Resource-intensive Tasks Research Articles

Who Should See the Patient? On Discretionary Patient-Provider Assignments in Hospitals

Traditional principles in operations management and organizational theory suggest that standardizing assignments of tasks to individuals can have substantial benefits and boost performance. However, in various organizations including hospitals, individuals are not forced to follow recommended assignments, and thus, deviations from routine task assignments are often observed. This is due to the conventional wisdom that professionals should be given the opportunity to use their discretionary judgement and deviate from routine assignments when they perceive it to be advantageous. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in a children’s hospital, and generate insights into whether and when hospital administrators should allow providers to use their discretion to deviate from routine assignments. To perform our analyses, we identify 73 top medical diagnoses and use detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a carefully-designed survey of physicians and utilize it to identify the routine provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of discretionary deviations from routine provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmission rates and rate of adverse events), and cost (measured by total charges). Taken together, our findings suggest that allowing providers to use their discretion in order to deviate from routine assignments is beneficial for task types (patients’ diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we find that routines should be enforced: deviations are either detrimental or yield no tangible benefits. Our findings also establish a no free lunch theorem: while for some task types deviations are beneficial regarding some performance measures, they simultaneously degrade performance in terms of other dimensions. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which discretionary deviations are invoked more in practice.

Multivariate Monitoring Workflow for Formulation, Fill and Finish Processes.

Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV).

An efficient task offloading scheme in vehicular edge computing

Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle’s stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes.

Comparison of Modeling Methods for DoE-Based Holistic Upstream Process Characterization.

Upstream bioprocess characterization and optimization are time and resource-intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time-resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed-batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data-driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time-resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black-box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time-resolved trajectories. This showcases the high potential of hybrid models for soft-sensing and model predictive control.

Evaluation of a new visualization and analytics solution for slow control data for large scale experiments

Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.

Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization

Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.

COMPUTATIONAL OFFLOADING FOR PERFORMANCE IMPROVEMENT AND ENERGY SAVING IN MOBILE DEVICES

The mobiles devices such as the smart phones and the wearables take a vital role in our daily life scenario as they are been used as alternative for many devices apart from communication. The mobile devices that are controlled in terms of dimensions and weightiness to make them easy and flexible for handling in turn limits the computational energy, storage and the lifetime of the battery. So this entails the need for the external device to support the mobile devices by providing a computational power, storage and energy, this is known as the computational offloading. So the paper puts forth the mobile cloud services as an external platform to offload the resource intensive computation tasks of the mobile devices to enhance the performance of the mobile devices in terms of storage, energy consumption and battery life. The performance evaluation of the mobile cloud based offloading in the mobile devices proves the efficiency of the proffered method in terms of storage, energy and battery lifetime.

Multivessel Computation Offloading in Maritime Mobile Edge Computing Network

With the development of the maritime networks, the data of vessel users is growing exponentially, and more and more resource intensive tasks, such as multimedia applications, high-definition video playback and games, appear in the daily demands. These changes have greatly increased the energy consumption and bandwidth requirements of vessel terminals and networks. In order to meet the requirements of high bandwidth and low delay for the high-speed development of mobile network, and reduce the network load, the concept of mobile edge computing (MEC) is proposed and has been widely supported by the academia and industry. It is considered to be one of the key technologies of the next generation networks. Inspired by this idea, this paper introduces computing offloading technology to maritime mobile cloud networks. Maritime mobile cloud network is the product of the continuous development of cloud computing technology and mobile Internet technology. In this paper, we studied the issue of computation task offloading for vessel terminals, focusing on minimizing the energy consumption of vessel terminals and the execution delay of computation task. First, it determines that whether if it should be offloaded to the cloud server. Second, the server should be selected to run the computation task. The goal of the optimization is to minimize the energy consumption of vessel terminals and the execution delay of computation task taking into account of different weights. To reduce the execution latency and device energy consumption, we proposed a multivessel computation offloading algorithm based on improved Hungarian algorithm in maritime MEC network. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape.

Large-scale single-cell RNA sequencing (scRNA-seq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a dataset using a small subset of cells, which we refer to as a geometric sketch. Oursketches provide more comprehensive visualization of transcriptional diversity, capture rare cell typeswith high sensitivity, and reveal biological cell typesvia clustering. Our sketch of umbilical cord bloodcells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated. The construction of our sketches is extremely fast,which enabled us to accelerate other crucial resource-intensive tasks, such as scRNA-seq data integration, while maintaining accuracy. We anticipate our algorithm will become an increasingly essential step when sharing and analyzing the rapidly growing volume of scRNA-seq data and help enable the democratization of single-cell omics.

Sniff‐Phish: A novel framework for resource intensive computation in cloud to detect email scam

AbstractToday, the growing significance of digitization across the globe has made the cybersecurity as inevitable. There are many threats in existence to induce harm and to make illegal activities in a device or a network. Email scam or phishing is a technique that most of the hackers use as baits to infiltrate into a system. After a systematic analysis of the infiltration process, a Chrome extension application named Sniff‐Phish is developed, where the resource intensive computational tasks are performed in cloud with which we could be able to detect various categories of real‐time email scams or phishing attacks like zero‐day and spear phishing. This extension application is based upon analyzing some basic criterion of the malicious URL and duly presenting the legitimacy level of the link in the form of a report. The accuracy obtains for Sniff‐Phish is 98% and covers a wide range of phishing websites, resulting in the less false positive rate of 1.2%. Considering the zero‐day and spear phishing detection rate of Sniff‐Phish, the overall experimental results prove that the proposed system outperforms the conventional phishing detection methods.

Resource identification in fog-to-cloud systems: toward an identity management strategy

Fog-to-Cloud (F2C) is a novel paradigm aiming at extending the cloud computing capabilities to the edge of the network through the hierarchical and coordinated management of both, centralized cloud datacenters and distributed fog resources. It will allow all kinds of devices that are capable to connect to the F2C network to share its idle resources and access both, service provider and third parties’ resources to expand its own capabilities. However, despite the numerous advantages offered by the F2C model, such as the possibility of offloading delay-sensitive tasks to a nearby device and using the cloud infrastructure in the execution of resource-intensive tasks, the list of open challenges that needs to be addressed to have a deployable F2C system is pretty long. In this paper we focus on the resource identification challenge, proposing an identity management system (IDMS) solution that starts assigning identifiers (IDs) to the devices in the F2C network in a decentralized fashion using hashes and afterwards, manages the usage of those IDs applying a fragmentation technique. The obtained results during the validation phase show that our proposal not only meets the desired IDMS characteristics, but also that the fragmentation strategy is aligned with the constrained nature of the devices in the lowest tier of the network hierarchy.

Who Should See the Patient? On Discretionary Patient-Provider Assignments in Hospitals

In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilize it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a “no free lunch” theorem: while for some task types deviations are beneficial regarding some performance measures, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.

Time-sensitive resource re-allocation strategy for interdependent continuous tasks

Abstract An increase in volumes of data and a shift towards live data enabled a stronger focus on resource-intensive tasks which run continuously over long periods. A Grid has potential to offer the required resources for these tasks, while considering a fair and balanced allocation of resources among multiple client agents. Taking this into account, a Grid might be unwilling to allocate its resources for long time, leading to task interruptions. This problem becomes even more serious if an interruption of one task may lead to the interruption of dependent tasks. Here, we discuss a new strategy for resource re-allocation which is utilized by a client with the aim to prevent too long interruptions by re-allocating resources between its own tasks. Those re-allocations are suggested by a client agent, but only a Grid can re-allocate resources if agreed. Our strategy was tested under the different Grid settings, accounting for the adjusted coefficients, and demonstrated noticeable improvements in client utilities as compared to when it is not considered. Our experiment was also extended to tests with environmental modelling and realistic Grid resource simulation, grounded in real-life Grid studies. These tests have also shown a useful application of our strategy.

Healthcare Analytics with R and MongoDB using Social Media

The recent advent of various social media platforms has opened new avenues of data collection and analysis. The unstructured social media data needs proper data classification for efficient healthcare analytics. R has latency and the latency is induced by need to 'load' 'offline' data files. This paper introduces a framework to create a model to offset the processing of streaming data in R. This paper demonstrates how the framework can work by analysing the frequency of the international classification of diseases (ICD-10) keywords as a part of healthcare analytics. The proposed framework offsets the work of resource intensive analytics tasks like dynamic querying, summation, aggregation, map-reduce by performing these at the NoSQL data store and R can use these pre-computed results to perform subsequent analytics. This paper also illustrates how efficiency can be achieved in processing streamed data in R by comparing processing times with and without use of the proposed model.

Joint Computation Offloading and Resource Allocation in C-RAN With MEC Based on Spectrum Efficiency

The cloud radio access network (C-RAN) with mobile edge computing (MEC) structure which consists of a baseband unit (BBU) pool integrating with an MEC server and several remote radio heads (RRHs) beside the mobile terminals can help users with computational resource-intensive tasks and bring extra profits to network operator at the same time. This paper presents a novel task-aware C-RAN with MEC structure and formulates a profit maximization problem by jointly optimizing offloading strategy, radio and computational resources allocation under the constraints of offloading latency, fronthaul capacity along with limited bandwidth and computational resource. To solve this NP-hard optimization problem in a distributed and efficient way, we propose a spectrum efficiency (SE)-based joint optimization for offloading and resource allocation (SJOORA) scheme which decomposes the original problem into two sub-problems. A SE-based offloading strategy is proposed with confirmed resource allocation, and on the other hand, bandwidth and computational resource allocation problem is solved by using a Lagrangian multiplier method with predetermined offloading strategy. Finally, by solving these two sub-problems iteratively, a suboptimal solution is obtained for the original problem. The simulation results show that the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.

Task scheduling and resource allocation in cloud computing using a heuristic approach

Cloud computing is required by modern technology. Task scheduling and resource allocation are important aspects of cloud computing. This paper proposes a heuristic approach that combines the modified analytic hierarchy process (MAHP), bandwidth aware divisible scheduling (BATS) + BAR optimization, longest expected processing time preemption (LEPT), and divide-and-conquer methods to perform task scheduling and resource allocation. In this approach, each task is processed before its actual allocation to cloud resources using a MAHP process. The resources are allocated using the combined BATS + BAR optimization method, which considers the bandwidth and load of the cloud resources as constraints. In addition, the proposed system preempts resource intensive tasks using LEPT preemption. The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics.

An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres

Cloud datacentre resources and the arriving jobs are addressed to be exhibiting increased level of heterogeneity. A single Cloud job may encompass one to several number of tasks, such tasks usually exhibit increased level of behavioural heterogeneity though they belong to the same job. Such behavioural heterogeneity are usually evident among the level of resource consumption, resource intensiveness, task duration etc. These task behavioural heterogeneity within jobs impose various complications in achieving an effective energy efficient management of the Cloud jobs whilst processing them in the server resources. To this end, this paper investigates the impacts of the task level behavioural heterogeneity upon energy efficiency whilst the tasks within given jobs are executed in Cloud datacentres. Real-life Cloud trace logs have been investigated to exhibit the impacts of task heterogeneity from three different perspectives including the task execution trend and task termination pattern, the presence of few proportions of resource intensive and long running tasks within jobs. Furthermore, the energy implications of such straggling tasks within jobs have been empirically exhibited. Analysis conducted in this study demonstrates that Cloud jobs are extremely heterogeneous and tasks behave distinctly under different execution instances, and the presence of energy-aware long tail stragglers within jobs can significantly incur extravagant level of energy expenditures.

SIM-Cumulus: An Academic Cloud for the Provisioning of Network-Simulation-as-a-Service (NSaaS)

Large-scale network simulations are resource and time intensive tasks due to a number of factors i.e., setup configuration, computation time, hardware, and energy cost. These factors ultimately force network researchers to scale-down the scope of experiments, either in terms of simulation entities involved or in abridging expected micro-level details. The Cloud technology facilitates researchers to address mentioned factors by the provisioning of pre-configured instances on shared infrastructure. In this paper, an academic Cloud architecture SIM-Cumulus targeting the research institutions is proposed. SIM-Cumulus provides the framework of virtual machine instances specifically configured for large-scale network simulations, with the aim of efficiency in terms of simulation execution time and energy cost. The performance of SIM-Cumulus is evaluated using large-scale wireless network simulations. Simulation results show that SIM-Cumulus is beneficial in three aspects i.e., 1) promotion of research within the domain of computer networks; 2) consumption of considerably fewer resources in terms of simulation elapsed time and usage cost; and 3) reduction of carbon emission leading toward sustainable IT development.

Гибридные вычислительные кластеры для изучения структуры, функции и регуляции белков

Суплатов Дмитрий Андреевич, к.х.н., с.н.с. НИИ физико-химической биологии имени А.Н. Белозерского, Московский государственный университет имени М.В. Ломоносова (Москва, Российская Федерация). Попова Нина Николаевна, к.ф-м.н., доцент Факультета вычислительной математики и кибернетики, Московский государственный университет имени М.В. Ломоносова (Москва, Российская Федерация). Копылов Кирилл Евгеньевич, студент Химического факультета, Московский государственный университет имени М.В. Ломоносова (Москва, Российская Федерация). Шегай Максим Викторович, аспирант Факультета вычислительной математики и кибернетики, Московский государственный университет имени М.В. Ломоносова (Москва, Российская Федерация). Воеводин Владимир Валентинович, чл.-корр. РАН, д.ф.-м.н., зав. кафедрой супер- компьютеров и квантовой информатики Факультета вычислительной математики и кибернетики, зам. директора Научно-исследовательского вычислительного центра, Московский государственный университет имени М.В. Ломоносова (Москва, Российская Федерация). Швядас Витас Каятоно, д.х.н., профессор Факультета биоинженерии и биоинформатики и НИИ физико-химической биологии имени А.Н. Белозерского, Московский государственный университет имени М.В. Ломоносова (Москва, Российская Федерация). D.A. Suplatov, N.N. Popova, K.E. Kopylov, M.V. Shegay, Vl.V. Voevodin, V.K. Svedas Lomonosov Moscow State University (GSP-1, Leninskie Gory 1, Moscow, 119991 Russia) E-mail: d.a.suplatov@belozersky.msu.ru, popova@cs.msu.su, kopylov@mail.chem.msu.ru, max.shegai@gmail.com, voevodin@parallel.ru, vytas@belozersky.msu.ru

Community‐Driven Hypothesis Testing: A Solution for the Tragedy of the Anticommons

Shared ownership of property and resources is a longstanding challenge throughout history that has been amplifying with the increasing development of industrial and postindustrial societies. Where governments, project planners, and commercial developers seek to develop new infrastructure, industrial projects, and various other land-and resource-intensive tasks, veto power shared by various local stakeholders can complicate or halt progress. Risk communication has been used as an attempt to address stakeholder concerns in these contexts, but has demonstrated shortcomings. These coordination failures between project planners and stakeholders can be described as a specific kind of social dilemma that we describe as the "tragedy of the anticommons." To overcome such dilemmas, we demonstrate how a two-step process can directly address public mistrust of project planners and public perceptions of limited decision-making authority. This approach is examined via two separate empirical field experiments in Portugal and Tunisia, where public resistance and anticommons problems threatened to derail emerging industrial projects. In both applications, an intervention is undertaken to address initial public resistance to such projects, where specific public stakeholders and project sponsors collectively engaged in a hypothesis-testing process to identify and assess human and environmental health risks associated with proposed industrial facilities. These field experiments indicate that a rigorous attempt to address public mistrust and perceptions of power imbalances and change the pay-off structure of the given dilemma may help overcome such anticommons problems in specific cases, and may potentially generate enthusiasm and support for such projects by local publics moving forward.