Stringent Deadlines Research Articles

Hubungan Antara Stres Kerja dan Keluhan Nyeri Punggung Bawah Pekerja Las Industri Pembuatan Kapal

Low Back Pain (LBP) is a widespread health issue that profoundly impacts workers' productivity and quality of life. In addition to non-specific health effects, LBP can result in long-term disability and considerable economic losses. Globally, LBP affects 619 million individuals, with projections suggesting an increase to 843 million by 2050, particularly Low Middle Income Countries (LMICs) in Asia and Africa. The shipbuilding industry, known for its physically strenuous tasks and stringent deadlines, presents unique ergonomic challenges that can exacerbate job-related stress and precipitate the onset of LBP. This study examines Project X PT Y, a pivotal entity in Indonesia's shipbuilding sector. Utilizing a quantitative methodology and a cross-sectional research design, the study encompasses all 26 workers from the welding workshop at Project X PT Y. The analysis, conducted using the Spearman test, reveals a robust monotonic relationship between work-related stress and LBP complaints, indicating that the incidence of LBP increases concomitantly with elevated work stress.

Goal progress velocity as a determinant of shortcut behaviors.

Employees often have a great deal of work to accomplish within stringent deadlines. Therefore, employees may engage in shortcut behaviors, which involve eschewing standard procedures during goal pursuit to save time. However, shortcuts can lead to negative consequences such as poor-quality work, accidents, and even large-scale disasters. Despite these implications, few studies have investigated the antecedents of shortcut behaviors. In this research, we propose that employees engage in shortcut behaviors to regulate their velocity (i.e., rate of progress). Specifically, we predict that when individuals experience slower-than-referent velocity, they will (a) believe that the goal is unlikely to be met via standard procedures and (b) experience feelings of frustration. In turn, we expect these psychological states to be related to the perceived utility of shortcuts, especially when shortcuts are perceived as viable means to achieve the goal. Finally, we predict that the perceived utility of shortcuts will be positively related to actual shortcut behaviors. We tested these predictions using a laboratory experiment in which we manipulated velocity and unobtrusively observed shortcuts (Study 1, N = 147), as well as a daily diary study in which employees reported their velocity and shortcut behaviors over 5 consecutive workdays (Study 2, N = 395). Both studies provided support for our predictions. In sum, this research provides evidence to suggest that the experience of slow progress can lead to shortcuts not only by casting doubt on employees' perceived likelihood of meeting the goal but also by producing feelings of frustration. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach

With the expansion of the internet of things (IoT) devices and their applications, the demand for executing complex and deadline-aware tasks is growing rapidly. Fog-enabled IoT architecture has evolved to accomplish these tasks at the fog layer. However, fog computing devices have limited power supply and computation resources compared to cloud devices. In delay-sensitive applications of fog-enabled IoT architecture, executing tasks with stringent deadlines while reducing the service latency and energy usage of fog resources is a difficult challenge. This paper presents an effective task scheduling strategy to allocate fog computing resources for IoT requests to meet the deadline of the requests and resource availability. Initially, the scheduling problem is formulated as mixed-integer nonlinear programming (MINLP) to reduce the energy consumption of the fog resources and service time of the tasks subject to the deadline and resource availability constraints. To address the high dimensionality issue of the tasks in a dynamic environment, a fuzzy-based reinforcement learning (FRL) mechanism is employed to reduce the service delay of the tasks and energy usage of the fog nodes. Initially, the tasks are prioritized using fuzzy logic. Then the prioritized tasks are scheduled using the on-policy reinforcement learning technique, which enhances the long-term reward compared to the Q-learning approach. Further, the evaluation outcomes reflect that the proposed task scheduling technique outperforms the existing algorithms with an improvement of up to 23% and 18% regarding service latency and energy consumption, respectively.

Joint task offloading and resource optimization in NOMA-based vehicular edge computing: A game-theoretic DRL approach

Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. This work presents a non-orthogonal multiple access (NOMA) based architecture in VEC, where heterogeneous edge nodes are cooperated for real-time task processing. We derive a vehicle-to-infrastructure (V2I) transmission model by considering both intra-edge and inter-edge interferences and formulate a cooperative resource optimization (CRO) problem by jointly optimizing the task offloading and resource allocation, aiming at maximizing the service ratio. Further, we decompose the CRO into two subproblems, namely, task offloading and resource allocation. In particular, the task offloading subproblem is modeled as an exact potential game (EPG), and a multi-agent distributed distributional deep deterministic policy gradient (MAD4PG) is proposed to achieve the Nash equilibrium. The resource allocation subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed by using a gradient-based iterative method and KKT condition. Finally, we build the simulation model based on real-world vehicular trajectories and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solutions.

Joint VNF Placement and Scheduling for Latency-Sensitive Services

Next-generation 6G networks are envisioned to be a key enabler for low-latency services (e.g., extended reality, remote surgery), which cannot be potentially realized by currently deployed networks. Network function virtualization (NFV) and software-defined networking (SDN) are going to continue playing their key role as two promising technologies in 6G to realize these services due to flexibility, agility, scalability, and cost-efficiency. Although NFV and SDN bring several benefits, provisioning latency-sensitive network services (NSs) in an NFV-based infrastructure remains a challenge, as they require stringent service deadlines. To efficiently meet such stringent service deadlines, VNF placement and scheduling need to be carried out jointly. Most of the existing studies tackle these two problems separately. In this paper, we study the joint VNF placement and scheduling problem for latency-sensitive NSs. We aim at optimally determining whether to place new VNFs or to reuse the already deployed VNFs to optimize profits while guaranteeing stringent deadlines. To solve the problem, we formulate it as an integer linear programming (ILP) problem. Due to its complexity, we also propose two efficient heuristics, namely, greedy-based and Tabu search-based algorithms to solve the problem. The simulation results show that our proposed algorithms achieve higher profits than the existing benchmarks.

Improving the Schedulability of Real-Time Tasks Using Fog Computing

Due to the significant communication delay to user tasks, the cloud is not ideal for executing real-time tasks with stringent deadlines. Fog computing consists of low computation capability fog nodes, or cloudlets located in proximity to the source of the data generation: the users. These cloudlets are ideal for executing tasks that have early deadlines. In this paper, we propose algorithms that schedule a set of real-time tasks on such an embedded-fog-cloud architecture. We consider hard, firm and soft tasks. The execution framework consists of embedded, fog and cloud processors. Tasks are scheduled on appropriate processors based on their deadline requirements. In general, hard real-time tasks are executed on embedded processors, firm real-time tasks on fog processors, and soft real-time tasks on cloud processors. We also propose a sufficient schedulability condition. Simulation results from the CERIT trace as well as test-bed results show that the proposed algorithms offer superior performance as compared to algorithms that do not employ fog processors. Employing an <inline-formula><tex-math notation="LaTeX">$Embedded-fog-cloud$</tex-math></inline-formula> architecture offers an improvement of 62.37 percent for real-time Success Ratio (SR) and 35 percent for Average Response Time as compared to scheduling tasks on the <inline-formula><tex-math notation="LaTeX">$cloud$</tex-math></inline-formula> alone.

Scheduling of Low Latency Services in Softwarized Networks

The fifth generation (5G) networks are expected to support diverse business verticals (i.e., manufacturing, health care, etc.) with varying quality of service requirements. While today’s mobile networks are a one size fits all architecture, tomorrow’s 5G mobile networks are envisioned to encourage agility, programmability and elasticity through enabling a software-based architecture promoted by network slicing. Network slicing is a new paradigm consisting of partitioning the underlying network infrastructure into different logical network slices, each dedicated to address the requirements (i.e., ultra-low latency, ultra-reliability, etc.) of a group of services. Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies have been identified as main enablers of network slicing, facilitating the fulfillment of the aforementioned services’ requirements. In this paper, we study the Latency-Aware service scheduling (LASS) problem to solve the network function mapping, the traffic routing and the network service scheduling in the context of an ultra-low latency network slice to consider services with stringent deadlines. We propose the LASS-Game, a novel game-theoretic approach presenting a scalable solution for the LASS problem that accounts for the centralized aspect of the problem while leveraging a decentralized mapping, routing and scheduling decisions.

Multipath transport and packet spraying for efficient data delivery in data centres

Modern data centres provide large aggregate network capacity and multiple paths among servers. Traffic in data centres is very diverse; most of the data is produced by long, bandwidth hungry flows but the large majority of flows, which commonly come with stringent deadlines regarding their completion time, are short. It has been shown that TCP is not efficient for any of these types of traffic in modern data centres. MultiPath TCP (MPTCP) employs multipath data transport and is efficient for long flows but ill-suited for short flows.In this paper, we present Maximum MultiPath TCP (MMPTCP), a novel transport protocol which extends MPTCP and, compared to TCP and MPTCP, reduces short flows’ completion times, while providing excellent goodput to long flows. To do so, MMPTCP runs in two phases; initially, it randomly scatters packets in the network under a single congestion window exploiting all available paths. This is beneficial to latency-sensitive flows. After a specific amount of data is sent, MMPTCP switches to a regular MultiPath TCP mode. MMPTCP is incrementally deployable in existing data centres as it does not require any modifications outside the transport layer and behaves well when competing with MPTCP flows. We also present a topology-specific extension of MMPTCP that adjusts the numbers of subflows during the second phase of the protocol based on knowledge about the location of the receiver in the data centre.We present extensive evaluation that shows that MMPTCP’s design objectives are met. We have implemented MMPTCP (along with MPTCP and packet spraying) in ns-3 and evaluated our protocol in simulated FatTree topologies. We have evaluated how MMPTCP performs compared to TCP and MPTCP and how its performance is affected by transient hotspots in the network. We have also experimented with different thresholds for duplicate acknowledgements and fast retransmissions and shown that MMPTCP performs well when the size of short flows is widely ranged. Finally, we have evaluated how MMPTCP performs under conditions that result in Incast, when different congestion control algorithms are used in its second phase and when varying the overall network load.

Order acceptance and scheduling: overview and complexity results

The order acceptance and scheduling (OAS) problem is an important aspect of the manufacturing process, especially when production is carried out on a made-to-order basis with stringent deadlines and limited production capacity. This paper provides an overview of existing OAS problems by considering due date related dimensions in deterministic scenarios. A three-field notation was introduced for classifying OAS problems, and reviewing solution algorithms and complexity results for the same. This research effort proved the complexity results for existing, open as well as new OAS problems with due dates, and additionally, outlined the complexity results for newly defined OAS problems without due date dimensions.

Order acceptance and scheduling: overview and complexity results

The order acceptance and scheduling (OAS) problem is an important aspect of the manufacturing process, especially when production is carried out on a made-to-order basis with stringent deadlines and limited production capacity. This paper provides an overview of existing OAS problems by considering due date related dimensions in deterministic scenarios. A three-field notation was introduced for classifying OAS problems, and reviewing solution algorithms and complexity results for the same. This research effort proved the complexity results for existing, open as well as new OAS problems with due dates, and additionally, outlined the complexity results for newly defined OAS problems without due date dimensions.

Guest Editorial Special Section on Industrial Communication Technologies and Systems

The eleven papers in this special section focus on industrial communication systems and technologies (ICS). Major requirements of the ICS are real-time support and dependability under all considered load and fault assumptions. The ICS may need to meet stringent deadlines as a basis for the stability in control loops and alarm monitoring. At the same time, high reliability and availability is demanded to avoid down times. In particular, reliability with respect to critical failure modes is of utmost importance for safety-critical applications where failures may result in significant financial losses or risk to humans and the environment. From the point of view of the information and communication technology (ICT), current ICS solutions are based on wired systems to support distributed industrial controls.

A Delay-Bounded MAC Protocol for Mission- and Time-Critical Applications in Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSNs) designed for mission- and time-critical applications require timely and deterministic data delivery within stringent deadline bounds. Exceeding delay limits for such applications can lead to system malfunction or ultimately dangerous situations that can threaten human safety. In this paper, we propose Slot Stealing Medium Access Control (SS-MAC), an efficient SS-MAC protocol to guarantee predictable and timely channel access for time-critical data in IWSNs. In the proposed SS-MAC, aperiodic time-critical traffic opportunistically steals time slots assigned to periodic non-critical traffic. Additionally, a dynamic deadline-based scheduling is introduced to provide guaranteed channel access in emergency and event-based situations, where multiple sensor nodes are triggered simultaneously to transmit time-critical data to the controller. The proposed protocol is evaluated mathematically to provide the worst-case delay bound for the time-critical traffic. Performance comparisons are carried out between the proposed SS-MAC and WirelessHART standard and they show that, for the time-critical traffic, the proposed SS-MAC can achieve, at least, a reduction of almost 30% in the worst-case delay with a significant channel utilization efficiency.

Mixed-criticality federated scheduling for parallel real-time tasks

A mixed-criticality system comprises safety-critical and non-safety-critical tasks sharing a computational platform. Thus, different levels of assurance are required by different tasks in terms of real-time performance. As the computational demands of real-time tasks increase, tasks may require internal parallelism in order to complete within stringent deadlines. In this paper, we consider the problem of mixed-criticality scheduling of parallel real-time tasks and propose a novel mixed-criticality federated scheduling (MCFS) algorithm for parallel tasks modeled by a directed acyclic graph. MCFS is based on federated intuition for scheduling parallel real-time tasks. It strategically assigns cores and virtual deadlines to tasks to achieve good schedulability. For high-utilization tasks (utilization $$\ge $$ 1), we prove that MCFS provides a capacity augmentation bound of $$2+\sqrt{2}$$ and $$(5+\sqrt{5})/2$$ for dual- and multi-criticality, respectively. We show that MCFS has a capacity augmentation bound of $$11m/(3m-3)$$ for dual-criticality systems with both high- and low-utilization tasks. For high-utilization tasks, we further provide a MCFS-Improve algorithm that has the same bound but can admit many more task sets in practice. Results of numerical experiments show that MCFS-Improve significantly improves over MCFS for many different workload settings. We also present an implementation of a MCFS runtime system in Linux that supports parallel programs written in OpenMP. Our implementation provides graceful degradation and recovery features. We conduct empirical experiments to demonstrate the practicality of our MCFS approach.

A Secured Real Time Scheduling Model for Cloud Hypervisor

Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.

Mobile Data Offloading for Streaming-Class Traffic with QoS Guarantee

Faced with the tremendous increase in the amount of data traffic and associated congestion, mobile network operators are moving towards Heterogeneous networks (HetNets), in the process of expanding network capacity. Offloading data traffic onto Wi-Fi in order to avoid congestion in the backbone is an important step in the evolution of HetNets. On-the-spot and delayed offloading have been widely studied in the literature. This paper proposes an offloading algorithm which has low computational complexity. The proposed algorithm offloads data based on a balking function which is dependent on present network condition. Using extensive simulations, the authors demonstrate that the proposed algorithm achieves reduction in mean transmission delay without sacrificing much on the offloading efficiency. This technique is more efficient and applicable to real-time traffic, like live streaming video and audio, which has short and stringent delay requirements or deadlines.

DAWN: Delay-Aware Wi-Fi Offloading and Network Selection

To accommodate the explosive growth in mobile data traffic, both mobile cellular operators and mobile users are increasingly interested in offloading the traffic from cellular networks to Wi-Fi networks. However, previously proposed offloading schemes mainly focus on reducing the cellular data usage, without paying too much attention on the quality of service (QoS) requirements of the applications. In this paper, we study the Wi-Fi offloading problem with delay-tolerant applications under usage-based pricing. We aim to achieve a good tradeoff between the user's payment and its QoS characterized by the file transfer deadline. We first propose a general Delay-Aware Wi-Fi Offloading and Network Selection (DAWN) algorithm for a general single-user decision scenario. We then analytically establish the sufficient conditions, under which the optimal policy exhibits a threshold structure in terms of both the time and file size. As a result, we propose a monotone DAWN algorithm that approximately solves the general offloading problem, and has a much lower computational complexity comparing to the optimal algorithm. Simulation results show that both the general and monotone DAWN schemes achieve a high probability of completing file transfer under a stringent deadline, and require the lowest payment under a non-stringent deadline as compared with three heuristic schemes.

Quality-Driven Cross-Layer Design for H.264/AVC Video Transmission over OFDMA System

Video applications over wireless network have attracted more and more attention in recent years. However, due to the unique characteristics of video, limited available resources and the time-varying wireless channel state, video transmission over wireless network is still a challenging task. In this paper, a novel cross-layer design is proposed to maximize the overall received video quality with the limited network resource and the stringent deadline constraints over OFDMA network. Considering the information extracted from the Application layer, Media Access Control layer and Physical layer, we formulate the packet scheduling, subcarrier assignment and power allocation into a mathematical model. By employing Lagrange dual decomposition, asymptotically optimal packet scheduling and resource allocation strategy can be obtained. To reduce the computation complexity, we further develop a suboptimal solution which performs packet scheduling and subcarrier assignment jointly but power optimization independently. Finally, we validate our proposed algorithms in a multi-user scenario where all the video sequences are pre-encoded by H.264/AVC encoder. Experimental results demonstrate that our proposed algorithms can indeed improve the received video quality significantly compared with other existing algorithms.

Speed Accuracy Trade-off under Response Deadlines

The majority of two-alternative forced choice (2AFC) psychophysics studies have examined speed-accuracy trade-offs either in free-response or fixed viewing time paradigms with no hard time constraints on responding. Under response deadlines, reward maximization requires participants to modulate decision thresholds over the course of a trial such that when the deadline arrives a response is ensured despite the possible reduction of accuracy to the chance level. Importantly, this normative threshold collapsing process should take account not only of the deadline time but also the participants’ level of timing uncertainty about the deadline. For instance, for a given level of timing uncertainty participants facing a more stringent deadline and for a given deadline participants with higher timing uncertainty should start collapsing thresholds earlier within a trial(Frazier, P., & Yu, A. J. (2008). Sequential hypothesis testing under stochastic deadlines.Advances In Neural Information Processing Systems, 20, 465-472). We tested human participants in random dot motion discrimination task in three different conditions: free-response paradigm and with two different response deadlines (800 and 1000ms). We also quantified the level of timing uncertainty using two different temporal reproduction tasks. Micro speed-accuracy trade-off curves suggested that participants might have collapsed thresholds within a trial when facing a response deadline; however, observed performance diverged markedly from optimality. Participants did not take account of either response deadline times or their timing uncertainty. Our findings define a limit on the optimal temporal risk assessment performance of humans. We also consider other possible sources of observed reduction in accuracy as response times approach the deadline, through simulations of data using empirical model parameters.

Delivering London 2012: Transport programme assurance

The UK Olympic Delivery Authority transport team was responsible for providing safe, reliable and efficient transport during the London 2012 Olympic and Paralympic Games, and for leaving a positive transport legacy. Many organisations – from transport authorities to operators – worked together on this task. While the role of the ODA, as set out in the Olympic and Paralympic Act, was integrating, facilitating and coordinating, actual delivery was the role of the transport delivery partners: Transport for London, Network Rail, the Highways Agency. The ODA had overarching responsibility for managing and engaging these organisations to ensure delivery to the best industry standards. The delivery of enhanced transport infrastructure and operational functionality was central to the task, with 18 challenging key projects to be completed to stringent deadlines. A ‘light on process, heavy on performance' approach, underpinned by a comprehensive assurance framework and strong financial control, drove the projects forward. All were delivered under budget and ahead of time. This paper describes how the portfolio of transport projects was identified, the development of the budget, the programme assurance governance, the transition from infrastructure projects to operations, and the management of health, safety and sustainability.

Introduction to mini‐theme issue on “protease‐activated receptor signaling”

Introduction to mini‐theme issue on “protease‐activated receptor signaling”