Streaming algorithms for multitasking scheduling with shared processing

In this paper, we design the first streaming algorithms for the problem of multitasking scheduling on parallel machines with shared processing. In one pass, our streaming approximation schemes can provide an approximate value of the optimal makespan. If the jobs can be read in two passes, the algorithm can find the schedule with the approximate value. This work not only provides an algorithmic big data solution for the studied problem, but also gives an insight into the design of streaming algorithms for other problems in the area of scheduling

Streaming Algorithms for Multitasking Scheduling with Shared Processing

In this paper, we design the first streaming algorithms for the problem of multitasking scheduling on parallel machines with shared processing. In one pass, our streaming approximation schemes can provide an approximate value of the optimal makespan. If the jobs can be read in two passes, the algorithm can find the schedule with the approximate value. This work not only provides an algorithmic big data solution for the studied problem, but also gives an insight into the design of streaming algorithms for other problems in the area of scheduling

Multitasking Scheduling with Shared Processing

Recently, the problem of multitasking scheduling has attracted a lot of attention in the service industries where workers frequently perform multiple tasks by switching from one task to another. Hall, Leung and Li (Discrete Applied Mathematics 2016) proposed a shared processing multitasking scheduling model which allows a team to continue to work on the primary tasks while processing the routinely scheduled activities as they occur. The processing sharing is achieved by allocating a fraction of the processing capacity to routine jobs and the remaining fraction, which we denote as sharing ratio, to the primary jobs. In this paper, we generalize this model to parallel machines and allow the fraction of the processing capacity assigned to routine jobs to vary from one to another. The objectives are minimizing makespan and minimizing the total completion time. We show that for both objectives, there is no polynomial time approximation algorithm unless P = NP if the sharing ratios are arbitrary for all machines. Then we consider the problems where the sharing ratios on some machines have a constant lower bound. For each objective, we analyze the performance of the classical scheduling algorithms and their variations and then develop a polynomial time approximation scheme when the number of machines is a constant

Multitasking Scheduling with Shared Processing

Recently, the problem of multitasking scheduling has attracted a lot of attention in the service industries where workers frequently perform multiple tasks by switching from one task to another. Hall, Leung and Li (Discrete Applied Mathematics 2016) proposed a shared processing multitasking scheduling model which allows a team to continue to work on the primary tasks while processing the routinely scheduled activities as they occur. The processing sharing is achieved by allocating a fraction of the processing capacity to routine jobs and the remaining fraction, which we denote as sharing ratio, to the primary jobs. In this paper, we generalize this model to parallel machines and allow the fraction of the processing capacity assigned to routine jobs to vary from one to another. The objectives are minimizing makespan and minimizing the total completion time. We show that for both objectives, there is no polynomial time approximation algorithm unless P=NP if the sharing ratios are arbitrary for all machines. Then we consider the problems where the sharing ratios on some machines have a constant lower bound. For each objective, we analyze the performance of the classical scheduling algorithms and their variations and then develop a polynomial time approximation scheme when the number of machines is a constant

1. Introduction

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Multitasking versus multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors

Why is it that behaviors that rely on control, so striking in their diversity and flexibility, are also subject to such striking limitations? Typically, people cannot engage in more than a few—and usually only a single—control-demanding task at a time. This limitation was a defining element in the earliest conceptualizations of controlled processing; it remains one of the most widely accepted axioms of cognitive psychology, and is even the basis for some laws (e.g., against the use of mobile devices while driving). Remarkably, however, the source of this limitation is still not understood. Here, we examine one potential source of this limitation, in terms of a trade-off between the flexibility and efficiency of representation (“multiplexing”) and the simultaneous engagement of different processing pathways (“multitasking”). We show that even a modest amount of multiplexing rapidly introduces cross-talk among processing pathways, thereby constraining the number that can be productively engaged at once. We propose that, given the large number of advantages of efficient coding, the human brain has favored this over the capacity for multitasking of control-demanding processes.National Science Foundation (U.S.). Graduate Research Fellowship Progra

The impact of working memory load on task execution and online plan adjustment during multitasking in a virtual environment

Three experiments investigated the impact of working memory load on online plan adjustment during a test of multitasking in young, nonexpert, adult participants. Multitasking was assessed using the Edinburgh Virtual Errands Test (EVET). Participants were asked to memorize either good or poor plans for performing multiple errands and were assessed both on task completion and on the extent to which they modified their plans during EVET performance. EVET was performed twice, with and without a secondary task loading a component of working memory. In Experiment 1, articulatory suppression was used to load the phonological loop. In Experiment 2, oral random generation was used to load executive functions. In Experiment 3, spatial working memory was loaded with an auditory spatial localization task. EVET performance for both good- and poor-planning groups was disrupted by random generation and sound localization, but not by articulatory suppression. Additionally, people given a poor plan were able to overcome this initial disadvantage by modifying their plans online. It was concluded that, in addition to executive functions, multiple errands performance draws heavily on spatial, but not verbal, working memory resources but can be successfully completed on the basis of modifying plans online, despite a secondary task load

Methods for Performance Evaluation of Parallel Computer Systems

Although parallel computers have existed for many years, recently there has been a surge of academic, industrial and governmental interest in parallel computing. Commercially manufactured parallel computers have started to become available. Many new experimental parallel architectures are reported in the literature every year. Software for many types of applications, from scientific number crunching to artificial intelligence, is being written to run on parallel machines. Performance is an essential consideration both in the design of new systems and the deployment of existing systems. Users of computers wish to utilize their hardware and software systems as efficiently as possible. Over the years, a field known as computer performance evaluation has arisen to address the problem of quantifying and predicting computer performance. Methods exist that can determine how efficiently a system's resources are being used. These can help track down the probable causes of performance problems

The effects of time of day and circadian rhythm on performance during variable levels of cognitive workload

The present study examined the effects of time of day of testing on a simulated aviation task. The tasks required the participants to engage in multitasking while electroencephalogram (EEG) data was collected to objectively measure participants’ workload. Task demands were altered throughout the testing period to expose participants to both high and low workload conditions. Additionally, individual differences in circadian rhythm were explored by assessing participants’ circadian typology. No significant differences in performance were found resulting from time of day differences. However, performance and EEG differences were found based on phase of testing and workload manipulations. Subjective workload measures were influenced by time of day, with a moderating effect of circadian typology. Implications are discussed

Information, Technology and Information Worker Productivity

We study the fine-grained relationships among information flows, IT use, and individual information-worker productivity, by analyzing work at a midsize executive recruiting firm. We analyze both project-level and individual-level performance using: (1) direct observation of over 125,000 e-mail messages over a period of 10 months by individual workers (2) detailed accounting data on revenues, compensation, project completion rates, and team membership for over 1300 projects spanning 5 years, and (3) survey data on a matched set of the same workers’ IT skills, IT use and information sharing. These detailed data permit us to econometrically evaluate a multistage model of production and interaction activities at the firm, and to analyze the relationships among communications flows, key technologies, work practices, and output. We find that (a) the structure and size of workers’ communication networks are highly correlated with their performance; (b) IT use is strongly correlated with productivity but mainly by allowing multitasking rather than by speeding up work; (c) productivity is greatest for small amounts of multitasking but beyond an optimum, multitasking is associated with declining project completion rates and revenue generation; and (d) asynchronous information seeking such as email and database use promotes multitasking while synchronous information seeking over the phone shows a negative correlation. Overall, these data show statistically significant relationships among social networks, technology use, completed projects, and revenues for project-based information workers. Results are consistent with simple production models of queuing and multitasking and these methods can be replicated in other settings, suggesting new frontiers for bridging the research on social networks and IT value.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc