of the proposed task scheduler are discussed through the comparison to the existing solutions.

Turning over the Beds – How Optimizing Pre-Discharge Patient Teaching Reduced Discharge Delays in the Inpatient Hospital at Dana-Farber Cancer Institute and Brigham and Women's Hospital

<h3>Introduction</h3> With rapidly increasing demand at academic medical centers, inpatient resource allocation is a critical focus. Patients receiving autologous stem cell transplant (ASCT) often face complicated discharges, requiring lengthy day of discharge teaching sessions from nurses and dietitians that may cause delays in discharge time. We queried if streamlining the complex discharge process by scheduling teaching appointments in advance would reduce unnecessary delays in discharge. <h3>Methods</h3> An interdisciplinary committee reviewed existing workflows with relevant stakeholders. Process improvement methodologies, including value-stream mapping, were utilized to reduce variation and delays. New teaching workflows ("discharge pilot") were implemented using a model line approach to iteratively test operations on 1 inpatient pod and were later rolled out on 4 (of 10) pods. After IRB review, patients were allocated to receive the discharge pilot based upon admission to one of these pods. Discharge time was compiled to assess impact. <h3>Discharge teaching before</h3> Typically, discharge teaching was completed on the day of discharge as clinicians waited for caregivers to arrive, contributing to delays; teaching took 2 hours. Information given in teach sessions varied greatly between clinicians. Beyond poor utilization of staff, patients and caregivers reported they could not retain critical information as they were prepared for discharge. <h3>Discharge Teaching Workflow Redesign</h3> Teach content delivery was standardized using templated guidelines for home and medication preparation. A provider checklist (Fig 1) was created specifying when to complete discharge readiness tasks. Specialized posters were hung in patient rooms for provider documentation and to engage patients and caregivers on their care journey (Fig 2). Nurses distributed caregiver requirements prior to the teaching appointment and caregivers were notified of required attendance. Teaching appointments were scheduled 2-3 days prior to discharge. <h3>Resulting change in discharge timing</h3> Between Jan. – Sep. 2019, 58.8% of patients receiving pilot (advance) discharge teachings (n = 68) discharged before 2pm (range 10:27a – 7:01p). During this same time, only 39.7% of patients receiving no discharge intervention (n = 68) discharged before 2pm (range 11:29a – 11:30p) (p = 0.0257) (Fig 3). <h3>Conclusion</h3> Scheduling discharge teaching for patients and their caregivers in advance increased discharges from the hospital before 2pm, significant for operational improvement. This optimized staff resource utilization and allowed for more efficient bed turnover. Other factors like central line removal and transfusion timing should be examined. We are now examining satisfaction and anxiety during discharge when advance teaching is performed. Further research should assess precaution adherence for this intervention.

A Cooperative Coevolution Hyper-Heuristic Framework for Workflow Scheduling Problem

Workflow scheduling problem (WSP) is a well-known combinatorial optimization problem, which is defined to assign a series of interconnected tasks to the available resources to meet user defined Quality of Service (QoS). The guided random search methods and heuristic based methods are two most common methods for solving WSP. However, these methods either require expensive computational cost or heavily rely on human&#x0027;s empirical knowledge, which makes them inconvenient for practical applications. Keeping this in mind, this paper proposes a cooperative coevolution hyper-heuristic framework to solve WSP with an objective of minimizing the completed time of workflow. In particular, in the proposed framework, two heuristic rules, namely, the task selection rule (TSR) and the resource selection rule (RSR), are learned automatically by a cooperative coevolution genetic programming (CCGP) algorithm. The TSR is used to select a ready task for scheduling, while the RSR is used to allocate resources to perform the selected task. To improve the search efficiency, a set of low-level heuristics are defined and used as building blocks to construct the TSR and RSR. Further, to validate the effectiveness of the proposed framework, randomly generated workflow instances and four real-world workflows are used as test cases in the experimental study. Compared with several state-of-the-art methods, e.g., the Heterogeneous Earliest Finish Time (HEFT) and the Predict Earliest Finish Time (PEFT), the high-level heuristics found by our proposed framework demonstrate superior performance on all the test cases in terms of several metrics including the schedule length ratio, speedup and efficiency.

Task Scheduling in Graphic Processing Units Heterogeneous With Density of The Graph

An efficient scheduling algorithm is significant to achieve high performance in heterogeneous consisted by the central processing units (CPU) and the graphic processing unit (GPU). However, most of the scheduling algorithms are not suitable to meet the present demands of the heterogeneous. According to the characteristics of the direction acyclic graph, the direction acyclic graph nodes density list scheduling algorithm (DAG-NDLS) was proposed. The algorithm had two important phase, the first phase was task prioritizing, selected the ready task with highest priority, defined by the summation of the node density and the transmission between the node and child nodes, and the second phase was processor selection, selected the processors for the tasks with the earliest finish time minimum. In the end, compared the performances of the proposed algorithm with the heterogeneous earlier finish time algorithm and the high performance task scheduling algorithm, the results of the proposed algorithm were the best in case of the efficiency and utilization.

An enhanced priority-based scheduling heuristic for DAG applications with temporal unpredictability in task execution and data transmission

Abstract In the context of large-scale distributed computing environments, among various DAG scheduling heuristics aiming at coping with severe uncertainties during task execution, those ones that try to maximize the parallelism of ready tasks seems to be promising. However, most of such heuristics consider only the DAG structure and/or task execution cost when making scheduling decision, while the data transmission cost, which receives particular attention in big data era, is often ignored. When the information about task execution time and data transmission time (even though might be inaccurate) is given, it might be interesting to investigate whether this information should be taken into account during scheduling, and if yes, how to. This paper presents an enhanced priority-based heuristic, which properly considers the DAG structure attributes, the task execution time and data transmission time estimates in the task prioritization. The evaluation result shows the proposed approach obtains better scheduling performance than existing solutions.

Scheduling Parallel Real-Time Recurrent Tasks on Multicore Platforms

We consider the scheduling of a real-time application that is modeled as a collection of parallel and recurrent tasks on a multicore platform. Each task is a directed-acyclic graph (DAG) having a set of subtasks (i.e., nodes) with precedence constraints (i.e., directed edges) and must complete the execution of all its subtasks by some specified deadline. Each task generates potentially infinite number of instances where the releases of consecutive instances are separated by some minimum inter-arrival time. Each DAG task and each subtask of that DAG task is assigned a fixed priority. A two-level preemptive global fixed-priority scheduling ( GFP ) policy is proposed: a task-level scheduler first determines the highest-priority ready task and a subtask-level scheduler then selects its highest-priority subtask for execution. To our knowledge, no earlier work considers a two-level GFP scheduler to schedule recurrent DAG tasks on a multicore platform. We derive a schedulability test for our proposed two-level GFP scheduler. If this test is satisfied, then it is guaranteed that all the tasks will meet their deadlines under GFP . We show that our proposed test is not only theoretically better but also empirically performs much better than the state-of-the-art test in scheduling randomly generated parallel DAG task sets.

Load Balancing Scheduling Algorithm for Concurrent Workflow

Concurrent workflow scheduling algorithm works in three phases, namely rank computation, tasks selection, and resource selection. In this paper, we introduce a new ranking algorithm that computes the rank of a task, based on its successor rank and its predecessors average communication time, instead of its successors rank. The advantage of this ranking algorithm is that two dependent tasks are assigned to the same machine and as a result the scheduled length is reduced. The task selection phase selects a ready task from each workflow and creates a task pool. The resource selection phase initially assigns tasks using min-min heuristic, after initial assignment, tasks are moved from the highly loaded machines to the lightly loaded machines. Our resource selection algorithm increases the load balance among the resources due to tasks assignment heuristic and reassignment of tasks from the highly loaded machines. The simulation results show that our proposed scheduling algorithm performs better over existing approaches in terms of load balance, makespan and turnaround time.

Cost-Efficient Scheduling for Deadline Constrained Grid Workflows

Cost optimization for workflow scheduling while meeting deadline is one of the fundamental problems in utility computing. In this paper, a two-phase cost-efficient scheduling algorithm called critical chain is presented. The proposed algorithm uses the concept of slack time in both phases. The first phase is deadline distribution over all tasks existing in the workflow which is done considering critical path properties of workflow graphs. Critical chain uses slack time to iteratively select most critical sequence of tasks and then assigns sub-deadlines to those tasks. In the second phase named mapping step, it tries to allocate a server to each task considering task's sub-deadline. In the mapping step, slack time priority in selecting ready task is used to reduce deadline violation. Furthermore, the algorithm tries to locally optimize the computation and communication costs of sequential tasks exploiting dynamic programming. After proposing the scheduling algorithm, three measures for the superiority of a scheduling algorithm are introduced, and the proposed algorithm is compared with other existing algorithms considering the measures. Results obtained from simulating various systems show that the proposed algorithm outperforms four well-known existing workflow scheduling algorithms.

Scheduling of Parallel Tasks with Proportionate Priorities

Parallel computing systems promise higher performance for computationally intensive applications. Since programmes for parallel systems consist of tasks that can be executed simultaneously, task scheduling becomes crucial for the performance of these applications. Given dependence constraints between tasks, their arbitrary sizes, and bounded resources available for execution, optimal task scheduling is considered as an NP-hard problem. Therefore, proposed scheduling algorithms are based on heuristics. This paper presents a novel list scheduling heuristic, called the Noodle heuristic. Noodle is a simple yet effective scheduling heuristic that differs from the existing list scheduling techniques in the way it assigns task priorities. The priority mechanism of Noodle maintains a proportionate fairness among all ready tasks belonging to all paths within a task graph. We conduct an extensive experimental evaluation of Noodle heuristic with task graphs taken from Standard Task Graph. Our experimental study includes results for task graphs comprising of 50, 100, and 300 tasks per graph and execution scenarios with 2-, 4-, 8-, and 16-core systems. We report results for average Schedule Length Ratio (SLR) obtained by producing variations in Communication to Computation cost Ratio. We also analyse results for different degree of parallelism and number of edges in the task graphs. Our results demonstrate that Noodle produces schedules that are within a maximum of 12 % (in worst-case) of the optimal schedule for 2-, 4-, and 8-core systems. We also compare Noodle with existing scheduling heuristics and perform comparative analysis of its performance. Noodle outperforms existing heuristics for average SLR values.

Формирование навыков и умений „креативного письма” у студентов филологических и неофилологических направлений

The present article is devoted to the consideration of a current and extremely popular in both the United States and Europe subject: "creative writing". Researchers say that if the ability to think and write creatively was developed in the native tongue, the learner should not have any problem with the transfer of this ability onto a foreign language. Creative writing is not only a perfect way of mastering foreign languages, but it is also great fun and even a therapy. In the article there have been suggested exercises which develop imagination, creativity as well as ready tasks of "creative writing".

Dynamic security management for real-time embedded applications in industrial networks

Widely deployed real-time embedded systems can improve the performance of industrial applications, but these systems also face the critical challenge of providing high quality security in an unpredictable network environment. We measure the time and energy consumptions of commonly used cryptographic algorithms on a real embedded platform and introduce a method to quantify the security risk of real-time applications. We propose a Dynamic Security Risk Management (DSRM) mechanism to manage the aperiodic real-time tasks for networked industrial applications. Inspired by the feedback design philosophy, DSRM is designed as a two-level control mechanism. The upper-level component makes efforts to admit or reject the arrival tasks and assigns the reasonable security level for each admitted task. With three proportional feedback controllers at the lower level, the security level of each ready task can be adjusted adaptively according to the dynamic environments. Simulation results show the superiority of the proposed mechanism.

Simultaneous hardware and time redundancy with online task scheduling for low energy highly reliable standby-sparing system

Standby-sparing is one of the common techniques in order to design fault-tolerant safety-critical systems where the high level of reliability is needed. Recently, the minimization of energy consumption in embedded systems has attracted a lot of concerns. Simultaneous considering of high reliability and low energy consumption by DVS is a challenging problem in designing such a system, since using DVS has been shown to reduce the reliability profoundly. In this article, we have studied different schemes of standby-sparing systems from the energy consumption and reliability point of view. Moreover, we propose a new standby-sparing scheme which addresses both reliability and energy consumption jointly together. This scheme uses a simple energy management coupled with an online task scheduler which tries to dispatch those ready tasks which are expected to lead to high reliability and low energy consumption in the system. The effectiveness of the proposed scheme has been shown on TGFF under stochastic workloads. The results show 52% improvement on energy saving compared to the conventional hot standby-sparing system. Moreover, two orders of magnitude higher reliability is obtained on average, while preserving the same level of energy saving as compared to the state-of-the-art low-energy standby-sparing system (LESS).