Workflow Tasks Research Articles

Replication and data management-based workflow scheduling algorithm for multi-cloud data centre platform

Scientific workflow applications have a large amount of tasks and data sets to be processed in a systematic manner. These applications benefit from cloud computing platform that offer access to virtually limitless resources provisioned elastically and on demand. Running data-intensive scientific workflow on geographically distributed data centres faces massive amount of data transfer. That affects the whole execution time and monitory cost of scientific workflows. The existing efforts on scheduling workflow concentrate on decreasing make span and budget; little concern has been paid to contemplate tasks and data sets dependency. In this paper, we introduced workflow scheduling technique to overcome data transfer and execute workflow tasks within deadline and budget constraints. The proposed techniques consist of initial data placement stage, which clusters and distributes datasets based on their dependence and replication-based partial critical path (R-PCP) technique which schedules tasks with data locality and dynamically maintains dependency matrix for the placement of generated data sets. To reduce run time datasets movement, we use interdata centre tasks replication and data sets replication to make sure data sets availability. Simulation results with four workflow applications illustrate that our strategy efficiently reduces data movement and executes all chosen workflows within user specified budget and deadline. Results reveal that R-PCP has 44.93% and 31.37% less data movement compared to random and adaptive data-aware scheduling (ADAS) techniques, respectively. R-PCP has 26.48% less energy consumption compared with ADAS technique.

Severity-Mapped Vibrotactile Cues to Support Interruption Management with Weather Messaging in the General Aviation Cockpit

Despite the increasing availability of technologies that provide access to aviation weather information in the cockpit, weather remains a prominent contributor to general aviation (GA) accidents. Pilots fail to detect the presence of new weather information, misinterpret it, or otherwise fail to act appropriately on it. When cognitive demands imposed by concurrent flight tasks are high, the risks increase for each of these failure modes. Previous research shows how introducing vibrotactile cues can help ease or redistribute some of these demands, but there is untapped potential in exploring how vibratory cues can facilitate “interruption management”, i.e., fitting the processing of available weather information into flight task workflow. In the current study, GA pilots flew a mountainous terrain scenario in a flight training device while receiving, processing, and acting on various weather information messages that were displayed visually, in graphical and text formats, on an experimental weather display. Half of the participants additionally received vibrotactile cues via a connected smartwatch with patterns that conveyed the “severity” of the message, allowing pilots to make informed decisions about when to fully attend to and process the message. Results indicate that weather messages were acknowledged more often and faster when accompanied by the vibrotactile cues, but the time after acknowledgment to fully process the messages was not significantly affected by vibrotactile cuing, nor was overall situation awareness. These findings illustrate that severity-encoded vibrotactile cues can support pilot awareness of updated weather as well as task management in processing weather messages while managing concurrent flight demands.

Bio-Inspired Workflow Scheduling on HPC Platforms

Efficient scheduling of tasks in workflows of cloud or grid applications is a key to achieving better utilization of resources as well as timely completion of the user jobs. Many scientific applications comprise several tasks that are dependent in nature and are specified by workflow graphs. The aim of the cloud meta-scheduler is to schedule the user application tasks (and the applications) so as to optimize the resource utilization and to execute the user applications in minimum amount of time. During the past decade, there have been several attempts to use bio-inspired scheduling algorithms to obtain an optimal or near optimal schedule in order to minimize the overall schedule length and to optimize the use of resources. However, as the number of tasks increases, the solution space comprising different tasks-resource mapping sequences increases exponentially. Hence, there is a need to devise mechanisms to improvise the search strategies of the bio-inspired scheduling algorithms for better scheduling solutions in lesser number of iterations/time. The objective of the research work in this paper is to use bio-inspired bacteria foraging optimization algorithm (BFOA) along with other heuristics algorithms for better search of the scheduling solution space for multiple workflows. The idea is to first find a schedule by the heuristic algorithms such as MaxMin, MinMin, and Myopic, and use these as initial solutions (along with other randomly generated solutions) in the search space to get better solutions using BFOA. The performance of our approach with the existing approaches is compared for quality of the scheduling solutions. The results demonstrate that our hybrid approach (MinMin/Myopic with BFOA) outperforms other approaches.

QoS-Aware Robotic Streaming Workflow Allocation in Cloud Robotics Systems

Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.

Energy Efficient and Reliability Aware Workflow Task Scheduling in Cloud Environment

With the rising demand for cloud services, the high energy consumption of cloud data centers is a significant problem that needs to be handled. The Dynamic Voltage and Frequency Scaling approach has been identified as one of the efficient techniques to conserve energy, particularly while scheduling real-world scientific workflows. Moreover, scientific workflows demand high-availability of the system. The computational systems in the cloud data centers are not failure-free and further frequency scaling impacts negatively on the reliability of the system by increasing the transient fault rate. A trade-off is required between energy conservation and the reliability of the computational machine. In this paper, we propose an energy-efficient and reliability aware workflow task scheduling in a cloud environment (EERS) algorithm, which conserves energy and maximizes the system reliability. The EERS comprises five sub-algorithms. First, we apply a task rank calculation algorithm to preserve the task dependencies. Second, a task clustering algorithm to reduce the communication cost which reduces energy consumption. The third is the sub-target time distribution algorithm to define the sub_makespan for each task. Further, we propose a cluster-VM mapping algorithm that reduces energy and maximizes system reliability and finally, a slack algorithm to reclaim slack associated with the non-critical tasks. The performance of the EERS evaluated on the WorkflowSim simulator using two real-world scientific workloads CyberShake and Montage. The results indicate that it surpasses the related existing approaches.

A Bespoke Electronic Health Record for Epilepsy Care (EpiToMe): Development and Qualitative Evaluation

BackgroundWhile electronic health records (EHR) bring various benefits to health care, EHR systems are often criticized as cumbersome to use, failing to fulfill the promise of improved health care delivery with little more than a means of meeting regulatory and billing requirements. EHR has also been recognized as one of the contributing factors for physician burnout.ObjectiveSpecialty-specific EHR systems have been suggested as an alternative approach that can potentially address challenges associated with general-purpose EHRs. We introduce the Epilepsy Tracking and optimized Management engine (EpiToMe), an exemplar bespoke EHR system for epilepsy care. EpiToMe uses an agile, physician-centered development strategy to optimize clinical workflow and patient care documentation. We present the design and implementation of EpiToMe and report the initial feedback on its utility for physician burnout.MethodsUsing collaborative, asynchronous data capturing interfaces anchored to a domain ontology, EpiToMe distributes reporting and documentation workload among technicians, clinical fellows, and attending physicians. Results of documentation are transmitted to the parent EHR to meet patient care requirements with a push of a button. An HL7 (version 2.3) messaging engine exchanges information between EpiToMe and the parent EHR to optimize clinical workflow tasks without redundant data entry. EpiToMe also provides live, interactive patient tracking interfaces to ease the burden of care management.ResultsSince February 2019, 15,417 electroencephalogram reports, 2635 Epilepsy Monitoring Unit daily reports, and 1369 Epilepsy Monitoring Unit phase reports have been completed in EpiToMe for 6593 unique patients. A 10-question survey was completed by 11 (among 16 invited) senior clinical attending physicians. Consensus was found that EpiToMe eased the burden of care documentation for patient management, a contributing factor to physician burnout.ConclusionsEpiToMe offers an exemplar bespoke EHR system developed using a physician-centered design and latest advancements in information technology. The bespoke approach has the potential to ease the burden of care management in epilepsy. This approach is applicable to other clinical specialties.

Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning.

The characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics. We leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit. We examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness. The use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.

Research on Workflow Task Scheduling Strategy in Edge Computer Environment

In recent years, with the rapid development of science and technology, edge technology has been gradually applied to many fields, such as computer cloud environment. Through edge computing, we can schedule cloud workflow tasks, which can allocate the workflow tasks submitted by users to appropriate computing resources. Then, based on the amount of resources used, we can pay for it immediately. For most ordinary users, workflow task scheduling is to balance the completion time and execution cost reasonably, which can coordinate the two into the optimal. With the continuous development of science and technology, intelligent mobile terminal equipment has gradually replaced the electronic computer, which has become the main computing platform for consumers. At the same time, with the development of natural language processing and video analysis technology, the world’s mobile data traffic has become an explosive growth. “Mobile edge computing” is a way for mobile network edge to provide IT service environment and cloud computing capability, which will reduce the delay of network operation and service delivery. By emphasizing proximity to mobile users, we can improve the user experience. Firstly, this paper analyzes the related concepts. Then, this paper mainly studies the workflow task scheduling strategy.

Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

SummaryWorkflow scheduling is the most focused research issue in the on‐demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced Artificial Fish Swarm Algorithm (EAFSA)‐based IaaS Cloud Partial Critical Path (IC‐PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

Hybrid Heuristic Algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

QoS-driven metaheuristic service composition schemes: a comprehensive overview

Services Oriented Architecture provides Web Services (WSs) as reusable software components that can be applied to create more complicate composite services for users according to the specified QoS limitations. However, considering many WSs that may be appropriate for each task of a user-submitted workflow, finding the optimal WSs for a composite WS to maximize the overall QoS is an NP-hard problem. As a result, numerous composition schemes have been suggested in the literature to untangle this problem by using various metaheuristic algorithms. This paper presents a comprehensive survey and taxonomy of such QoS-oriented metaheuristic WS composition schemes provided in the literature. It investigates how metaheuristic algorithms are adapted for the WS composition problem and highlight their main features, advantages, and limitations. Also, in each category of the studied composition schemes, a comparison of their applied QoS factors, evaluated metrics, exploited simulators, and properties of the applied metaheuristic algorithms are explained. Finally, the concluding remarks and future research directions are summarized to help researchers in working in this area.

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.

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

Task Duplication-Based Scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.

From female surgical resident to academic leaders: challenges and pathways forward

Despite an increasing number of female surgeons it is still very difficult for them to get into high rank positions. What are the obstacles for women to achieve a professional level where they can lead teams, departments or organisations or be invited as speakers in meetings or for editorial comments? Besides the general attitude still existing in many societies that women are responsible for child care there is a clear difference in self-presentation of women compared to their male counterparts. Women tend to underestimate their competence and skills whereas (often male) decision makers expect candidates to present themselves in a rather self-confident way. This unrecognized bias and the “glass ceiling” are encountered by many women in their career. Prestigious activities like publishing papers, presenting at conferences or working in committees are predominantly searched for by male colleagues whereas it is not uncommon for women to be confined to tasks in clinical workflow. Another bias is calling women by their first names instead of their full names, professional titles and achievements. Women should always introduce themselves by full name. Mentoring is a helpful career tool for female surgeons. A mentor can assist a mentee with strategic planning and help to redefine her way especially after a setback. When she clearly communicates her needs and expectations to seniors it shows her ambition and willingness to advance her career. Finding sponsorship is even more beneficial as a sponsor can use his influence to promote a woman to a high rank position. Surgical women´s associations exist worldwide. They enable young professionals to get in contact to female leaders/role models and exchange views with female colleagues. Institutional programs for (gender) equity are installed in some places to facilitate scientific and professional career advancement. Women are as capable as men although it is a greater challenge.

What is Your Code Clone Detection and Evolution Research Made Of?

Over the past few decades, clone detection and evolution have become a major area of study in software engineering. Clone detection experiments present several challenges to researchers such as accurate data collection, selecting proper code detection algorithms, and understanding clone evolution phenomena. This paper attempts to facilitate clone detection and evolution research by providing a structured and systematic mechanism to conduct experiments. Clone detection experiments usually consist of several tasks such as fetching data from a version control system, performing necessary pre-processing activities, and feeding the data to a clone detection algorithm. Therefore, a particular clone detection experiment can interpret as a meaningful combination of such tasks into a scientific workflow. In this work, the concrete tasks in a code clone detection workflow are referred to as Building Blocks. This paper presents a useful collection of Building Blocks identified based on a systematic literature review, and a conceptual framework of an experimental testbed to facilitate clone detection experiments. The reusability of the Building Blocks was validated using four case studies selected from the literature. The validation results confirm the reusability and the expressiveness of the Building Blocks in new ventures. Besides, the proposed experimental testbed is proven beneficial in conducting and replicating clone detection experiments.

Distributed and Communication-Aware Coalition Formation and Task Assignment in Multi-Robot Systems

In many missions, multiple robots need to cooperate to complete tasks. The workflow of these multi-robot tasks involves forming coalitions of robots, assigning them to available tasks, and jointly executing the tasks. In this article, we investigate such a workflow in an application-independent yet realistic setting. We abstract the key properties of tasks and robots and propose three distributed coalition formation and task assignment methods. Distributed methods rely on a timely and accurate exchange of state information in multi-robot systems (MRS). Thus we focus on two important communication aspects: (i) how to achieve consistent coalition formation and task assignment in the presence of communication faults, and (ii) how to reduce the communication effort required for the state updates in the MRS. In particular, we investigate the effect of event-triggered, time-triggered, and hybrid communication. We evaluate our distributed approaches in a simulation study using ns-3 and compare them with centralized methods and different network conditions. We demonstrate the sensitivity of complex missions to failure-prone MRS communication and provide robust, effective, and communication-aware methods for coalition formation and task assignment.

An HC-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Computing System

Many scientists are using meta-heuristic techniques for dynamic workflow task scheduling in the area of cloud computing systems to get optimum solutions. Many swarm intelligent algorithms have been designed so far which are having many limitations as some get trapped in local optima, a few are having low convergence speed, some are having poor global search facilities, etc. Still, there is a requirement of designing a new algorithm or modification of existing algorithms to overcome the limitations of the existing techniques. A new Hybrid Cat Swarm Optimization algorithm named H-CSO was designed inspired by the HEFT algorithm and the initialization problem of the Cat Swarm Optimization was overcome. Still, that algorithm has a limitation of getting stuck in local minima. To overcome this algorithm a part of the Crow Search Algorithm has been integrated into H-CSO and described in this paper. After simulation, it was found that the new hybrid algorithm named HC-CSO outperforms CSO and H-CSO.