Real-time Jobs Research Articles

A Hybrid Scheduling Framework for Mixed Real-Time Tasks in an Automotive System With Vehicular Network

As vehicles integrate more and more autonomous driving functionalities, it becomes more and more important to use vehicular networks to fully guarantee the safety and real-time performance of on-board computing tasks. Current studies on vehicular networks pay much attention to the performance improvement of network communication and resource allocation, while ignoring the fact that automotive on-board computing tasks play a significant role in vehicle safety and need to be elegantly handled in vehicular networks. In this paper, we propose a hybrid scheduling framework for meeting all hard real-time task deadlines while minimizing soft real-time task deadline misses. In particular, the proposed scheduler is composed of some local schedulers and a global scheduler, where the former guarantees the schedulability of all hard real-time tasks, and the latter decides the assignment of soft real-time jobs dynamically online. Depending on the remaining processing capability of a vehicular network, arrival jobs are either assigned for further processing or discarded. An approach combining the utilization-based schedulability test and demand-supply analysis is proposed to effectively assign tasks to processors offline. To meet as many soft real-time task deadlines as possible, tasks' demand and supply bounds are computed at runtime, such that online execution information can be used to compensate for the scheduling loss with the worst-case assumption made by the offline test. Experimental results demonstrate that, compared to a genetic algorithm, our proposed approach needs far less computation to assign more tasks offline. The online scheduling also saves many soft real-time jobs that were to be dropped by the offline algorithm.

Reading Dysfluency in Indian Classrooms: An Insight

Reading text in English is an important skill for students of higher education in India, as their understanding of their core subjects in specific and the world of information in general is based upon this skill. The ability to read English fluently, increases their job prospects also, as real time job environments use English as the official medium of communication and require that students read English with ease. Technical students who engage primarily with numerical data, diagrams and other non-textual content in their core subjects, have an especially hard time coping with tasks which involve reading textual English. There are several problems that lead to lack of fluency and thus lack of understanding of the content. This paper will study reading “ dysfluency “ problems technical students face, when reading English like word recognition difficulties, inability to read in sense groups, problems to do with accuracy, automaticity and expression. This paper will attempt to offer strategies to overcome this problem like Loud reading, Echo reading and Choral reading and some unconventional reading practices that can help solve this very common but important skill gap.

Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

High-performing, real-time pose detection and tracking in real-time will enable computers to develop a finer-grained and more natural understanding of human behavior. However, the implementation of real-time human pose estimation remains a challenge. On the one hand, the performance of semantic keypoint tracking in live video footage requires high computational resources and large parameters, which limiting the accuracy of pose estimation. On the other hand, some transformer-based models were proposed recently with outstanding performance and much fewer parameters and FLOPs. However, the self-attention module in the transformer is not computationally friendly, which makes it difficult to apply these excellent models to real-time jobs. To overcome the above problems, we propose a transformer-like model, named ShiftPose, which is regression-based approach. The ShiftPose does not contain any self-attention module. Instead, we replace the self-attention module with a non-parameter operation called the shift operator. Meanwhile, we adapt the bridge–branch connection, instead of a fully-branched connection, such as HRNet, as our multi-resolution integration scheme. Specifically, the bottom half of our model adds the previous output, as well as the output from the top half of our model, corresponding to its resolution. Finally, the simple, yet promising, disentangled representation (SimDR) was used in our study to make the training process more stable. The experimental results on the MPII datasets were 86.4 PCKH, 29.1PCKH@0.1. On the COCO dataset, the results were 72.2 mAP and 91.5 AP50, 255 fps on GPU, with 10.2M parameters, and 1.6 GFLOPs. In addition, we tested our model for single-stage 3D human pose estimation and draw several useful and exploratory conclusions. The above results show good performance, and this paper provides a new method for high-performance, real-time attitude detection and tracking.

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.

Cost-aware real-time job scheduling for hybrid cloud using deep reinforcement learning

Cost-aware real-time job scheduling for hybrid cloud using deep reinforcement learning

The impacts of COVID-19 on clean energy labor markets: Evidence from multifaceted analysis of public health interventions and COVID-health factors

COVID-19 pandemic has affected clean energy labor market. Using real-time job vacancy data, this study analyzes the impacts of the pandemic on the U.S. clean energy labor market in 2020, including biomass, energy efficiency (EE), electric vehicle (EV), power/microgrid, solar, and wind industries. This study identifies how COVID-health factors and public health interventions influence clean energy job availability during the early COVID pandemic. Overall, California had the most energy jobs and experienced a significant decrease in April 2020. EV and solar had the highest percentages of job vacancies during the pandemic in general. Still, lockdowns had the most severe influence on EE and wind jobs. Stay-at-home orders negatively affected clean energy job vacancies in biomass, EV, power/microgrid, and wind. Social-gathering restrictions, however, did not have much influence. Increased COVID tests at the state level had the strongest and most positive influence on clean energy job postings, indicating the importance of a state's ability to manage public health infrastructure or crisis issues. COVID hospitalizations negatively influenced the job vacancies in biomass and wind but did not affect the other four sectors; conversely, as COVID death numbers increased, the number of jobs in biomass, EV, power grid, solar, and wind decreased, but not in EE jobs.

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

The computing in Real-time is rapidly focusing much developments in technologies so that the real-time jobs are to be scheduled and executed on computing systems in particular time frame. The scheduling and load balancing techniques in distributed systems face numerous challenges because of lack of centralized strategy to dispatch the jobs in multiprocessors systems. In this work, we propose an Algorithm Fuzzy Scheduling (AFS) for real-time jobs that includes of Arrival time, Deadline and Computation time as the scheduling parameters of input. The approach AFS is analyzed and compared with Existing Fuzzy Algorithm (EFA) model for evaluation of performances from the outcome of the simulation. The jobs are scheduled on multiprocessor at higher system load by making use of fuzzy mechanisms in the algorithms. The experimental results prove that the proposed AFS achieves a better performance comparatively to EFA at various system load factors with respect to mean turnaroundtime, mean response time and count of missed deadlines. This is the initial phase of the algorithm, that will be enhanced to consider a greater number of parameters to be associated with jobs for better decision making and to investigate the scope for algorithm level parallelism.

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

The job-shop scheduling problem (JSSP) is a complex combinatorial problem, especially in dynamic environments. Low-volume-high-mix orders contain various design specifications that bring a large number of uncertainties to manufacturing systems. Traditional scheduling methods are limited in handling diverse manufacturing resources in a dynamic environment. In recent years, artificial intelligence (AI) arouses the interests of researchers in solving dynamic scheduling problems. However, it is difficult to optimize the scheduling policies for online decision making while considering multiple objectives. Therefore, this paper proposes a smart scheduler to handle real-time jobs and unexpected events in smart manufacturing factories. New composite reward functions are formulated to improve the decision-making abilities and learning efficiency of the smart scheduler. Based on deep reinforcement learning (RL), the smart scheduler autonomously learns to schedule manufacturing resources in real time and improve its decision-making abilities dynamically. We evaluate and validate the proposed scheduling model with a series of experiments on a smart factory testbed. Experimental results show that the smart scheduler not only achieves good learning and scheduling performances by optimizing the composite reward functions, but also copes with unexpected events (e.g. urgent or simultaneous orders, machine failures) and balances between efficiency and profits.

Energy-aware systems for real-time job scheduling in cloud data centers: A deep reinforcement learning approach

With the advantages such as high-performance, low-maintenance, and reliability, more and more companies are moving their computing infrastructures to the cloud. In the meantime, with the increasing number of users continuously submitting jobs to cloud, energy consumed by the current cloud data centers has become a major concern for cloud service providers, due to financial and environmental reasons. In this paper, we propose a deep reinforcement learning (DRL) approach to handle real-time jobs. Specifically, we focus on allocating incoming jobs to appropriate virtual machines (VMs) in a way that energy consumption can be optimized while high quality of service (QoS) can be achieved. We give the detailed design and implementation of our approach, and our experimental results demonstrate that the proposed method can achieve better performance in job success rate and average response time with less energy consumption than the current approaches, in the presence of different real-time cloud workloads.

Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

A Comprehensive Assessment of Cultivation Environment of Top Innovative High-Level Talents Based on Deep Learning Algorithm.

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real-time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top-level talents, will become more and more crucial. Growing in importance is China's economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation.

Job Shop Scheduling: A Novel DRL approach for continuous schedule-generation facing real-time job arrivals

We present a DRL-based novel approach to solve the Job Shop Scheduling Problem (JSSP) in real-time while facing unpredictable job arrival disruptions. Our proposed approach consists of continuously generating improved schedules based on a rescheduling technique: It leads to continuous generation of schedules in triggered rescheduling points, and thus gives immediate response to random job arrivals. To implement the proposed technique, we use Proximal Policy Optimization Actor and Critic (PPO-AC), a combination of two RL algorithms. PPO-AC is used to assign job operations to available machines based on the job shop state that is represented by dynamic disjunctive graphs, modeling precedence constraints between job operations, and resource sharing constraints. Graph Embedding modeling is also applied for dynamic graph representation in PPO-AC algorithm. Preliminary numerical experiments of our innovative solution are discussed in this paper.

Using action research to develop a real-time measure of job satisfaction in the operating room setting

This paper outlines how action research was used to develop a real-time job satisfaction measurement tool for the operating room (OR) setting. It offers insight into how collaborative action research can be used in a complex interprofessional setting to create a practical, valid and relevant tool. The study was conducted within one New Zealand hospital OR department during the period of March 2018 – June 2019. Using action research cycles, researchers and hospital personnel collaboratively created an innovative one-minute daily job satisfaction measure called the Morale-o-Meter. Complexities relating to its development and acceptability are explored and reflected upon, in order to draw insight for other researchers who are looking to use this methodology in a similar setting.

Energy-aware adaptive offloading of soft real-time jobs in mobile edge clouds

We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

Real-time order acceptance and scheduling for data-enabled permutation flow shops: Bilevel interactive optimization with nonlinear integer programming

With the fourth-generation industrial revolution, manufacturing industries are focusing on dynamic, fully autonomous, and more customer-oriented production systems. This customer-oriented change converts classically static customer demand into that which is dynamic and real-time, as no prior information regarding customer demand is known in advance. This paper focuses on real-time order acceptance and scheduling (r-OAS) for a data-enabled permutation flow shop. To compensate for the shortage in prevailing approaches that make bottleneck-based decisions or assume that the intermediate buffers among workstations are infinite, an r-OAS scheme is generated based on a data-driven representation, which can concisely predict the dynamic production status of flow shops and the corresponding makespan of a job with finite intermediate buffer constraints. Using this representation, real-time job release planning (r-JRP) can be coupled with r-OAS to minimize various operational costs of flow shops (i.e., the costs of the work-in-process, earliness, and tardiness). In terms of the inherent interactive mechanism between r-OAS and r-JRP, in which r-OAS generates a decision space for r-JRP and r-JRP then feeds the lowest operational costs back for use in r-OAS decision-making, a bilevel interactive optimization (BIO) is formulated to simultaneously address the two subproblems based on the Stackelberg game. The r-OAS acts as the leader, while r-JRP acts as the follower. The BIO is a type of nonlinear integer programming, and a bilevel tabu-enumeration heuristic algorithm is developed to solve it. The efficiency of the BIO is verified through a practical case study. The results show that the BIO can increase the net revenue of flow shops by 2.97%, compared to the bottleneck-based approach, and by 2.45% and 0.92%, respectively, compared to step-by-step methodologies.

Understanding Real-Time Job Signatures on Deepwater Cementing Jobs with Dynamic Losses

Summary Two of the most important parameters to monitor during a primary cementing job are the flow rate in and return flow rate measurements. To achieve optimum job results of a primary cementing job, measuring annular return rates and comparing them with simulated data in real time will provide a better understanding of job signatures and result in the best possible top of cement (TOC) estimation prior to running any cement evaluation log or making a decision to continue drilling the next section of the well. The return rate job signature along with the wellhead pressure is essential to understanding the behavior and discrepancies between simulated and acquired surface data. Therefore, to assess the risk of job issues, such as unsuspected washout and lost circulation among others, accurate measurements of the return rate are critical. Historically, the cement job evaluation has been limited by the fact that most drilling rigs do not have an accurate flowmeter installed on the annulus return line, and a simple verification of mud tanks volume vs. pumped volume, as reported by drillers or mud loggers, more often than not results in an unreliable assessment of the volume lost downhole, due to the unfamiliarity with the U-tubing effect and lack of data consolidation from the cement unit (flow rate in) and the rig (flow rate in and flow rate out). In this paper, we will review a solution developed to mitigate the lack of a direct flow-rate measurement by computing and displaying the return rate using either a paddle meter measurement or the derivative over time of the volume observed in the rig tanks.

Resilience-Enhancing Solution to Mitigate Risk for Sustainable Supply Chain—An Empirical Study of Elevator Manufacturing

As the complexity of supply chains increases, the enhancement of resilience for mitigating sustainable disruption risks in supply chains is an important issue. Quality function deployment (QFD) has been successfully applied in many domains to solve multicriteria decision-making (MCDM) problems. However, research on developing two houses of quality to connect sustainable supply chain disruption risks, resilience capacities, and resilience-enhancing features in elevator manufacturing supply chains by using the MCDM approach is lacking. This study aims to develop a framework for exploring useful decision-making by integrating the MCDM approach and QFD. By applying the framework, supply chain resilience can be improved by identifying the major sustainable risks and the key resilience to mitigate these risks. Important managerial insights and practical implications are obtained from the framework implementation in a case study of the elevator manufacturing industry. To strengthen resilience and thus mitigate key risks, the most urgent tasks are to connect the working site and the backstage to enhance product development and design and to share real-time job information. When these features are strengthened, agility, capacity, and visibility can be improved. Finally, unexpected events lead to changes in supplier delivery dates, and factors such as typhoon and lack of critical capacities/skilled employees with the greatest impact can be alleviated. This framework will provide an effective and pragmatic approach for constructing sustainable supply chain risk resilience in the elevator manufacturing industry.

Emergency Preparedness in Dietetics during a Pandemic: Lessons Learned from an ICU Dietitian during Covid-19

Purpose: The incidence, prevalence, and chaos of the Covid-19 disease sequelae is an adverse event akin to a natural disaster or wartime creating a high degree of uncertainty and vulnerability for health care providers. This is an account of a Registered Dietitian Nutritionist’s (RDN) experience on the frontlines during an epidemic providing care to patients with Covid-19. The purpose of the series of discussions was to create a live account of a frontline RDN’s experience during the pandemic to develop recommendations for emergency preparedness in dietetics during Covid-19. Methods: Since the Covid-19 pandemic is causing significant hardship, loss of life, and changes in health care provider roles, particularly in the intensive care unit (ICU), we conducted a real-time job analysis of an ICU RDN in New York during the height of the pandemic in that region. Critical Incident Technique (CIT) has been gradually refined and is used for curriculum development, performance evaluation, and for the creation of professional standards in health professions jobs. To gain an in-depth understanding of the job of an ICU RDN on the frontlines during Covid-19, CIT was used an information-gathering strategy in a series of 7, hour-long meetings via Zoom, an online meeting platform. These meetings occurred during the apex of the Covid-19 pandemic in NYC from March 2020 - June 2020. Results: Covid-19 resulted in RDN decision-making that involved circumstances with a high degree of variability and uncertainty. There were several mediators that enabled the RDN to be resilient and adapt to the adversity of Covid-19 in this hospital. Conclusions: The RDN’s account of her experience during Covid-19 resulted in several recommendations for ICU emergency preparedness. This account also elucidates the trauma and resilience experienced by a health care provider during Covid-19.

A pseudo-linear time algorithm for the optimal discrete speed minimizing energy consumption

We consider the classical problem of minimizing off-line the total energy consumption required to execute a set of n real-time jobs on a single processor with a finite number of available speeds. Each real-time job is defined by its release time, size, and deadline (all bounded integers). The goal is to find a processor speed schedule, such that no job misses its deadline and the energy consumption is minimal. We propose a pseudo-linear time algorithm that checks the schedulability of the given set of n jobs and computes an optimal speed schedule. The time complexity of our algorithm is in ${\mathcal {O}}(n)$ , to be compared with ${\mathcal {O}}(n\log (n))$ for the best known solution. Besides the complexity gain, the main interest of our algorithm is that it is based on a completely different idea: instead of computing the critical intervals, it sweeps the set of jobs and uses a dynamic programming approach to compute an optimal speed schedule. Our linear time algorithm is still valid (with some changes) with arbitrary (non-convex) power functions and when switching costs are taken into account.

Metaheuristički algoritmi za rešavanje fleksibilnog problema raspoređivanje radnih mesta

The problem with flexible job planning (FJSP) is a modification o f the classic job booking problem. This paper deals with the problem o f flexible job deployment and processing o f operations on one machine from a set o f alternative machines. The problem o f deploying flexible jobs in real time is one o f a group o f difficult NP problems (Non-deterministic polynomial time). The motive o f this paper is to show the application o f meta-heuristic algorithms on the example of flexible job schedules and present the appropriate method to future studies. To solve this problem, two meta-heuristic algorithms were used: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The aim o f the paper is to achieve the speed o f the conversion solution in a series o f iterations, minimizing the total time o f deployment of jobs on an alternative set of machines, as well as minimizing the total schedule time. The problem o f deploying flexible jobs has great application, and with this test we propose an algorithm for solving this problem.