Background Workload Research Articles

Workload Measurement in Subspecialty Placental Pathology in Canada.

Workload measurement is important to help determine optimal staffing and workload distribution for pathology laboratories. The Level 4 Equivalent (L4E) System is the most widely used Anatomical Pathology (AP) workload measurement tool in Canada. However, it was initially not developed with subspecialties in mind. In 2016, a Pan-Canadian Pediatric-Perinatal Pathology Workload Committee (PCPPPWC) was organized to adapt the L4E System to assess Pediatric-Perinatal Pathology workload. Four working groups were formed. The Placental Pathology Working Group was tasked to develop a scheme for fair valuation of placental specimens signed out by subspecialists in the context of the L4E System. Previous experience, informal time and motion studies, a survey of Canadian Pediatric-Perinatal Pathologists, and interviews of Pathologists' Assistants (PA) informed the development of such scheme. A workload measurement scheme with average L4E workload values for examination and reporting of singleton and multiple gestation placentas was proposed. The proposal was approved by the Canadian Association of Pathologist - Association canadienne des pathologistes Workload and Human Resources Committee for adoption into the L4E System. The development of a workload measurement model for placental specimens provides an average and fair valuation of these specimen types, enabling its use for resource planning and workload distribution.

The Effects of Increased Visual Information on Cognitive Workload in a Helicopter Simulator.

To test the effects of enhanced display information ("symbology") on cognitive workload in a simulated helicopter environment, using the detection response task (DRT). Workload in highly demanding environments can be influenced by the amount of information given to the operator and consequently it is important to limit potential overload. Participants (highly trained military pilots) completed simulated helicopter flights, which varied in visual conditions and the amount of information given. During these flights, participants also completed a DRT as a measure of cognitive workload. With more visual information available, pilots' landing accuracy was improved across environmental conditions. The DRT is sensitive to changes in cognitive workload, with workload differences shown between environmental conditions. Increasing symbology appeared to have a minor effect on workload, with an interaction effect of symbology and environmental condition showing that symbology appeared to moderate workload. The DRT is a useful workload measure in simulated helicopter settings. The level of symbology-moderated pilot workload. The increased level of symbology appeared to assist pilots' flight behavior and landing ability. Results indicate that increased symbology has benefits in more difficult scenarios. The DRT is an easily implemented and effective measure of cognitive workload in a variety of settings. In the current experiment, the DRT captures the increased workload induced by varying the environmental conditions, and provides evidence for the use of increased symbology to assist pilots.

Interactive Context for Mobile OS Resource Management

This paper presents an application-transparent execution context for mobile operating systems that reflects the criticality of current execution on the user interactivity. We track Android / Linux system-level events including semantic syscalls, UI actions, and standard inter-process and intra-process communication interfaces that signal the initiation and propagation of interactivity-related executions. This interactive context enables new optimizations in CPU scheduling and power state management. We recognize that online resource scheduling is susceptible to inaccurate contexts—e.g., misidentifying an interactive execution as background leads to possible priority inversion by schedulers. In particular, low-level OS events such as kernel block / wake-up carry ambiguous semantics that could induce false context propagations. We selectively expose such uncertainties to the schedulers so that executions with uncertain contexts are prioritized behind interactive executions but otherwise run as fast as possible to minimize the priority inversion. We further recognize the importance of prompt propagation of the interactivity context at the earliest possible moment to facilitate immediate prioritization. We utilize the interactivity context to enable differential per-core frequency control and background workload consolidation. Experiments on a quad-core smartphone with a dozen mobile workload scenarios show that we can, on average, save 13 percent energy with only 1 percent degradation on the interactive performance.

Interactive Effect of Background Variables and Workload Parameters on the Quality of Life among Nurses Working in Highly Complex Hospital Units: A Cross-sectional Study

Interactive Effect of Background Variables and Workload Parameters on the Quality of Life among Nurses Working in Highly Complex Hospital Units: A Cross-sectional Study

Cloud workload prediction based on workflow execution time discrepancies

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (sim 20%) better workload predictions for the future of simulated clouds than random workload selection.

Effects of Task Difficulty and Display Format on Automation Usage Strategy: A Workload Capacity Analysis.

Objective An experiment used workload capacity analysis to quantify automation usage strategy across different task difficulty and display format types in a speeded task. Background Workload capacity measures the efficiency of concurrent information processing and can serve as a gauge of automation usage strategy in speeded decision tasks. The present study used workload capacity analysis to investigate automation usage strategy while information display format and task difficulty were manipulated. Method Subjects performed a speeded judgment task assisted by an automated aid that issued decision cues at varying onset times. Response time distributions were converted to measures of workload capacity. Results Two variants of a workload capacity measure, CzOR and CzAND, gave evidence that operators moderated their own decision times both in anticipation of and following the arrival of the aid's diagnosis under difficult task conditions regardless of display format. Conclusion Assistance from an automated decision aid may cause operators to delay their own responses in a speeded decision task, producing joint response time distributions that are slower than optimal. Application Even when it renders its own judgments quickly and with high accuracy, an automated decision aid may slow responses from a user. Automation designers should consider the relative costs and benefits of response accuracy and time when choosing whether and how to implement an automated decision aid.

Modeling and simulation of distributed computing workflows in heterogeneous network environments

Next-generation computation- and network-intensive collaborative applications in various science, engineering, and e-commerce fields feature large-scale computing workflows of complex structures. Efficient algorithms are needed for task scheduling, module deployment, and service provisioning to support the execution of such distributed workflows in heterogeneous network environments and optimize their end-to-end performance for fast system response or smooth data flow. However, deploying large-scale distributed applications in real network environments is extremely challenging due to the inherent dynamics in the reliability, availability, accessibility, and capacity of massively distributed system resources, which are typically shared among a broad community of users over the Internet or dedicated connections. We propose a simulation system to study the execution dynamics of distributed computing workflows and evaluate the network performance of workflow scheduling or mapping algorithms before actual deployment and experimentation. The proposed simulation system visually illustrates the dynamic execution process of workflows in network environments by simulating module execution on computer nodes and data transfer over network links in a completely distributed and parallel manner. Furthermore, the simulation system takes background traffic and workload into consideration to achieve a high level of simulation accuracy for distributed applications deployed in shared production network environments. We implement the simulation system using multi-threaded programming and conduct extensive testings on various mapping schemes using a large number of simulated workflows and networks. The simulation-based performance measurements are quantitatively confirmed by both the experimental observations collected in real networks and the theoretical results obtained by rigorous performance analysis based on well-defined mathematical models.

Scheduling DAG-based applications in multicluster environments with background workload using task duplication

Grids or multicluster computing environments are becoming increasingly popular to both scientific and commercial applications. Process scheduling remains a central issue to be effectively resolved in order to exploit the full potential that the grid or multicluster environment can offer. We use a directed acyclic graph (DAG) to model a process or an application where the nodes of the DAG represent the tasks of the process. Prior to the execution of a process in a multicluster environment, the tasks are required to be mapped onto the clusters. In this article, it is shown that the algorithm developed by He et al. [L. He, S.A. Jarvis, D.P. Spooner, D. Bacigalupo, G. Tan, and G.R. Nudd, Mapping DAG-based applications to multiclusters with background workload, Proceedings of the 2005 IEEE International Symposium on Cluster Computing and the Grid, Cardiff, 2005, pp 855–862.] for the multicluster DAG mapping problem can be significantly improved by incorporating the task duplication strategy. The proposed process scheduling algorithm has a time complexity O(| V|2(r+d+1)), where |V| represents the number of tasks; r, the number of clusters; and d, the maximum in-degree of tasks.

Scheduling DAG-based applications in multicluster environments with background workload using task duplication

Grids or multicluster computing environments are becoming increasingly popular to both scientific and commercial applications. Process scheduling remains a central issue to be effectively resolved ...

Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.

Data mining on an OLTP system (nearly) for free

This paper proposes a scheme for scheduling disk requests that takes advantage of the ability of high-level functions to operate directly at individual disk drives. We show that such a scheme makes it possible to support a Data Mining workload on an OLTP system almost for free: there is only a small impact on the throughput and response time of the existing workload. Specifically, we show that an OLTP system has the disk resources to consistently provide one third of its sequential bandwidth to a background Data Mining task with close to zero impact on OLTP throughput and response time at high transaction loads. At low transaction loads, we show much lower impact than observed in previous work. This means that a production OLTP system can be used for Data Mining tasks without the expense of a second dedicated system. Our scheme takes advantage of close interaction with the on-disk scheduler by reading blocks for the Data Mining workload as the disk head “passes over” them while satisfying demand blocks from the OLTP request stream. We show that this scheme provides a consistent level of throughput for the background workload even at very high foreground loads. Such a scheme is of most benefit in combination with an Active Disk environment that allows the background Data Mining application to also take advantage of the processing power and memory available directly on the disk drives.

Optimal mapping of neural-network learning on message-passing multicomputers

In this paper, we study the optimal mapping of the learning process in multilayer feed-forward artificial neural networks (ANNs) on message-passing multicomputers, with an objective of minimizing the completion time of the given learning algorithm. This optimization problem is NP-hard in general and cannot be solved directly even for a small number of neurons. By observing the dominance of the computation time of a parallel neural-network learning algorithm over its communication time, we present a novel approximation algorithm for mapping large neural networks on multicomputers, given a user-specified error degree that can be tolerated in the final mapping. The target ANNs we study are learned by a static learning rule, such as the back-error-propagation learning algorithm. We study both static and dynamic mapping schemes for systems with static and dynamic background workload. Experimental results for mapping on systems with static background workload, which include a network of Sun workstations and an Intel iPSC/2 Hypercube multicomputer, are found to be very close to those predicted by analysis. Experimental results for mapping on multicomputers with dynamic background workload are obtained by simulations.