Tasks Of Job Research Articles

SLearn: A Case for Task Sampling Based Learning for Cluster Job Scheduling

The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which uses past job execution information to estimate the runtime properties of newly arrived jobs. However, with fast-paced development in cluster technology (in both hardware and software) and changing user inputs, job runtime properties can change over time, which lead to inaccurate predictions. In this paper, we explore the potential and limitation of real-time learning of job runtime properties, by proactively sampling and scheduling a small fraction of the tasks of each job. Such a task-sampling-based approach exploits the similarity among runtime properties of the tasks of the same job and is inherently immune to changing job behavior. Our analytical and experimental analysis of 3 production traces with different skew and job distribution shows that learning in space can be substantially more accurate. Our simulation and testbed evaluation on Azure of the two learning approaches anchored in a generic job scheduler using 3 production cluster job traces shows that despite its online overhead, learning in space reduces the average Job Completion Time (JCT) by 1.28×, 1.56×, and 1.32× compared to the prior-art history-based predictor.

TEBAS: A Time-Efficient Balance-Aware Scheduling Strategy for Batch Processing Jobs

Common schedulers for long-term running services that perform task-level optimization fail to accommodate short-living batch processing (BP) jobs. Thus, many efficient job-level scheduling strategies are proposed for BP jobs. However, the existing scheduling strategies perform time-consuming objective optimization which yields non-negligible scheduling delay. Moreover, they tend to assign BP jobs in a centralized manner to reduce monetary cost and synchronization overhead, which can easily cause resource contention due to the task co-location. To address these problems, this paper proposes TEBAS, a time-efficient balance-aware scheduling strategy, which spreads all tasks of a BP job into the cluster according to the resource specifications of a single task based on the observation that computing tasks of a BP job commonly possess similar features. The experimental results show the effectiveness of TEBAS in terms of scheduling efficiency and load balancing performance.

Solving Multi-Criteria Problem by Using Efficient Algorithm

For multi criteria sequencing problem on one machine, we propose an algorithm total completion total lateness maximum lateness maximum earliness (CLLE) to find efficient solutions in this paper. The criteria are total completion time(∑Cj), total lateness(∑Lj), maximum Lateness (Lmax) and maximum earliness (Emax). A collection of n independent jobs (tasks) has to be sequenced on one machine jobs j (j=1, 2, 3, …., n) wanted processing time pj and due date dj. Conclusions is formulated on the competence of the (CLLE) algorithm based on results from computational experiments.

Impact of Task-Fit Misalignment and the Director of Clinical Education: A Grounded Theory Study.

The roles and responsibilities of the director of clinical education (DCE) are of interest in the academic physical therapist (PT) community. This study explored the impact of professional and positional task characteristics of the DCE position on task-fit misalignment within PT education programs. Task-fit explores how the tasks of a particular job align with the appropriately trained person who holds the position. A recently published DCE Matrix highlights 2 extremes of misalignment and nonoptimal task-fit between the position and the person. Misalignment was investigated rather than alignment because negative connotations often have greater impact on behavior, decision making, and relationships. The impact of misalignment is unknown. Program directors and DCEs among ACAPT member PT programs were surveyed. A qualitative grounded theory design was used. Two rounds of surveys with focused questions about the impact of task-fit misalignment. Qualitative content data analysis through a constant comparative process was followed. Respondent descriptive statistics were collected. Three realms of impact were identified when misalignment of positional and professional characteristics exists. The realms include the 1) individual person holding the position; 2) academic program; or 3) physical therapy profession. There is a high level of agreement that task-fit misalignment affects each realm; variability exists about its prevalence. There seems to be an interconnectedness of impact among the person, the program, and the profession when misalignment exists between the fit of the DCE to the job or the work environment to the DCE. Findings indicate job crafting may be necessary to evolve the nature of the DCE work toward higher director-level tasks. This is the first study of its kind to explore the impact intersection of person-job fit and task-fit and the impact on a DCE's responsibilities. Nonrespondent or attrition bias may have limited sample size.

Co-Scheduler: A Coflow-Aware Data-Parallel Job Scheduler in Hybrid Electrical/Optical Datacenter Networks

To support higher demand for datacenter networks, networking researchers have proposed hybrid electrical/optical datacenter networks (Hybrid-DCN) that leverages optical circuit switching (OCS) along with traditional electrical packet switching (EPS). However, due to the high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reconfiguration</i> delay of OCS, OCS is used only for bulk data transfers between racks to amortize the reconfiguration delay. Existing job schedulers for data-parallel frameworks are not designed for Hybrid-DCN, since they neither place tasks to aggregate data traffic to take advantage of OCS, nor schedule tasks to minimize the Coflow completion time (CCT). In this paper, we describe the mismatch between existing job schedulers and the advanced Hybrid-DCN, introduce the requirements for the new scheduler, and present the implementation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co-scheduler</i> , a job scheduler for data-parallel frameworks that aims to improve job performance by placing the tasks of jobs to aggregate enough data traffic to better leverage OCS to minimize the CCT in Hybrid-DCN. Specifically, for every job, Co-scheduler computes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guidelines</i> on how many racks to place the job’s input data and the job’s tasks. The guidelines are dynamically generated based on the real-time job characteristics or predictable job characteristics from prior runs, with the aim of leveraging OCS whenever possible and efficient and minimizing CCT of jobs. Co-scheduler then schedules the tasks of jobs based on the guidelines. We evaluate the effectiveness of Co-scheduler using trace-driven simulation. The evaluation demonstrates that Co-scheduler can improve makespan, average job completion time, and average CCT of a workload by up to 56%, 61%, and 79%, respectively, compared to the state-of-the-art schedulers.

Integrated Production and Transportation Scheduling Method in Hybrid Flow Shop

The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem (HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles (AGVs) is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

Risk factors for occupational injuries in schools among educators and support staff

Introduction: School districts employ a large number of employees who work in educational (e.g., teachers) or other support roles, including educational assistants, custodians, food service, bus drivers, and community and recreation workers. District employees perform a wide array of job tasks and experience a wide spectrum of work-related risks and injuries. Methods: Workers’ compensation data were coupled with Minnesota Department of Education district employee denominator data to evaluate risk factors for injury and severity. Variables included district location and type, employee job classification, age, and gender. Rates of injury and rate ratios were calculated to measure comparative injury risk using negative binomial regression and 95% confidence internals. Incidence and frequency of injury cause, nature, and body part we calculated. Results: Saint Paul and Minneapolis metropolitan area (versus non-metro) districts had higher risk (RR = 1.35, 95%CI = 1.18–1.54) of employee injuries. All job classifications in support roles had increased risk of injury claims versus educators, however food service (RR = 5.14, 95%CI = 4.61–5.74), custodial (RR = 3.85, 95%CI = 3.41–4.34), and transportation (RR = 4.15, 95%CI = 3.38–5.10) exhibited the highest comparative risk to educators; significant risk of lost-time injury was also present in these workers. Males and females had similar risk of injury for all claims, however males had elevated risk of lost-time injury (RR = 1.46, 95%CI = 1.26–1.69) versus females. All age groups >41-years-old exhibited increased risk of injury as compared to 31–40-year-olds. The magnitude of lost-time injury risk also increased with age. Falls and slips (29.1%), strains/sprains/ruptures (45.2%), and upper extremities (31.3%) most frequent cause, nature, and body part injured, respectively. Conclusions: Characteristics of districts, schools, workers, and their jobs tasks and hazards vary. Many categories of support staff in schools have elevated risk of injury, including lost-time injury, as compared to educators. Practical Applications: Injury prevention in schools should be approached by targeting job classifications; high risk jobs can be prioritized for prevention.

Cautiously optimistic: A survey of radiation oncology professionals' perceptions of automation in radiotherapy planning.

While there is evidence to show the positive effects of automation, the impact on radiation oncology professionals has been poorly considered. This study examined radiation oncology professionals' perceptions of automation in radiotherapy planning. An online survey link was sent to the chief radiation therapists (RT) of all Australian radiotherapy centres to be forwarded to RTs, medical physicists (MP) and radiation oncologists (RO) within their institution. The survey was open from May-July 2019. Participants were 204 RTs, 84 MPs and 37 ROs (response rates ∼10% of the overall radiation oncology workforce). Respondents felt automation resulted in improvement in consistency in planning (90%), productivity (88%), quality of planning (57%), and staff focus on patient care (49%). When asked about perceived impact of automation, the responses were; will change the primary tasks of certain jobs (66%), will allow staff to do the remaining components of their job more effectively (51%), will eliminate jobs (20%), and will not have an impact on jobs (6%). 27% of respondents believe automation will reduce job satisfaction. 71% of respondents strongly agree/agree that automation will cause a loss of skills, while only 25% strongly agree/agree that the training and education tools in their department are sufficient. Although the effect of automation is perceived positively, there are some concerns on loss of skillsets and the lack of training to maintain this. These results highlight the need for continued education to ensure that skills and knowledge are not lost with automation.

Randomized mechanism design for decentralized network scheduling

ABSTRACT In the network scheduling, jobs (tasks) must be scheduled on uniform machines (processors) connected by a complete graph so as to minimize the total weighted completion time. This setting can be applied in distributed multi-processor computing environments and also in operations research. In this paper, we study the design of randomized decentralized mechanism in the setting where a set of non-preemptive jobs select randomly a machine from a set of uniform machines to be processed on, and each machine can process at most one job at a time. We introduce a new concept of myopic Bayes–Nash incentive compatibility which weakens the classical Bayes–Nash incentive compatibility and derive a randomized decentralized mechanism under the assumption that each job is a rational and selfish agent. We show that our mechanism can induce jobs to report truthfully their private information referred to myopic Bayes–Nash implementability by using a graph theoretic interpretation of the incentive compatibility constraints. Furthermore, we prove that the performance of this mechanism is asymptotically optimal.

Visualization of job availability based on text analytics localization approach

&lt;span&gt;Rate of employment is a strong indicator of economic stability of a country. It relates to the number of volumes of produced products and services. If the unemployment rate is high, the amount of gross domestic product (GDP) of a country may be declined. One of the main factors that contributes to low rate of employment is the mismatch between job seeker and the requirement of the job applied. This is due to the limited analysis performed on the relevant information on job advertisement; such as, skills, responsibilities of the job, location and expectation of the employers. The obscure job descriptions provided in the advertisement may result in application of unsuitable candidates that can cause rejection of the candidate and the potential employer may take a long time to filter and evaluate the applications. A system that is able to provide relevant information in a simple and catchy way is needed to simplify the task of job searching. In this paper we proposed a text analytics technique to extract users’ comments from social media such as Twitter and Facebook on job advertisement. The result is then displayed in a geotagged map that can reveal the density of job availability based on geographical location. The job seekers can easily observe and select their desired job location. The initial system shows potential of the inclusion of the proposed approach in job advertisement websites. In comparison to other job searching websites, this system can provide additional information on public view about the advertised job obtained from the social media text analytics. With this additional information, jobseekers have more confidence in job selection and allows employers to receive more suitable candidates for the available positions. It is hoped that the proposed system can tailor the job advertisements to the need of the jobseeker and making the job application more relevant hence reducing the potential employers’ processing time.&lt;/span&gt;

DAMHSE: Programming heterogeneous MPSoCs with hardware acceleration using dataflow-based design space exploration and automated rapid prototyping

Heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs) with programmable hardware acceleration are currently gaining market share in the embedded device domain. Largest MPSoCs combine several software processing cores with programmable logic. In these systems, reaching the optimal implementation performance is difficult because many manual and time-consuming steps are required to build, from the application specification, a prototype with measurable performances. In this paper, a method is developed that, based on state-of-the-art tools and High-Level Synthesis, deploys within less than an hour a whole hardware-software rapid prototype from a unique dataflow-based application representation: DAMHSE (DAtaflow Method for Hardware/Software Exploration). A human-driven Design Space Exploration (DSE) is conducted in order to find the most performing architectural solution, and compilable/synthesizable code is generated. The method has been tested on an image processing system with software and hardware parallelism. Results show that the obtained absolute performance (pixel/cycles) reaches state-of-the-art, and that DAMHSE leads to a heterogeneous system where performance increases significantly when the application is granted with more hardware resources.One of the greatest challenges in creating such a design automation method resides in the application behavior that may change over time and affect application concurrency and system performance. In order to overcome this problem, the design-time DAtaflow Method for Hardware/Software Exploration (DAMHSE) method is complemented with a runtime application management system that dynamically dispatches jobs (tasks) among the available processing elements (CPUs and/or hardware accelerators). Experimental results show that the performance penalty due to runtime application mapping and scheduling is limited and that the computational performance of the adaptive system remains high. Apart from the vendor-specific HLS, the tools and the frameworks used by the proposed method are open source and tutorials are available to reproduce the results.

FNEE1 Arkansas EFNEP's Transition to Online Training Shows Positive Feedback

Objective To maximize the efficiency of training provided to Arkansas EFNEP employees using an online interactivity format. Use of Theory or Research The Arkansas Expanded Food and Nutrition Education Program (EFNEP) state staff is responsible for program training for EFNEP Educators across 17 counties in Arkansas. As a team, we concluded that our program training needed greater continuity. Arkansas EFNEP changed to an online training delivery method (rather than all face-to-face) in hopes that this would make better use of time and money. Further, the EFNEP Educators will obtain a great conceptual knowledge of their role in a shorter amount of time and be able to work independently sooner. Target Audience The target audience is the Arkansas EFNEP Educators. Program Description Each newly hired EFNEP Educator has responsibility for completion of all online training modules housed through the University of Arkansas Extension website. Once the Educator has received access to this website, they can begin training. The training is divided into five modules, which are the five key areas of the Educators job responsibilities. Once they complete a module, state staff will schedule a Zoom session with the Educator to follow up with any questions and provide a preview for the next module. The training can be completed in one to two weeks. Results At the conclusion of the training, the employee completes an evaluation. The evaluations rate the training based on the improvement of the employee's knowledge, the instruction, the employee's perception of the ability to complete their jobs tasks, the presentation of information, and suggestions for improvement. To date, evaluations have provided positive feedback. Conclusions The online delivery method chosen for the EFNEP training has shown very positive results. The Educators can complete training in shorter amount of time with more focus and consistency. Funding Expanded Food and Nutrition Education Program.

Dependency-Aware Network Adaptive Scheduling of Data-Intensive Parallel Jobs

Datacenter clusters often run data-intensive jobs in parallel for improving resource utilization and cost efficiency. The performance of parallel jobs is often constrained by the cluster's hard-to-scale network bisection bandwidth. Various solutions have been proposed to address the issue, however, most of them do not consider inter-job data dependencies and schedule jobs independently from one another. In this work, we find that aggregating and co-locating the data and tasks of dependent jobs offer an extra opportunity for data locality improvement that can help to greatly enhance the performance of jobs. We propose and design Dawn, a dependency-aware network-adaptive scheduler that includes an online plan and an adaptive task scheduler. The online plan, taking job dependencies into consideration, determines where (i.e., preferred racks) to place tasks in order to proactively aggregate dependent data. The task scheduler, based on the output of online plan and dynamic network status, adaptively schedules tasks to co-locate with the dependent data in order to take advantage of data locality. We implement Dawn on Apache Yarn and evaluate it on physical and virtual clusters using various machine learning and query workloads. Results show that Dawn effectively improves cluster throughput by up to 73 and 38 percent compared to Fair Scheduler and ShuffleWatcher, respectively. Dawn not only significantly enhances the performance of jobs with dependency, but also works well for jobs without dependency.

Multistage interval scheduling games

We study a game theoretical model of multistage interval scheduling problems in which each job consists of exactly one task (interval) for each of t stages (machines). In the game theoretical model, the machine of each stage is controlled by a different selfish player who wants to maximize her total profit, where the profit for scheduling the task of a job j is a fraction of the weight of the job that is determined by the set of players that also schedule their corresponding task of job j. We provide criteria for the existence of pure Nash equilibria and prove bounds on the Price of Anarchy and the Price of Stability for different social welfare functions.

Robust and Probabilistic Failure-Aware Placement

Motivated by the growing complexity and heterogeneity of modern data centers, and the prevalence of commodity component failures, this article studies the failure-aware placement problem of placing tasks of a parallel job on machines in the data center with the goal of increasing availability. We consider two models of failures: adversarial and probabilistic. In the adversarial model, each node has a weight (higher weight implying higher reliability) and the adversary can remove any subset of nodes of total weight at most a given bound W and our goal is to find a placement that incurs the least disruption against such an adversary. In the probabilistic model, each node has a probability of failure and we need to find a placement that maximizes the probability that at least K out of N tasks survive at any time. For adversarial failures, we first show that (i) the problems are in Σ 2 , the second level of the polynomial hierarchy; (ii) a variant of the problem that we call R obust F ap (for Robust Failure-Aware Placement) is co-NP-hard; and (iii) an all-or-nothing version of R obust F ap is Σ 2 -complete. We then give a polynomial-time approximation scheme (PTAS) for R obust F ap , a key ingredient of which is a solution that we design for a fractional version of R obust F ap . We then study H ier R obust F ap , which is the fractional R obust F ap problem over a hierarchical network, in which failures can occur at any subset of nodes in the hierarchy, and a failure at a node can adversely impact all of its descendants in the hierarchy. To solve H ier R obust F ap , we introduce a notion of hierarchical max-min fairness and a novel Generalized Spreading algorithm, which is simultaneously optimal for every upper bound W on the total weight of nodes that an adversary can fail. These generalize the classical notion of max-min fairness to work with nodes of differing capacities, differing reliability weights, and hierarchical structures. Using randomized rounding, we extend this to give an algorithm for integral H ier R obust F ap . For the probabilistic version, we first give an algorithm that achieves an additive ϵ approximation in the failure probability for the single level version, called P rob F ap , while giving up a (1 + ϵ) multiplicative factor in the number of failures. We then extend the result to the hierarchical version, H ier P rob F ap , achieving an ϵ additive approximation in failure probability while giving up an (L + ϵ) multiplicative factor in the number of failures, where L is the number of levels in the hierarchy.

U.S. Army physical demands study: Identification and validation of the physically demanding tasks of combat arms occupations

ObjectivesIn 2013, the U.S. Army began developing physical tests to predict a recruit’s ability to perform the critical, physically demanding tasks (CPDTs) of combat arms jobs previously not open to women. The purpose of this paper is to describe the methodology and results of analyses of the accuracy and inclusiveness of the critical physically demanding task list. While the job analysis included seven combat arms jobs, only data from the 19D Cavalry Scout occupation are presented as the process was similar for all seven jobs. DesignJob analysis MethodsAs the foundation, senior subject matter experts from each job reviewed materials and reached consensus on the CPDTs and performance standards for each job. The list was reviewed by Army leadership and provided to the researchers. The job analysis consisted of reviewing job and task related documents and field manuals, observing >900 soldiers performing the 32 CPDTs, conducting two focus groups for each job, and analyzing responses to widely distributed job analysis questionnaires. ResultsOf the 32 CPDTs identified for seven combat jobs, nine were relevant to 19D soldiers. Focus group discussions and job analysis questionnaire results supported the tasks and standards identified by subject matter experts while also identifying additional tasks. ConclusionsThe tasks identified by subject matter experts were representative of the physically demanding aspects of the 19D occupation.

INTEGRATED EXTREMAL OPTIMIZATION AND RANDOM FOREST BASED SCHEDULING FOR CLOUD COMPUTING ENVIRONMENT

Cloud computing is the most recent continuation of parallel computing, distributed computing and grid computing. Basically it is a parallel and distributed system containing various powerfully interconnected virtualized machines. Virtual machines are assigned over system to give administrations to users. The major problem nowadays occurs in cloud computing is Load Balancing. Basically it is a term used to spread jobs tasks over multiple networks. This paper defines the parallel processor scheduling technique in cloud computing environment. The proposed technique of Random Forest with integration of Extremal Optimization improves the efficiency and results of the tasks spreaded into multiple networks defined by the parameters like speedup, makespan time, migration cost etc. which indicates better outcomes of proposed technique than the existing one.

Competitive algorithms for multistage online scheduling

We study an online flow shop scheduling problem where each job consists of several tasks that have to be completed in t different stages and the goal is to maximize the total weight of accepted jobs. The set of tasks of a job contains one task for each stage and each stage has a dedicated set of identical parallel machines corresponding to it that can only process tasks of this stage. In order to gain the weight (profit) associated with a job j, each of its tasks has to be executed between a task-specific release date and deadline subject to the constraint that all tasks of job j from stages 1,⋯,i−1 have to be completed before the task of the ith stage can be started. In the online version, jobs arrive over time and all information about the tasks of a job becomes available at the release date of its first task. This model can be used to describe production processes in supply chains when customer orders arrive online.Even the basic version of the offline problem with a single machine in each stage, unit weights, unit processing times, and fixed execution times for all tasks (i.e., deadline minus release date equals processing time) is APX-hard and we show that the approximation ratio of any polynomial-time approximation algorithm for this basic version of the problem must depend on the number t of stages.For the online version of the basic problem, we provide a (2t−1)-competitive deterministic online algorithm and a matching lower bound. Moreover, we provide several (sometimes tight) upper and lower bounds on the competitive ratios of online algorithms for several generalizations of the basic problem involving different weights, arbitrary release dates and deadlines, different processing times of tasks, and several identical machines per stage.

Cloud Computing-System Implementation for Business Applications

Nowadays, companies are faced with the task of processing huge quantum of data. As the traditional database systems cannot handle this task in a cost-efficient manner, companies have built customized data processing frameworks. Cloud computing has emerged as a promising approach to rent a large IT infrastructure on a short-term pay-per-usage basis. This paper attempts to schedule tasks on compute nodes so that data sent from one node to the other has to traverse as few network switches as possible. The challenges and opportunities for efficient parallel data processing in cloud environments have been demonstrated and Nephele, the first data processing framework, has been presented to exploit the dynamic resource provisioning offered by the IaaS clouds. The overall utilisation of resources has been improved by assigning specific virtual machine types to specific tasks of a processing job and by automatically allocating or deallocating virtual machines in the course of a job execution. This has led to substantial reduction in the cost of parallel data processing.

Gas migration through cement slurries analysis: A comparative laboratory study

Cementing is an essential part of every drilling operation. Protection of the wellbore from formation fluid invasion is one of the primary tasks of a cement job. Failure in this task results in catastrophic events, such as blow outs. Hence, in order to save the well and avoid risky and operationally difficult remedial cementing, slurry must be optimized to be resistant against gas migration phenomenon. In this paper, performances of the conventional slurries facing gas invasion were reviewed and compared with modified slurry containing special gas migration additive by using fluid migration analyzer device. The results of this study reveal the importance of proper additive utilization in slurry formulations. The rate of gas flow through the slurry in neat cement is very high; by using different types of additives, we observe obvious changes in the performance of the cement system. The rate of gas flow in neat class H cement was reported as 36000 ml/hr while the optimized cement formulation with anti-gas migration and thixotropic agents showed a gas flow rate of 13.8 ml/hr.