Real-time Tasks Research Articles

Few-shot learning for facial expression recognition: a comprehensive survey

Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in improving the convenience in human life. It is widely applied in human–computer interaction tasks. However, recently, FER tasks have encountered certain data and training issues. To address these issues in FER, few-shot learning (FSL) has been researched as a new approach. In this paper, we focus on analyzing FER techniques based on FSL and consider the computational complexity and processing time in these models. FSL has been researched as it can solve the problems of training with few datasets and generalizing in a wild-environmental condition. Based on our analysis, we describe certain existing challenges in the use of FSL in FER systems and suggest research directions to resolve these issues. FER using FSL can be time efficient and reduce the complexity in many other real-time processing tasks and is an important area for further research.

Adaptive Correction of Landmark for Visual Homing in Mobile Vehicles

This paper presents an optimized visual homing method based on Average Landmark Vector (ALV), termed Adaptive Correction of Landmark (ACoL), which exhibits good homing performance and robustness in both static and dynamic indoor environments. The salient features of this paper are: (1) Inherent constraints of visual homing are analyzed in detail to reveal the limitations of existing ALV-based methods. (2) Instead of removing mismatched landmarks or artificially assigning fixed weights to landmarks, a novel solution is proposed by adaptively evaluating a reasonable weight for each landmark to account for contribution to the output. (3) Computation time of the proposed method is particularly concerned to enable the vehicle to perform real-time homing tasks in actual scenarios. (4) Simulations and experiments demonstrate the superiority of the proposed method under both static and dynamic conditions. Our method focuses on improving a vehicle's homing ability in terms of landmark matching, landmark distribution and homing direction. Comparative studies with other ALV-based methods demonstrate that our method generalizes well to various complex and challenging indoor scenarios.

REMAP: A Spatiotemporal CNN Accelerator Optimization Methodology and Toolkit Thereof

Designing CNN accelerators is getting more difficult owing to the fast-increasing types of CNN models. Some approaches use constant dataflow and micro-architecture that have lower design comlexity. However, these accelerators are difficult to adapt with the highly-diverse CNN models and often suffer from low PE utilization. Some other accelerators resort to reconfigurable devices such as FPGA and CGRA to support flexible dataflows in order to to fit diverse CNN layers. However, layer-by-layer processing may require more energy for frequent reconfiguration and off-chip DDR access. In this work, we introduce a reconfigurable pipeline accelerator (RPA) that can reduce the latency and DDR access by pipelining the compuptation of CNN layers. Although there has been several researches that try to speedup the design process by automatically exploring sub-set of the accelerator design space, identifying an available automated design tool that can effectively find the complete and optimal design scheme remains a problem, especially for the novel RPA architecture type. Unfortunately, comprehensive exploration of the whole design space faces an excessive large searching space. To tackle this problem, we propose REMAP, a toolkit for designing CNN accelerators based on the Monte Carlo Tree Search (MCTS) method. To efficiently search the huge design space, we propose several methods to improve searching efficiency. Evaluations show that REMAP significantly outperforms some state-of-the-art approaches; compared with GAMMA, it achieves an average speed increase of 14.75×, and an energy reduction of 45.45%; it also achieves a speed increase of 32.6× against ConfuciuX on MobileNetV2 and ResNet50. We also show a FPGA accelerator implementation which is based on REMAP’s search result, and it achieves high performance in real-time CNN tasks. This indicates that REMAP can provide high-quality design exploration with valuable insights and useful architecture design guidances.

6G-Empowered Offloading for Realtime Applications in Multi-Access Edge Computing

Multi-access Edge Computing (MEC) is a promising solution to the resource shortage problem on mobile devices. With MEC, a fraction of the computational tasks on mobile devices could be offloaded to edge servers. Over the past years, a series of machine learning based offloading methods for MEC have been proposed to reduce the completion time of computational tasks. However, most of the existing methods do not work well for realtime applications, which involve tasks with rigorous deadline constraints. In addition, offloading data-intensive tasks via the latest wireless networks, such as LTE and 5G, could lead to unsatisfactory transmission delays. Furthermore, with the state-of-the-art learning-based methods, both the training and inference operation of the learning algorithm are carried out on mobile devices, undesirably leaving less computation resources for computational tasks on mobile devices. In this paper, we propose a 6G-empowered learning-based offloading scheme, MELO, which can be used to make appropriate offloading decisions for realtime tasks. Specifically, the task offloading problem is first formulated as a Markov Decision Process. Thereafter, the problem is solved with a Reinforcement Learning (RL) algorithm, TD3. In addition, 6G is adopted as the communication infrastructure to sufficiently support the data transfer between mobile devices and edge servers. Furthermore, to leave more resources on mobile devices, we devise a novel learning architecture, EALA. With EALA, the training and inference operation of a learning algorithm are decoupled. The training operation is carried out on edge servers while the inference operation is performed on mobile devices. Our experimental results indicate that MELO outperforms the existing offloading methods in terms of task completion time.

Efficient CUDA stream management for multi-DNN real-time inference on embedded GPUs

Deep Neural Networks (DNNs) are widely used in Cyber–Physical Systems (CPS) that often involve multiple DNN tasks with varying real-time requirements. These tasks need to be deployed on a single embedded hardware platform with limited resources, such as an embedded GPU. Efficiently sharing the same embedded GPU among multiple real-time DNN tasks is a complex challenge. While existing DNN frameworks (e.g., PyTorch and TensorFlow) focus on maximizing average performance and high throughput on GPU, they lack scheduling management mechanisms considering multiple DNNs with different timing requirements. In this paper, we address this challenge by thoroughly examining and summarizing the scheduling rules for multiple kernels with different priorities in CUDA streams. Based on these rules, we design a framework that supports multi-DNN real-time inference and propose a method for allocating CUDA streams to DNN kernels to meet schedulability requirements while maximizing GPU resource utilization. Our proposed approach is implemented on an NVIDIA Jetson AGX Xavier embedded GPU system and validated using several popular DNNs. The results show that our approach achieves shorter response times compared with several state-of-the-art methods.

Quantum enhanced time synchronisation for communication network

It is essential to establish precise times in future communication networks. Any real-time task’s ability to function depends on the system’s ability to synchronise time. In the current communication network, time synchronisation is critical and must be maintained to transmit data packets. The functionality of 6G, the Tactile Internet, Time-Sensitive Networking, and ultra-reliable low-latency communications is highly susceptible to time synchronisation. We investigated the idea of employing time synchronisation across different communication network nodes. The current state-of-the-art employs network protocols like Precision Time Protocol for clock synchronisation across different nodes. These network protocols are not very robust and can generate jitters in data transmission. In this paper, we suggested synchronising the time of the node clocks at three different places using quantum technology. Notably, the oscillation frequencies of each qubit (or oscillator) located at these nodes can be synchronised using quantum synchronisation technique. This set of three oscillators will work as a single clock and will be the master clock of the network. We propose distributing precise time and frequency standards using quantum synchronisation on node clocks. We can synchronise the three qubits (each placed at one node) to oscillate at an identical frequency by applying an external field of a wavelength of 813.32nm. We analysed our model for different coupling constants and dissipation rates to provide an analysis of the behaviour of the amount of synchronisation in different experimental configurations. The optimal accuracy for our system is 1.6×1015 signals per second. Further, we used the Allan deviation to examine the stability of our system for various noise strengths.

Segment based power-efficient scheduling for real-time DAG tasks on edge devices

Smart Mobile Devices (SMDs) are crucial for the edge computing paradigm’s real-world sensing. Real-time applications, which are computationally intensive and periodic with strict time constraints, can typically be used to replicate real-world sensing. Such applications call for increased processing speed, memory capacity, and battery life on SMDs, which are typically resource-constrained due to physical size restrictions. As a result, scheduling real-time applications for SMDs that are power efficient is crucial for the regular operation of edge computing platforms, and downstream decision-making tasks like computation offloading require the prediction of power consumption using power-saving approaches like DVFS. The main question is how to swiftly develop a better solution to the NP-Hard power efficient scheduling problem with DVFS. Thus, by segmenting the aligned tasks on an SMD, we present a segment-based analysis approach. Additionally, we offer a segment-based scheduling algorithm (SEDF) that draws inspiration from the segment-based analysis approach to achieve power-efficient scheduling for these real-time workloads. This segment-based approach yields a power consumption bound (PB), and a computation offloading use case is developed to demonstrate the application of PB in the subsequent decision-making processes. Both simulations and actual device tests are used to confirm the PB, SEDF, and the effectiveness of offloading decision-making. We demonstrate empirically that PB can be utilized to make approximative optimal decisions in decision-making problems involving computation offloading. SEDF is a straightforward and effective scheduling approach that can cut the power consumption of a multi-core SMD by roughly 30%.

How Hard Is It Really? Assessing Game-Task Difficulty Through Real-Time Measures of Performance and Cognitive Load

Background Serious and entertainment game designers strive to create engaging, immersive, and often, challenging games. This task involves modifying game mechanics or environments to create experiences with differing levels of challenge to meet player skill. The balance between different game mechanics or environments, and the differing levels of challenge they pose, is typically understood through iterative testing. Balance and challenge becomes increasingly important in serious games and simulation training as these games commonly need to be engaging and impart learning content. Overburdening players’ cognitive capacity with either too much gameplay challenge or learning content may reduce the educational effectiveness of the game. Aim. In this research, we develop a game-based driving simulation with different gameplay tasks to explore the impact of different types of challenges and game aesthetics on real-time cognitive load and task performance, which may inform serious game design. We also test the validity of a game-embedded real-time cognitive load measuring method. Method A total of 31 participants undertook the driving simulation experiment under three different aesthetic conditions using a within-subject experimental design. Cognitive load was measured using three different methods, and performance was measured via in-game metrics. Additionally, demographic and engagement surveys were also completed. Results Player performance and cognitive load respond differently to different types of challenge, and an appropriate level of game challenge can lower cognitive load. The embedded cognitive load measure was validated as an effective method for evaluating real-time cognitive load during gameplay. Conclusion The results demonstrate the validity of a dual measure approach for future adaptive serious games and simulation training environments combining performance and cognitive load. An easy to implement, and robust, in-game measure for cognitive load has been validated in real-world conditions. From these results, a system for dynamic difficulty adjustment is proposed tailored towards serious games and simulation.

A heuristic mixed real-time task allocation of virtual utilization in multi-core processor

Multi-core processor is widely used as the running platform for safety-critical real-time systems such as spacecraft, and various types of real-time tasks are dynamically added at runtime. In order to improve the utilization of multi-core processors and ensure the real-time performance of the system, it is necessary to adopt a reasonable real-time task allocation method, but the existing methods are only for single-core processors or the performance is too low to be applicable. Aiming at the task allocation problem when mixed real-time tasks are dynamically added, we propose a heuristic mixed real-time task allocation algorithm of virtual utilization VU-WF (Virtual Utilization Worst Fit) in multi-core processor. First, a 4-tuple task model is established to describe the fixed-point task and the sporadic task in a unified manner. Then, a VDS (Virtual Deferral Server) for serving execution requests of fixed-point task is constructed and a schedulability test of the mixed task set is derived. Finally, combined with the analysis of VDS's capacity, VU-WF is proposed, which selects cores in ascending order of virtual utilization for the schedulability test. Experiments show that the overall performance of VU-WF is better than available algorithms, not only has a good schedulable ratio and load balancing but also has the lowest runtime overhead. In a 4-core processor, compared with available algorithms of the same schedulability ratio, the load balancing is improved by 73.9%, and the runtime overhead is reduced by 38.3%. In addition, we also develop a visual multi-core mixed task scheduling simulator RT-MCSS (open source) to facilitate the design and verification of multi-core scheduling for users. As the high performance, VU-WF can be widely used in resource-constrained and safety-critical real-time systems, such as spacecraft, self-driving cars, industrial robots, etc.

Mobility and Security Aware Real-Time Task Scheduling in Fog-Cloud Computing for IoT Devices: A Fuzzy-Logic Approach

Abstract This paper aims to improve the overall task processing time of mobile real-time Internet of Things (IoT) applications in fog-cloud computing, considering the various resource and security requirements along with the time constraints of the task. Fog computing extends the cloud resources to serve the IoT devices at the network edge. In such a scenario, deciding whether the tasks should be processed at the fog layer or submitted to the cloud is critical. Moreover, for real-time applications, the mobility of IoT devices and the limited bandwidth available at the edge devices endanger the low processing time of the task. Besides, the security demands of some IoT applications (i.e. healthcare) require processing the tasks by specific fog or cloud servers to assure confidentiality of information, which may also delay the task processing time. Therefore, we first address the mobility issue by proposing three different algorithms that work on allocating the mobile IoT device to the appropriate edge device (i.e. fog gateway), considering the distance and bandwidth load factors. Then, we offer a novel task scheduling algorithm that uses fuzzy logic to optimize the distribution of tasks between the fog and cloud layers, considering the task security requirements. The algorithm selects the proper processing unit to execute the task in the fog layer by exploiting the task demands (i.e. computation, storage, bandwidth, security) and deadline. Results demonstrate that considering the factors of distance and available bandwidth load while allocating the IoT device to the fog gateway improves the task processing time better than adopting one aspect. Results also show that our proposed scheduling algorithm outperforms other existing algorithms regarding makespan, turnaround time, success ratio and processing time metrics.

Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice

The power distribution network business gradually extends from the grid domain to the social service domain, and the new business keeps expanding. The edge device uses microservice architecture and container technology to realize the processing of different services by one physical device. Although the power distribution network IoT with cloud-edge architecture has good scalability, scenarios with insufficient resources for edge devices may occur. In order to support the scheduling and collaborative processing of tasks under the resource-constrained scenario from the edge device, this paper proposes a cloud-edge collaborative online scheduling method for the distribution of station area tasks under the microservice architecture. The article models the characteristics of power tasks and their constraints in the cloud-edge containerized scenario, designs the priority policy and task assignment policy based on the cloud-edge scheduling mechanism of containerized power tasks, and schedules the tasks in real time by an improved online algorithm. Simulation results show that the algorithm proposed in this paper has high task execution efficiency, can improve the completion rate of important tasks with limited resources of edge devices, and improves system security through resource replacement.

MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION

You have accessJournal of UrologyCME1 Apr 2023MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION Okyaz Eminaga, Timothy Lee, Mark Laurie, Jessie Ge, Vinh LA, Jin Long, Eugene Shkolyar, Xiao Jia, Axel Semjonow, Martin Bogemann, Hubert Lau, Lei Xing, and Joseph Liao Okyaz EminagaOkyaz Eminaga More articles by this author , Timothy LeeTimothy Lee More articles by this author , Mark LaurieMark Laurie More articles by this author , Jessie GeJessie Ge More articles by this author , Vinh LAVinh LA More articles by this author , Jin LongJin Long More articles by this author , Eugene ShkolyarEugene Shkolyar More articles by this author , Xiao JiaXiao Jia More articles by this author , Axel SemjonowAxel Semjonow More articles by this author , Martin BogemannMartin Bogemann More articles by this author , Hubert LauHubert Lau More articles by this author , Lei XingLei Xing More articles by this author , and Joseph LiaoJoseph Liao More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003318.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Development of intelligence systems for bladder tumor detection is cost- and labor-intensive. Specifically, image annotation is one of the most expensive tasks in the development of intelligence systems. Moreover, previous studies utilized selective screenshots or frame sequences for model development and validation, despite that cystoscopy is a dynamic visual inspection impacted by random noises. The current work proposes an efficient strategy to develop augmented intelligence strategy ready for real-time bladder tumor detection as computer-aided assistance tool for clinicians. METHODS: We used a previously published educational cystoscopy atlas (n=312 images) and our deep learning models (ConvNeXt, PlexusNet, MobileNet, SwinTransformer) covering a variety of model complexity and computation efficacy to estimate the ratio between cancer and normal confidence scores; We applied an image augmentation strategy called RandAugment to populate the training set for model training and externally validated on video records for the initial diagnostic cystoscopy prior to TURBT from 68 cases with benign and bladder cancers tumors (i.e., region of interest, ROI) at a single center. Each frame of the video was labeled by ROI status. ROI was confirmed by pathology examination and the Delphi method. The ROI status was predicted based on the ratio (if the ratio>1, then the frame is positive for ROI, otherwise negative). On external validation, areas of adequate illumination in video frames were considered as input for models. The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. The block level split each full-length video into small segments according to the ROI status. RESULTS: Specificity was statistically comparable between four models at frame (range: 30.0–44.8%) and block levels (56–67%) by p>0.05. While sensitivity at frame level (range: 81.4 – 88.1) statistically differed between the models, sensitivity at block level (100%) and ROI level (100%) were comparable between these models, indicating that all ROI are detectable by these models and that the frame-level model performance is impacted by the random noises. MobileNet and PlexusNet were computationally more efficient (22 and 19 frames per second, retrospectively) and suitable for real-time detection task than ConvNeXt and SwinTransformer (13 and 17 frames per second, retrospectively). CONCLUSIONS: Educational cystoscopy atlas and cost-effective model development strategy facilitates the development of accurate and efficient intelligence systems with potential use for real-time bladder tumor detection. Source of Funding: The work was supported by National Institutes of Health R01 CA260426 to JCL. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e850 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Okyaz Eminaga More articles by this author Timothy Lee More articles by this author Mark Laurie More articles by this author Jessie Ge More articles by this author Vinh LA More articles by this author Jin Long More articles by this author Eugene Shkolyar More articles by this author Xiao Jia More articles by this author Axel Semjonow More articles by this author Martin Bogemann More articles by this author Hubert Lau More articles by this author Lei Xing More articles by this author Joseph Liao More articles by this author Expand All Advertisement PDF downloadLoading ...

SAFLA: Scheduling Multiple Real-Time Periodic Task Graphs on Heterogeneous Systems

Many modern Cyber Physical Systems (CPSs) are composed of multiple independent periodically executing real-time control tasks having inter-dependent component sub-tasks. Each such control task is therefore usually represented as <i>Directed-acyclic Task Graphs</i> (DTGs). These CPSs are often distributed in nature and are quickly shifting from homogeneous to heterogeneous processing platforms in order to meet ever increasing demands for performance and energy savings, within limited resource budgets. In spite of the practical relevance of the problem in today's CPS design scenario, very few research works in literature have tried to address this due to its inherent computational as well as design complexity. This work endeavors to solve the problem of co-scheduling a set of periodic real-time applications each modelled as an independent DTG, to be executed on a distributed platform consisting of heterogeneous processors communicating using shared buses. Assuming the processing platform to be DVFS (Dynamic Voltage Frequency Scaling) enabled, we attempt to minimize dynamic energy dissipation associated with the execution of all DTGs over an hyperperiod <inline-formula><tex-math notation="LaTeX">$\mathcal {H}$</tex-math></inline-formula> while ensuring that no DTG instance within <inline-formula><tex-math notation="LaTeX">$\mathcal {H}$</tex-math></inline-formula> misses its deadline. The problem has first been formally represented as a constraint optimization problem. However, an optimal solution using standard solvers become prohibitively compute as well as memory intensive and doesn't scale even for moderate problem sizes. Hence, in this work, we attempt to develop a three-phase list-based hierarchical scheduling algorithm called <i>Slack Aware Frequency Level Allocator</i> ( <i>SAFLA</i> ). The efficacy of <i>SAFLA</i> has been critically evaluated through simulation using benchmark DTGs.

Real-Time Target Tracking System With Spiking Neural Networks Implemented on Neuromorphic Chips

Real-time target tracking is a usual task for humans despite the neural delays during the nervous system’s axonal transfer and neural processing. A plausible explanation is that the human brain employs predictive mechanisms to compensate for the delay. Inspired by the brain, this paper adopts a prediction network based on spiking neural networks (SNNs) to implement a real-time tracking task on a neuromorphic chip with low power consumption. The SNN-based prediction network outperforms the long short-term memory (LSTM) network on a small dataset and reduces 90% to 98% computations compared with LSTM. The quantized SNN-based network is deployed on a neuromorphic chip, and it takes 25ms and only 442 626nJ for a single prediction. The tracking performance of the system is also verified in real-life scenarios. Furthermore, the proposed real-time target tracking system can be easily ported to other neuromorphic platforms.

Task Offloading for Deep Learning Empowered Automatic Speech Analysis in Mobile Edge-Cloud Computing Networks

With the explosive growth of mobile multimedia services and artificial intelligence applications involving automatic speech analysis (ASA), mobile devices are increasingly unable to handle these computation-intensive tasks generated by users due to the limited computing resource. Besides, the existing cloud computing paradigm is not capable of processing such real-time and delay-sensitive ASA tasks. In this paper, by leveraging mobile edge computing and deep learning (DL), we investigate task offloading for DL-empowered ASA in mobile edge-cloud computing networks to minimize the total time for processing ASA tasks, thereby providing an agile service response. Specifically, to accelerate the processing of ASA tasks, we decompose a convolutional neural network based encoder-decoder model and deploy the encoder at edge servers to extract the features of ASA tasks. Moreover, edge servers derive the user tolerance limit by using a linear regression model for further enhancing the quality of experience of users. Based on some certain network constraints (i.e., user association and edge servers&#x0027; storage/computing capacity), we propose a low-complexity and distributed offloading framework to solve the formulated complex problem. Evaluation results demonstrate the effectiveness of the proposed framework on reducing the total time and improving the satisfaction rate of users.

Trends, relationship, and model of selected service sector workers in Malaysia: Physiological responses of mental workload and mental fatigue during performing real-time tasks

Service sector is a job that is considered exposed to high mental demand. There is lack of technique to measure issues related to the mental workload and mental fatigue levels among workers. Moreover, no studies have yet developed a model to predict mental fatigue especially for service sector workers. It is necessary to investigate mental workload and mental fatigue in real time working activities. The main purpose of this study was to identify the trends and relationship between mental workload and mental fatigue level among service sector workers. Ten participants with a mean age of 35.00±8.62 (SD) years took part in the study. Two experiments in Without Rest (WoR) and With Rest (WR) segments involving data entry and arithmetic tasks were conducted. Physiological measures using Electroencephalogram (EEG), Electrooculogram (EOG) and heart rate (HR) were assessed while participants were performing the tasks. The result shows that EEG alpha signal was significantly higher at the end of WR compared to WoR segment (p&lt;0.05). Comparison between WoR and WR segments for each task show that HR of WR tasks were significantly lower in all tasks (p&lt;0.05). This study developed seven mental workload and mental fatigue conceptual models with strong variables correlations (r&gt;0.05) to evaluate the variability of both parts of two types of activities, namely, data entry and arithmetic tasks. The findings highlighted that validated parameters and methods for mental fatigue and mental workload measures are brain signals and heart rate monitoring, and task performance measure. Significant findings of the study could be as a reference for organizations to plan and manage resources by optimizing mental workload condition and minimizing mental fatigue occurrence

Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Today, Function-as-a-Service is the most promising concept of serverless cloud computing. It makes possible for developers to focus on application development without any system management effort: FaaS ensures resource allocation, fast response time, schedulability, scalability, resiliency, and upgradability. Applications of 5G, IoT, and Industry 4.0 raise the idea to open cloud-edge computing infrastructures for time-critical applications too, i.e., there is a strong desire to pose real-time requirements for computing systems like FaaS. However, multi-node systems make real-time scheduling significantly complex since guaranteeing real-time task execution and communication is challenging even on one computing node with multi-core processors. In this paper, we present an analytical model and a heuristic partitioning scheduling algorithm suitable for real-time FaaS platforms of multi-node clusters. We show that our task scheduling heuristics could outperform existing algorithms by 55%. Furthermore, we propose three conceptual designs to enable the necessary real-time communications. We present the architecture of the envisioned real-time FaaS platform, emphasize its benefits and the requirements for the underlying network and nodes, and survey the related work that could meet these demands.

Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

In satellite-terrestrial integrated networks, it is a common practice to schedule real-time tasks from low Earth orbit (LEO) satellites to ground stations (GSs) for data processing. However, the joint task scheduling and resource allocation under unknown environment dynamics (e.g., transmission latency) remains to be a challenging problem. First, the tradeoff between task latencies and energy consumption should be carefully considered when making decisions to minimize task latencies under time-averaged energy consumption constraints. Second, to learn the environment uncertainties and minimize the system performance loss (i.e., regret) in terms of task latencies, both online feedback and offline history should be leveraged efficiently, and the accompanying exploration-exploitation tradeoff should be dealt with in a proper way. In this article, we formulate the joint task scheduling and resource allocation problem as a constrained combinatorial multi-armed bandit (CMAB) problem. To solve the problem, by integrating online learning, online control, and offline historical information, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task scheduling and Resource allocation scheme with Data-driven Bandit Learning</i> called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRDBL</i> . Our theoretical and numerical results show that TRDBL achieves a sublinear time-averaged regret while satisfying the time-averaged energy consumption constraints.

A systematic review on security aware real-time task scheduling

Nowadays, security and scheduling are the most crucial aspects of each distributed real-time application due to the rapidly enhanced use of such applications. This study presents a systematic literature review (SLR) on security and scheduling techniques for real-time applications in homogeneous and heterogeneous environments and to identify areas that need more attention for a wide range of applicability of real-time applications. The search procedure identified 3162 articles, and 103 best relevant articles are selected based on quality assessment scores for a brief review and discussions with the area experts or academicians. This study considers articles with a quality assessment score of more than five for a brief review. Hence, the applications related to security, scheduling, load balancing, fixed priority, task, job, and packet scheduling, and deadline constraints of soft and hard real-time systems in homogeneous and heterogeneous environments as per the state-of-the-art have been discussed. This discussion provides a better understanding of the real-time applications. The survey thus, aims to present the improvements in real-time task scheduling with security constraints in different distributed computing environments. The SLR also highlights various real-time task scheduling algorithms/techniques including the role of security. This SLR also provides meaningful and valuable study to the researchers by providing brief studies of various real-time task scheduling algorithms and security needs in tabular form.

A Measuring Tool for Interrupt Latency Based on Linux

Linux has been used more and more in the field of industrial control and robot control, the non-real-time nature of the final human-computer interaction program may cause system performance impact in industrial production. Interrupt response time is an important indicator to measure the real-time performance of real-time operating systems. Accurate test data can be used as an evaluation index for selecting the real-time performance of the operating system. In order to improve the real-time performance of Linux, analyze real-time problems, and solve possible real-time bottlenecks, this paper designs a probe-type tool that can measure the interrupt response time, output the interrupt call stack, and analyze the reasons for high interrupts. By optimizing specific limited and high-latency paths, Linux can still maintain an acceptable range of latency for a long time under various high-load scenarios. After testing, the tool has good measurement accuracy, which provides a basis for developing real-time tasks and solving real-time problems.