Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00021
Yoshitada Takaso, Hiroshi Oyama, H. Takada, Takuya Azumi
The scale and demand for protection functionalities in embedded systems continue to grow as Internet of things technology develops. Simultaneously, multiprocessor real-time operating systems (RTOSs) with memory protection functionalities are broadening in use. On the other hand, the large development effort and poor reusability of multiprocessor RTOS-based development remain hindrances to their use. To solve this problem, this paper proposes a component framework for multiprocessor RTOSs that includes memory protection. In the proposed framework, OS functionalities are reframed as components. The allocation of objects to processors and protection settings, which are supported by the OS, can then be configured based on the component description. Plugins are implemented here to generate files for object generation from component descriptions of OS functionalities. In addition, access to the protection domain can be defined based on the component description. Finally, test programs are used in a performance evaluation. The proposed framework enables extensions to the component-based OS while maintaining most of the functionality and performance of the target OS.
{"title":"HRMP3+TECS: Component Framework for Multiprocessor Real-time Operating System with Memory Protection","authors":"Yoshitada Takaso, Hiroshi Oyama, H. Takada, Takuya Azumi","doi":"10.1109/ISORC58943.2023.00021","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00021","url":null,"abstract":"The scale and demand for protection functionalities in embedded systems continue to grow as Internet of things technology develops. Simultaneously, multiprocessor real-time operating systems (RTOSs) with memory protection functionalities are broadening in use. On the other hand, the large development effort and poor reusability of multiprocessor RTOS-based development remain hindrances to their use. To solve this problem, this paper proposes a component framework for multiprocessor RTOSs that includes memory protection. In the proposed framework, OS functionalities are reframed as components. The allocation of objects to processors and protection settings, which are supported by the OS, can then be configured based on the component description. Plugins are implemented here to generate files for object generation from component descriptions of OS functionalities. In addition, access to the protection domain can be defined based on the component description. Finally, test programs are used in a performance evaluation. The proposed framework enables extensions to the component-based OS while maintaining most of the functionality and performance of the target OS.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114711204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00014
M. Sudvarg, Sanjoy Baruah, Chris Gill
Elastic scheduling provides a model for systems in which individual task utilizations can adapt to guarantee schedulability despite limited resources. Each task is characterized by a range of acceptable utilizations and an “elastic constant” representing its flexibility to reduce or “compress” its utilization from the desired maximum. Utilization compression is realized by either extending task periods or reducing workloads. This paper extends the model to address period compression for fixed-priority constrained-deadline task systems scheduled on a uniprocessor. We propose two approximate algorithms and one optimal algorithm for determining compression under the model. We then compare the execution times and accuracies of all three, demonstrating that even for large task sets, online compression can be performed feasibly on low-powered embedded systems.
{"title":"Elastic Scheduling for Fixed-Priority Constrained-Deadline Tasks","authors":"M. Sudvarg, Sanjoy Baruah, Chris Gill","doi":"10.1109/ISORC58943.2023.00014","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00014","url":null,"abstract":"Elastic scheduling provides a model for systems in which individual task utilizations can adapt to guarantee schedulability despite limited resources. Each task is characterized by a range of acceptable utilizations and an “elastic constant” representing its flexibility to reduce or “compress” its utilization from the desired maximum. Utilization compression is realized by either extending task periods or reducing workloads. This paper extends the model to address period compression for fixed-priority constrained-deadline task systems scheduled on a uniprocessor. We propose two approximate algorithms and one optimal algorithm for determining compression under the model. We then compare the execution times and accuracies of all three, demonstrating that even for large task sets, online compression can be performed feasibly on low-powered embedded systems.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00023
Zhuangwei Kang, Ziran Min, Shuang Zhou, Yogesh D. Barve, A. Gokhale
The primary challenge facing cloud-based deep learning systems is the need for efficient orchestration of large-scale datasets with diverse data formats and provisioning of high-performance data loading capabilities. To that end, we present DLCache, a cloud-native dataset management and runtime-aware data-loading solution for deep learning training jobs. DLCache supports the low-latency and high-throughput I/O requirements of DL training jobs using cloud buckets as persistent data storage and a dedicated computation cluster for training. DLCache comprises four layers: a control plane, a metadata plane, an operator plane, and a multi-tier storage plane, which are seamlessly integrated with the Kubernetes ecosystem thereby providing ease of deployment, scalability, and self-healing. For efficient memory utilization, DLCache is designed with an on-the-fly and best-effort caching mechanism that can auto-scale the cache according to runtime configurations, resource constraints, and training speeds. DLCache considers both frequency and freshness of data access as well as data preparation costs in making effective cache eviction decisions that result in reduced completion time for deep learning workloads. Results of evaluating DLCache on the Imagenet-ILSVRC and LibriSpeech datasets under various runtime configurations and simulated GPU computation time experiments showed up to a 147.49% and 156.67% improvement in data loading throughput, respectively, compared to the popular PyTorch framework.
{"title":"Dataset Placement and Data Loading Optimizations for Cloud-Native Deep Learning Workloads","authors":"Zhuangwei Kang, Ziran Min, Shuang Zhou, Yogesh D. Barve, A. Gokhale","doi":"10.1109/ISORC58943.2023.00023","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00023","url":null,"abstract":"The primary challenge facing cloud-based deep learning systems is the need for efficient orchestration of large-scale datasets with diverse data formats and provisioning of high-performance data loading capabilities. To that end, we present DLCache, a cloud-native dataset management and runtime-aware data-loading solution for deep learning training jobs. DLCache supports the low-latency and high-throughput I/O requirements of DL training jobs using cloud buckets as persistent data storage and a dedicated computation cluster for training. DLCache comprises four layers: a control plane, a metadata plane, an operator plane, and a multi-tier storage plane, which are seamlessly integrated with the Kubernetes ecosystem thereby providing ease of deployment, scalability, and self-healing. For efficient memory utilization, DLCache is designed with an on-the-fly and best-effort caching mechanism that can auto-scale the cache according to runtime configurations, resource constraints, and training speeds. DLCache considers both frequency and freshness of data access as well as data preparation costs in making effective cache eviction decisions that result in reduced completion time for deep learning workloads. Results of evaluating DLCache on the Imagenet-ILSVRC and LibriSpeech datasets under various runtime configurations and simulated GPU computation time experiments showed up to a 147.49% and 156.67% improvement in data loading throughput, respectively, compared to the popular PyTorch framework.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124769891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00035
X. Zhang, Zhangqin Huang
Abstract-As a narrowband communication technology, Long Range (LoRa) contributes to the long development of Internet of Things (IoT) applications. The LoRa gateway plays an important role in the IoT transport layer, and security and efficiency are the key issues of current research. On the one hand, in the centralized working model of IoT systems built by traditional LoRa gateways, all the dat a generated and reported by end devices are processed and stored in cloud servers, which are susceptible to security issues such as data loss and data falsification. On the other hand, edge computing, as an innovative approach that brings data processing and storage closer to the endpoints, can create a decentralized security infrastructure for LoRa gateway systems, resulting in an edge intelligent IoT working model. Although this paradigm delivers unique features and improved quality of service (QoS), installing IoT applications at LoRa gateways with limited computing and memory capabilities presents considerable obstacles. To address this challenge, an edge intelligent LoRa gatew ay is designed and implemented in this paper. Firstly, we developed an edge intelligent LoRa gateway prototype on an FPGA-based embedded hardware system. Then, we proposed Latency-Aware Algorithm (LAA) can greatly improve the reliability of the network system by using a distributed edge computing network technology that can achieve maintenance operations such as detection, repair, and replacement of failures of edge nodes in the network. Finally, many experiments were conducted to evaluate the performance of the proposed edgeintelligent LoRa gat eway. The results indicate that the proposed edge intelligent LoRa gateway is more effective in latency-aware in IoT applications, while greatly ensuring system availability and IoT network reliability.
{"title":"A Design and Implementation of Decentralized Edge Intelligent LoRa Gateway","authors":"X. Zhang, Zhangqin Huang","doi":"10.1109/ISORC58943.2023.00035","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00035","url":null,"abstract":"Abstract-As a narrowband communication technology, Long Range (LoRa) contributes to the long development of Internet of Things (IoT) applications. The LoRa gateway plays an important role in the IoT transport layer, and security and efficiency are the key issues of current research. On the one hand, in the centralized working model of IoT systems built by traditional LoRa gateways, all the dat a generated and reported by end devices are processed and stored in cloud servers, which are susceptible to security issues such as data loss and data falsification. On the other hand, edge computing, as an innovative approach that brings data processing and storage closer to the endpoints, can create a decentralized security infrastructure for LoRa gateway systems, resulting in an edge intelligent IoT working model. Although this paradigm delivers unique features and improved quality of service (QoS), installing IoT applications at LoRa gateways with limited computing and memory capabilities presents considerable obstacles. To address this challenge, an edge intelligent LoRa gatew ay is designed and implemented in this paper. Firstly, we developed an edge intelligent LoRa gateway prototype on an FPGA-based embedded hardware system. Then, we proposed Latency-Aware Algorithm (LAA) can greatly improve the reliability of the network system by using a distributed edge computing network technology that can achieve maintenance operations such as detection, repair, and replacement of failures of edge nodes in the network. Finally, many experiments were conducted to evaluate the performance of the proposed edgeintelligent LoRa gat eway. The results indicate that the proposed edge intelligent LoRa gateway is more effective in latency-aware in IoT applications, while greatly ensuring system availability and IoT network reliability.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114546731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00017
M. Hasan, Sibin Mohan
Modern Internet-of-Things devices are vulnerable to attacks targeting outgoing actuation commands that modify their physical behaviors. We present a “selective checking” mechanism that uses game-theoretic modeling to identify the suitable subset of commands to be checked in order to deter an adversary. This mechanism is coupled with a “delay-aware” trusted execution environment to ensure that only verified actuation commands are ever sent to the physical system, thus maintaining the safety and integrity of the system. Our proposed selective checking and trusted execution (SCATE) framework is implemented on an off-the-shelf ARM platform running embedded Linux and tested on four realistic IoT-specific cyber-physical systems (a ground rover, a flight controller, a robotic arm and an automated syringe pump).
{"title":"You Can’t Always Check What You Wanted: : Selective Checking and Trusted Execution to Prevent False Actuations in Real-Time Internet-of-Things","authors":"M. Hasan, Sibin Mohan","doi":"10.1109/ISORC58943.2023.00017","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00017","url":null,"abstract":"Modern Internet-of-Things devices are vulnerable to attacks targeting outgoing actuation commands that modify their physical behaviors. We present a “selective checking” mechanism that uses game-theoretic modeling to identify the suitable subset of commands to be checked in order to deter an adversary. This mechanism is coupled with a “delay-aware” trusted execution environment to ensure that only verified actuation commands are ever sent to the physical system, thus maintaining the safety and integrity of the system. Our proposed selective checking and trusted execution (SCATE) framework is implemented on an off-the-shelf ARM platform running embedded Linux and tested on four realistic IoT-specific cyber-physical systems (a ground rover, a flight controller, a robotic arm and an automated syringe pump).","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132792076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00016
Sanjoy Baruah, Thidapat Chantem, N. Fisher, Fatima Raadia
Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness. We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems, and develop corresponding scheduling algorithms and associated schedulability tests.
{"title":"A Scheduling Model Inspired by Security Considerations","authors":"Sanjoy Baruah, Thidapat Chantem, N. Fisher, Fatima Raadia","doi":"10.1109/ISORC58943.2023.00016","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00016","url":null,"abstract":"Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness. We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems, and develop corresponding scheduling algorithms and associated schedulability tests.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129382497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00042
T. Guo
This paper considers a use case of flying base station placement enabled by digital twin (DT), and demonstrates how DT can help reduce the impact of a non-stationary environment on reinforcement learning (RL). RL is able to learn the optimal policy via interacting with a specific environment. However, it has been observed that RL is very sensitive to environment change, mainly because the environment variation disturbs RL training/learning. A possible approach is to execute the RL process in the DT using snapshots of the environment (parameters), and update the parameters at a proper frequency. The DT-RL bundled approach takes advantage of computing resources in the DT, speeds up the process and saves battery energy of the flying base station, and more importantly, mitigates the nonstationary impact on RL. Specifically, the use case is about quickly connecting mobile users with an aerial bass station. The base station is autonomously and optimally placed according to a predefined criterion to connect scattered slow-movement users. Q-learning, a common type of RL, is employed as a solution to the optimization of base station placement. Tailored for this application, a two-stage base station placement algorithm is proposed and evaluated. For the configuration considered in this paper, numerical results suggest that 1) the Q-learning algorithm run solely in the physical space does not work due to intolerable time consuming and optimization divergence, and 2) the proposed scheme can catch up with random slow movement of mobile users, and tolerate certain measurement and model errors. With necessary modification and extension, the proposed framework could be applied to other DT-assisted cyber-physical systems.
{"title":"Digital Twin Enabled Q-Learning for Flying Base Station Placement: Impact of Varying Environment and Model Errors","authors":"T. Guo","doi":"10.1109/ISORC58943.2023.00042","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00042","url":null,"abstract":"This paper considers a use case of flying base station placement enabled by digital twin (DT), and demonstrates how DT can help reduce the impact of a non-stationary environment on reinforcement learning (RL). RL is able to learn the optimal policy via interacting with a specific environment. However, it has been observed that RL is very sensitive to environment change, mainly because the environment variation disturbs RL training/learning. A possible approach is to execute the RL process in the DT using snapshots of the environment (parameters), and update the parameters at a proper frequency. The DT-RL bundled approach takes advantage of computing resources in the DT, speeds up the process and saves battery energy of the flying base station, and more importantly, mitigates the nonstationary impact on RL. Specifically, the use case is about quickly connecting mobile users with an aerial bass station. The base station is autonomously and optimally placed according to a predefined criterion to connect scattered slow-movement users. Q-learning, a common type of RL, is employed as a solution to the optimization of base station placement. Tailored for this application, a two-stage base station placement algorithm is proposed and evaluated. For the configuration considered in this paper, numerical results suggest that 1) the Q-learning algorithm run solely in the physical space does not work due to intolerable time consuming and optimization divergence, and 2) the proposed scheme can catch up with random slow movement of mobile users, and tolerate certain measurement and model errors. With necessary modification and extension, the proposed framework could be applied to other DT-assisted cyber-physical systems.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125693952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00034
Bhaskar Anand, P. Rajalakshmi
Light Detection and Ranging (LiDAR) sensor play a vital role in the fields like environment perception for an autonomous vehicle, surveying and many other fields. LiDAR emits an enormous amount of data, which is difficult to transmit over a wireless medium. The need for LiDAR data transmission arises specifically in the Intelligent Transportation System (ITS) and surveying. It is advantageous to transmit LiDAR (mounted on a car) data over a wireless medium and receive the same by another vehicle or Road Side Units (RSUs) or control stations of a tesbed setup. Similarly, visualizing LiDAR data remotely while data acquisition by LiDAR mounted on Unmanned Aerial Vehicles (UAVs) could simplify the process of surveying. This paper presents a client-server-based LiDAR data streaming system using socket programming. In this system, the server transmits LiDAR point-cloud percept data, and the client captures and visualizes the streaming data over a WiFi medium. The server and the client are both equipped with the Robot Operating System(ROS). An extensive analysis of data transfer on two popular protocols, TCP and UDP, has also been presented. The data transmission and reception on ROS platform was successfully performed with a tolerable latency.
{"title":"Client-Server Based Implementation of Real-time LiDAR Data Streaming on ROS platform","authors":"Bhaskar Anand, P. Rajalakshmi","doi":"10.1109/ISORC58943.2023.00034","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00034","url":null,"abstract":"Light Detection and Ranging (LiDAR) sensor play a vital role in the fields like environment perception for an autonomous vehicle, surveying and many other fields. LiDAR emits an enormous amount of data, which is difficult to transmit over a wireless medium. The need for LiDAR data transmission arises specifically in the Intelligent Transportation System (ITS) and surveying. It is advantageous to transmit LiDAR (mounted on a car) data over a wireless medium and receive the same by another vehicle or Road Side Units (RSUs) or control stations of a tesbed setup. Similarly, visualizing LiDAR data remotely while data acquisition by LiDAR mounted on Unmanned Aerial Vehicles (UAVs) could simplify the process of surveying. This paper presents a client-server-based LiDAR data streaming system using socket programming. In this system, the server transmits LiDAR point-cloud percept data, and the client captures and visualizes the streaming data over a WiFi medium. The server and the client are both equipped with the Robot Operating System(ROS). An extensive analysis of data transfer on two popular protocols, TCP and UDP, has also been presented. The data transmission and reception on ROS platform was successfully performed with a tolerable latency.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132583146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00041
Akram Hakiri, Sadok Ben, Gokhale Aniruddha
This paper introduces the Hyper-5G research project, which aims at developing and evaluating an experimental proof of concept of a cross-Atlantic Network Digital Twin for the future wireless mobile 5G and beyond (B5G). Hyper-5G project brings innovative capabilities to allow distributed twins to replicate the 5G IoT network infrastructure digitally. Hyper-5G project interconnects two geographically distributed edge-cloud infrastructures, i.e., Grid5000 in Europe and Chameleon cloud in the US, to assess the feasibility of deploying new 5G IoT services using the twin. Hyper-5G offers an open European platform to experiment with different IoT scenarios, ranging from smart agriculture to healthcare, connected cars, etc. In the USA, Hyper-5G deploys the DT Hub inside the Chameleon cloud, connected to CHI-Edge IoT testbed, to enable emulating real-world IoT scenarios such as connected robots, smart cities, and smart grids, etc.
{"title":"Hyper-5G: A Cross-Atlantic Digital Twin Testbed for Next Generation 5G IoT Networks and Beyond","authors":"Akram Hakiri, Sadok Ben, Gokhale Aniruddha","doi":"10.1109/ISORC58943.2023.00041","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00041","url":null,"abstract":"This paper introduces the Hyper-5G research project, which aims at developing and evaluating an experimental proof of concept of a cross-Atlantic Network Digital Twin for the future wireless mobile 5G and beyond (B5G). Hyper-5G project brings innovative capabilities to allow distributed twins to replicate the 5G IoT network infrastructure digitally. Hyper-5G project interconnects two geographically distributed edge-cloud infrastructures, i.e., Grid5000 in Europe and Chameleon cloud in the US, to assess the feasibility of deploying new 5G IoT services using the twin. Hyper-5G offers an open European platform to experiment with different IoT scenarios, ranging from smart agriculture to healthcare, connected cars, etc. In the USA, Hyper-5G deploys the DT Hub inside the Chameleon cloud, connected to CHI-Edge IoT testbed, to enable emulating real-world IoT scenarios such as connected robots, smart cities, and smart grids, etc.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133227000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/ISORC58943.2023.00037
S. Salman, Van-Lan Dao, A. Papadopoulos, S. Mubeen, T. Nolte
The edge computing paradigm brings the capabilities of the cloud such as on-demand resource availability to the edge for applications with low-latency and real-time requirements. While cloud-native load balancing and scheduling algorithms strive to improve performance metrics like mean response times, real-time systems, that govern physical systems, must satisfy deadline requirements. This paper explores the potential of an edge computing architecture that utilizes the on-demand availability of computational resources to satisfy firm real-time requirements for applications with stochastic execution and inter-arrival times. As it might be difficult to know precise execution times of individual jobs prior to completion, we consider an admission policy that relies on single-bit execution time predictions for dispatching. We evaluate its performance in terms of the number of jobs that complete by their deadlines via simulations. The results indicate that the prediction-based admission policy can achieve reasonable performance for the considered settings.
{"title":"Scheduling Firm Real-time Applications on the Edge with Single-bit Execution Time Prediction","authors":"S. Salman, Van-Lan Dao, A. Papadopoulos, S. Mubeen, T. Nolte","doi":"10.1109/ISORC58943.2023.00037","DOIUrl":"https://doi.org/10.1109/ISORC58943.2023.00037","url":null,"abstract":"The edge computing paradigm brings the capabilities of the cloud such as on-demand resource availability to the edge for applications with low-latency and real-time requirements. While cloud-native load balancing and scheduling algorithms strive to improve performance metrics like mean response times, real-time systems, that govern physical systems, must satisfy deadline requirements. This paper explores the potential of an edge computing architecture that utilizes the on-demand availability of computational resources to satisfy firm real-time requirements for applications with stochastic execution and inter-arrival times. As it might be difficult to know precise execution times of individual jobs prior to completion, we consider an admission policy that relies on single-bit execution time predictions for dispatching. We evaluate its performance in terms of the number of jobs that complete by their deadlines via simulations. The results indicate that the prediction-based admission policy can achieve reasonable performance for the considered settings.","PeriodicalId":281426,"journal":{"name":"2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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