Large Job Research Articles

Scheduling Job Queue on Hadoop using Hybrid Hadoop Fair Sojourn Protocol

Background/Objectives: This paper gives an idea how to avoid starvation and minimize response time using hybrid hadoop fair sojourn protocol. Methods/Statistical analysis: The scheduler assigns the resources to the cluster so that job dimension is conditional whereas the job makes improvement towards its conclusion. Scheduling selection help the approach of computer-generated time and cluster funds are focused on jobs according to their arrangement. This ensures that not any of jobs (small or large jobs) undergo starvation. Findings: The Shortest Remaining Processing Time (SRPT)1, ranks the least amount of work to complete, is the one to minimize response time (sojourn time). The Hybrid Hadoop Fair Sojourn Protocol (hybrid HFSP)2 to pause jobs with higher SRPT and allow other waiting jobs in queue based on First Come First Serve (FCFS). Application/Improvements: Relating to existing method the accuracy is developed in suggested method.

The Effect of Business Cycle Fluctuations on Migration Decisions

I examine mechanisms that differentially influence migration behavior in response to labor market shocks between employed and unemployed workers in the US. Over the period of the Great Recession, overall migration rates in the US remained close to their respective long-term trends. However, migration evolved differently by employment status with unemployed workers being more likely to migrate during the recession and employed workers less likely. I estimate a dynamic non-stationary search model of migration, focusing on the role of employment frictions, earnings, and amenities on migration decisions. My results show that employed workers are faced with a large job queuing penalty when moving locations, which results in differing migration incentives for the two groups when faced with adverse labor market shocks. I also find that migration rates were muted because of the national scope of the Great Recession. I show that moving subsidies aimed at mitigating local unemployment are greatly hindered by workers' preferences for amenities.

The Occurrence of Diseases and Related Factors in a Center for Asylum Seekers in Italy.

IntroductionItaly is the main recipient of asylum seekers in the European region, and Sicily is their first point of arrival. This geographical position creates a large job for Health Authorities to identify and deal with the health of immigrants. This study evaluates the prevalence of disease among asylum seekers, assessing which are associated factors.MethodsA cross-sectional study was conducted to analyse demographic and clinical data in an Acceptance Centres for Asylum Seekers from February 2012 to May 2013. All variables that were found to be significant on unvariable analysis for the most frequent pathologies were included in a multivariable logistic regression model.ResultsPost-traumatic stress disorders with 17.4% and major depression with 7.3% were the most frequent diseases. The factors associated with post-traumatic stress disorders among asylum seekers were: major depression diagnosis (OR=2.91, p=0.004), Pakistan as a country of origin (OR=3.88, p<0.001), the largest number of medical visits (OR=1.02, p=0.033) and refugee status (OR=1.97, p=0.036). The variables linked with the diagnosis of major depression from the multivariable analysis were: suffering from post-traumatic stress disorders (OR=3.83, p<0.001), Pakistan as a country of origin (OR=3.45, p=0.004) and the highest number of visits to psychologist (OR=1.15, p<0.001).ConclusionsThe mental wellbeing of asylum seekers needs special attention, and interventions should be done to prevent the consolidation of psychiatric morbidity. A short psychological screening after the arrival might prove helpful here. Moreover, carefully designed longitudinal studies should be carried out when political recommendations try to change the organization of psychological and healthcare services.

Aligning workload control theory and practice: lot splitting and operation overlapping issues

This paper addresses the problem of lot splitting in the context of workload control (WLC). Past studies on WLC assumed that jobs released to the shop floor proceed through the different stages of processing without being split. However, in practice, large jobs are often split into smaller transfer sublots so that they can move more quickly and independently through the production process and allow operations overlapping relating to the same job. This paper assesses the performance of different lot splitting policies for job release and dispatching strategies under lot splitting. A new dispatching rule was designed to specifically take advantage of lot splitting and operations overlapping in the context of WLC. Discrete-event simulation is used to assess system performance in relation to the ability to provide shorter delivery times and on time deliveries. Results highlight the importance of releasing the sublots of the same job together and demonstrate that combining an effective lot splitting policy with an appropriate dispatching rule can enhance the performance of production systems.

Neuroscience Gateway � Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research

Neuroscience Gateway � Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research

The More You Know: Information Effects on Job Application Rates in a Large Field Experiment

This paper presents the results from a 2.3 million person field experiment that varies whether or not a job seeker sees the number of applicants for a job posting on a large job posting website, LinkedIn. This intervention increases the likelihood that a person will finish an application by 3.5%. Women have a larger increase in their likelihood of finishing an application than men. Overall, adding this information to a job posting may offer a light-touch way to both increase application rates and alter the diversity of the applicant pool.

Interdatacenter Job Routing and Scheduling With Variable Costs and Deadlines

To reduce their operational costs, datacenter (DC) operators can schedule large jobs at DCs in different geographical locations with time- and location-varying electricity and bandwidth prices. We introduce a framework and algorithms to do so that minimize electricity and bandwidth cost subject to job indivisibility, deadlines, priorities, and DC resource constraints. In doing so, we provide a way for DC operators to predict their operational costs for different DC placements and capacities, and thus make informed decisions about how to expand their DC network. Our distributed algorithm uses estimated job arrivals and day-ahead electricity prices to optimize over sliding time windows. We demonstrate its effectiveness on a Google DC trace and investigate the effects of different cost and performance criteria. The algorithm leverages heterogeneous job resource requirements and routing and scheduling flexibility: even deadline and indivisibility constraints yield little cost increase, though they significantly improve job completion times and localization at only one DC, respectively. We show that our algorithm reduces the cost much more than optimizing only electricity, only bandwidth, or a combination of resource costs and job completion times.

서비스산업 유형에 따른 일하기 좋은 직장 (Great Work Place)의 경영성과 연구

최근 10년간 정부에서는 서비스산업에 대한 많은 정책들을 발표 및 추진하고 있으며 서비스산업의 중요한 역할은 일자리 창출이며 안정적인 고용창출능력을 가졌을 뿐만 아니라 고용파급효과도 매우 크다. 2015년 6월 통계청의 발표를 보면 청년실업률(15~29세)은 10.2%를 나타냈다. 이는 6월 기준으로 1999년 6월 11.3%를 기록한 이후 16년 만에 가장 높은 수준이다. 서비스산업은 이처럼 악화되는 경제상황을 타개할 수 있는 하나의 대안으로 인식된다. 이에 서비스산업 유형별로 한국형 GWP변수를 적용하여 경영했을 때 어떤 성과의 차이가 있을까? GWP변수들은 서비스산업별로 어떤 변수가 더 중요하게 인식될까? GWP변수들은 구성원의 팀워크 활동에 어떤 영향을 미치고 직무몰입에는 어떤 영향을 미칠까? 그리고 그간 사례연구로만 검증하였던 경영성과에 대한 영향관계도 실증적으로 검증해보고자 했다. The government announced a number of policy on service industries and pushing. over the past 10 years. Important role in the service industry is a very large degree job creation, as well as employment impacts have had a stable employment skills. In June 2015 the Bureau of Statistics released the youth unemployment rate (15~29 years) showed a 10.2%. The service industry is recognized as one of the alternatives that can overcome the deteriorating economic situation this way. Is there a difference between industry by type what performance management is applied when this service was variable with a GWP of Korean? Can GWP variables are variables which are more important to recognize the service industry? GWP variables having a certain impact on the activities of the member teamwork, job involvement has what effect on? And the impact on business performance relationship who only proven case studies whilst also want to empirical verification.

Multiobjective large scale job sequencing optimization based on a synergy of compensatory and non compensatory methods

During the last years, the manufacturing industry is experiencing an increasing trend in the utilization of mixed-model assembly lines (MMALs). These lines, often adhering to the Just-In-Time production system, produce diversified products efficiently, maintaining least possible inventories. In this paper, the sequencing of an already balanced large scale double MMAL, manufacturing cars, is modeled as an integer mathematical programming problem and optimized over four objective functions. Two classical multiobjective methods are implemented to solve the mathematical integer programming problem, as well as a synergy of them. The former two are: the compensatory one, global criterion method, and the non-compensatory one, method of satisfactory goals, proposed by Benson. In addition, the model is solved using a combination of the aforementioned methods, as proposed by the authors, which is coupling certain characteristics from both. All three methods make use of the preferences, extracted from a real decision maker, responsible for the management of the production, who is enabled to choose the most preferred among the optimal solutions. Finally, the results, obtained from the implementation of the three separate methods, are assessed with regard to their effectiveness and efficiency.

The Concert Queueing Game: Fluid Regime with Random Order Service

The concert queueing problem corresponds to determining the equilibrium arrival profile of non-cooperative customers selecting their arrival times to a queue where the service opens at a specified time. The customers are allowed to arrive before or after this time. This problem has a variety of queuing applications including how people queue at airport, movie theaters, passport offices, ration lines, etc. This also captures the settings where large computational jobs are sent to servers that open for service at a specified time. Substantial literature is devoted to studying the more tractable fluid version of this problem, that is, each customer is considered an infinitesimal particle, resulting in a non-atomic game between customers. This allows for explicit determination of the unique equilibrium arrival profile in many such settings as well as the associated socially optimal centralized solution. The knowledge of both then allows the computation of price of anarchy (PoA) in the system. The literature thus far focuses on queues with the first come first serve (FCFS) service discipline. In this paper, we again consider the fluid regime and extend the analysis to the case where the service discipline is random order service (ROS). This is equivalent to the practically equally important processor sharing regime when the service times are exponential. The latter is relevant in computational settings while the former is a good approximation to settings where a customer is selected more or less at random by the server.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost.

An Optimized Genetic Approach for Scheduling Task Duplication in Parallel Systems

Task Scheduling deals with the set of tasks assigned to parallel multiprocessor system and the execution order of the schedule so that the total execution time is minimized. The role of a good scheduling algorithm is to efficiently assign each task to a processor depending on the resources needed, the communication overhead between related tasks is reduced and the precedence relations among tasks are satisfied. It can be efficiently used for tasks that have a large calculation, and have time constraints to complete the schedule. The efficient execution of the task scheduling on parallel system takes the structure of the task and the performance characteristics of the proposed genetic algorithm. It falls in the category of NP-complete problem. This study proposes a parallel genetic algorithm-based approach to schedule tasks on parallel system with task duplication heuristics. Task duplication can minimize inter-processor communication and hence results in shorter finish times. Its performance is measured in comparison with the Round Robin (RR), First Come First Serve (FCFS), and Multi-level queue scheduling (MQS), Shortest Job First (SJF), Largest Job First (LJF) and Priority scheduling methods.

Survay on Job Scheduling, Load Balancing and Fault Tolerance Techniques for Computational Grids

Computational grid is a network of loosely coupled, heterogeneous and geographically-dispersed computers acting together to perform a large compute-intensive job. In this article, we focus on the existing approaches to grid scheduling, load balancing and fault-tolerance problems. Although grid scheduling, load balancing and fault tolerance are active research areas in grid computing, these areas have largely been and continue to be developed independent of one another each focusing on different aspects of computing. Hence, in this survey, we hope to show that robust applications that can provide efficient results can be designed by collectively considering these areas. To this end, we first provide an introduction to the motivation, grid scheduling, load balancing and fault tolerance concepts of grid computing and discuss the works that have provided significant contributions to each of these areas since its inception until 2013. We discuss their advantages, disadvantages and analyze their suitability for usage in a dynamic grid environment. We conclude that, while important advancements have been made in each of these areas individually, high performance approaches that cumulatively consider these areas still remain to be explored. We also discuss the research work that is missing and what we believe the community should be considering. To the best of our knowledge, no such survey has been conducted in the literature up to now.

Foreign Ownership, Employment and Wages in Brazil: Evidence from Acquisitions, Divestments and Job Movers

How much do developing countries benefit from foreign investment? We contribute to this question by comparing the employment and wage practices of foreign and domestic firms in Brazil, using detailed matched firm-worker panel data. In order to control for unobserved worker differences, we examine both foreign acquisitions and divestments and worker mobility, including the joint estimation of firm and worker fixed effects. We find that changes in ownership do not tend to affect wages significantly, a result that holds both at the worker- and firm-levels. However, divestments are related to large job cuts, unlike acquisitions. On the other hand, movers from foreign to domestic firms take larger wage cuts than movers from domestic to foreign firms. Moreover, on average, the fixed effects of foreign firms are considerably larger than those of domestic firms, while worker selection effects are relatively small.

The More You Know: Information Effects in Job Application Rates by Gender in a Large Field Experiment

This paper presents the results from a 2.3 million person field experiment that varies whether or not a job seeker sees the number of applicants for a job posting on a large job posting website, LinkedIn. This intervention increases the likelihood that a person will finish an application by 3.5%. Women are more likely to finish an application than men. Overall, adding this information to a job posting may offer a light-touch way to both increase application rates and alter the diversity of the applicant pool.

The Effect of Real Workloads and Synthetic Workloads on the Performance of Job Scheduling for Non-Contiguous Allocation in 2D Mesh Multicomputers

The performance of non-contiguous allocation has been traditionally carried out by means of simulations based on synthetic workloads, and also it can be significantly affected by the job scheduling strategy used for determining the order in which jobs are selected for execution. To validate the performance of the non-contiguous allocation algorithms, there has been a need to evaluate the algorithm's performance based on a real workload trace. In this paper, the performance of the well-known Greedy Available Busy List (GABL) non-contiguous allocation strategy for 2D mesh-connected multicomputers is revisited considering several important job scheduling strategies based on a real workload trace, and the results are compared to those obtained from using a synthetic workload. The scheduling strategies used are the First-Come-First-Served (FCFS), Out-of-Order (OO), and Window-Based job scheduling strategies. These strategies have been selected because they are common and they have been used in related works (Ababneh &amp; Bani-Mohammad, 2011). Extensive simulation results based on synthetic and real workload models indicate that the Window-Based job scheduling strategy can improve both overall system performance and fairness (i.e., maximum job waiting delays) by adopting a large job scheduling window. Moreover, the relative performance merits of the scheduling strategies when a real workload trace is used are in general compatible with those obtained when a synthetic workload is used.

The Relation Between Maternal Work Hours and the Cognitive Development of Young School-Aged Children

This paper analyses the relation between maternal work hours and the cognitive development of young school-going children. We find that children’s language and sorting test scores are higher when their mothers have a large part-time job or even a full-time job. We find no evidence that this can be explained by a richer home environment in terms of the number of parent–child activities provided to the child.

GridSpice: A Distributed Simulation Platform for the Smart Grid

This paper describes GridSpice, a scalable open-source simulation framework for modeling, designing, and planning of the smart grid. GridSpice seamlessly integrates existing electric power simulation tools to enable modeling of large electric networks that blur the boundaries between generation, transmission, distribution, and markets. This is achieved via a cloud-based architecture that allows for parallelizing large simulation jobs across many virtual machines using a pay-as-you-go model. GridSpice simulations can be managed through a Representational State Transfer (REST) application programming interface (API), or through a Python library, allowing users to run simulations programmatically and interface with disparate data inputs, energy management systems (EMS), distribution management systems (DMS), and postprocessing tools. These capabilities make GridSpice an ideal tool for the development and testing of new grid control and optimization algorithms. GridSpice also provides an easy-to-use browser-based interface to allow novice users to begin without any setup or configuration on their local PC. A first implementation of the GridSpice framework integrates Gridlab-D and MATPOWER as simulation tools, and has been used for projects including optimizing the placement of distributed generation and developing optimal dispatch schedules for flexible loads. The GridSpice framework and Gridlab-D are freely available in open-source under the BSD license.

Data Management Challenges in Cloud Environments

Recently the cloud computing paradigm has been receiving special excitement and attention in the new researches. Cloud computing has the potential to change a large part of the IT activity, making software even more interesting as a service and shaping the way IT hardware is proposed and purchased. Developers with novel ideas for new Internet services no longer require the large capital outlays in hardware to present their service or the human expense to do it. These cloud applications apply large data centers and powerful servers that host Web applications and Web services. This report presents an overview of what cloud computing means, its history along with the advantages and disadvantages. In this paper we describe the problems and opportunities of deploying data management issues on these emerging cloud computing platforms. We study that large scale data analysis jobs, decision support systems, and application specific data marts are more likely to take benefit of cloud computing platforms than operational, transactional database systems. &#x0D;

The Value of Postsecondary Credentials in the Labor Market: An Experimental Study

We study employers' perceptions of the value of postsecondary degrees using a field experiment. We randomly assign the sector and selectivity of institutions to fictitious resumes and apply to real vacancy postings for business and health jobs on a large online job board. We find that a business bachelor's degree from a for-profit online institution is 22 percent less likely to receive a callback than one from a nonselective public institution. In applications to health jobs, we find that for-profit credentials receive fewer callbacks unless the job requires an external quality indicator such as an occupational license. (JEL I23, I26, J24, J44, J63, M51)