2019 IEEE Real-Time Systems Symposium (RTSS)最新文献_第4页

Work-in-Progress: Real-Time RPC for Hybrid Dual-OS System 正在进行的工作:混合双操作系统的实时RPC

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00057

Pan Dong, Zhe Jiang, A. Burns, Y. Ding, Jun Ma

{"title":"Work-in-Progress: Real-Time RPC for Hybrid Dual-OS System","authors":"Pan Dong, Zhe Jiang, A. Burns, Y. Ding, Jun Ma","doi":"10.1109/RTSS46320.2019.00057","DOIUrl":"https://doi.org/10.1109/RTSS46320.2019.00057","url":null,"abstract":"For the power and space sensitive systems such as automotive/avionic computers, an important trend is isolating and integrating multiple Operating Systems (OSs) in one physical platform, which is named as hybrid multi-OS system. Generally, in a commonly used hybrid dual-OS system, a RTOS (realtime operating system) and a GPOS (general-purpose operating system) are integrated. Cooperation (among the OSs) is a vital feature of a hybrid system to obtain the necessary capabilities, and inter-OS communication is the key. However, it is difficult to satisfy the real-time metrics of inter-OS communication required by the RTOS, due to the uncertainty in communication maintenance and the time-sharing policy of the GPOS. This paper aims to build a time predictable and secure RPC mechanism (i.e., the primary and critical communication unit in a hybrid multi-OS system). Afterwards, a real-time RPC scheme (termed RTRGRPC) is proposed, which is applied to a ready-built TrustZonebased hybrid dual-OS system (i.e., TZDKS). RTRG-RPC achieves accurate time control through three mechanisms: SGI message transforming, interrupt handler RPC servicing, and priorityswapping. Evaluations show that RTRG-RPC can achieve realtime predictability and can also reduce priority inversion.","PeriodicalId":102892,"journal":{"name":"2019 IEEE Real-Time Systems Symposium (RTSS)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121291150","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}

引用次数: 0

Work-in-Progress: Reducing Response Time of Static Priority Task Sets by Varying Offsets 正在进行的工作:通过改变偏移量减少静态优先级任务集的响应时间

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00072

Aaron Wong, A. Cheng

引用次数: 1

Work-in-Progress: Simplifying CPS Development with Real-Time Virtual Resources 正在进行的工作:使用实时虚拟资源简化CPS开发

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00065

A. Cheng

{"title":"Work-in-Progress: Simplifying CPS Development with Real-Time Virtual Resources","authors":"A. Cheng","doi":"10.1109/RTSS46320.2019.00065","DOIUrl":"https://doi.org/10.1109/RTSS46320.2019.00065","url":null,"abstract":"The specification, design, prototyping, analysis, implementation, information management, verification, privacy and security guarantees, safety assurance, and maintenance of cyber-physical systems (CPS) are extremely complex, owing to the multitude of operating systems, software components, hardware platforms, communication infrastructures, sensors and activators, human-machine interfaces, and numerous intertwined feedback loops. This paper describes a project to simplify all these life-cycle phases of developing and maintaining CPS by introducing Real-Time Virtual Resources (RTVR). RTVR forms a virtual layer between application software components and physical resources consisting of hardware platforms, communication infrastructures, and sensors and activators so that the software components can be implemented without knowledge of the details of the physical resources and thus can be ported from one physical resource into another with ease. Such open systems make it easy to add and remove software applications and reduce implementation cost when compared to systems which physically assign distinct computing resources to run different applications. However, most existing virtualization schemes significantly under-utilize the underlying physical resources in order to maintain the schedulability of real-time tasks as if they were scheduled on dedicated physical resources. Also, these schemes are not transparent to the software applications in that they need to be aware of each other and modification of the software may be necessary. Our proposed RTVR based on the Regularity-based Resource Partition (RRP) Model overcomes the above shortcomings, making it a true contender in simplifying all phases of CPS development and maintenance. This paper outlines the first of four project tasks to be performed: the specification, design, prototyping, analysis, implementation, verification, and maintenance of CPS with RTVR.","PeriodicalId":102892,"journal":{"name":"2019 IEEE Real-Time Systems Symposium (RTSS)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124352192","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}

引用次数: 1

EdgeBatch: Towards AI-Empowered Optimal Task Batching in Intelligent Edge Systems EdgeBatch:在智能边缘系统中实现ai授权的最优任务批处理

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00040

D. Zhang, Nathan Vance, Yang Zhang, Md. Tahmid Rashid, Dong Wang

{"title":"EdgeBatch: Towards AI-Empowered Optimal Task Batching in Intelligent Edge Systems","authors":"D. Zhang, Nathan Vance, Yang Zhang, Md. Tahmid Rashid, Dong Wang","doi":"10.1109/RTSS46320.2019.00040","DOIUrl":"https://doi.org/10.1109/RTSS46320.2019.00040","url":null,"abstract":"Modern Internet of Things (IoT) systems are increasingly leveraging deep neural networks (DNNs) with the goal of enabling intelligence at the edge of the network. While applying DNNs can greatly improve the accuracy of autonomous decisions and inferences, a significant challenge is that DNNs are traditionally designed and developed for advanced hardware (e.g., GPU clusters) and can not easily meet the real time requirements when deployed in a resource-constrained edge computing environment. While many systems have been proposed to facilitate deep learning at the edge, a key limitation lies in the under-utilization of the parallelizable GPU resources of edge nodes (e.g., IoT devices). In this paper, we propose EdgeBatch, a collaborative intelligent edge computing framework that minimizes the delay and energy consumption of executing DNN tasks at the edge by sharing idle GPU resources among privately owned IoT devices. EdgeBatch develops 1) a stochastic task batching mechanism that identifies the optimal batching strategy for the GPUs on IoT devices given uncertain task arrival times, and 2) a dynamic task offloading scheme that coordinates the collaboration among edge nodes to optimize the utilization of idle GPU resources in the system. We implemented EdgeBatch on a real-world edge computing testbed that consists of heterogeneous IoT devices (Jetson TX2, TX1, TK1, and Raspberry Pi3s). The results show that EdgeBatch achieved significant performance gains in terms of both the end-to-end delay and energy savings compared to the state-of-the-art baselines.","PeriodicalId":102892,"journal":{"name":"2019 IEEE Real-Time Systems Symposium (RTSS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411294","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}

引用次数: 29

Suspension-Based Locking Protocols for Parallel Real-Time Tasks 并行实时任务的基于挂起的锁定协议

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00033

Xu Jiang, Nan Guan, Yue Tang, Weichen Liu, H. Duan

引用次数: 8

Hiding DRAM Refresh Overhead in Real-Time Cyclic Executives 在实时循环执行器中隐藏DRAM刷新开销

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.9199011

Xing Pan, F. Mueller

引用次数: 2

SmartPC: Hierarchical Pace Control in Real-Time Federated Learning System SmartPC:实时联邦学习系统中的分层速度控制

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00043

Li Li, Haoyi Xiong, Zhishan Guo, Jun Wang, Chengzhong Xu

{"title":"SmartPC: Hierarchical Pace Control in Real-Time Federated Learning System","authors":"Li Li, Haoyi Xiong, Zhishan Guo, Jun Wang, Chengzhong Xu","doi":"10.1109/RTSS46320.2019.00043","DOIUrl":"https://doi.org/10.1109/RTSS46320.2019.00043","url":null,"abstract":"Federated Learning is a technique for learning AI models through the collaboration of a large number of resourceconstrained mobile devices, while preserving data privacy. Instead of aggregating the training data from devices, Federated Learning uses multiple rounds of parameter aggregation to train a model, wherein the participating devices are coordinated to incrementally update a shared model with their own parameters locally learned. To efficiently deploy Federated Learning system over mobile devices, several critical issues including realtimeliness and energy efficiency should be well addressed. This paper proposes SmartPC, a hierarchical online pace control framework for Federated Learning that balances the training time and model accuracy in an energy-efficient manner. SmartPC consists of two layers of pace control: global and local. Prior to every training round, the global controller first oversees the status (e.g., connectivity, availability, and energy/resource remained) of every participating device, then selects qualified devices and assigns them a well-estimated virtual deadline for task completion. Within such virtual deadline, a statistically significant proportion (e.g., 60%) of the devices are expected to complete one round of their local training and model updates, while the overall progress of multi-round training procedure is kept up adaptively. On each device, a local pace controller then dynamically adjusts device settings such as CPU frequency so that the learning task is able to meet the deadline with the least amount of energy consumption. We performed extensive experiments to evaluate SmartPC on both Android smartphones and simulation platforms using well-known datasets. The experiment results show that SmartPC reduces up to 32:8% energy consumption on mobile devices and achieves a speedup of 2.27 in training time without model accuracy degradation.","PeriodicalId":102892,"journal":{"name":"2019 IEEE Real-Time Systems Symposium (RTSS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129100678","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}

引用次数: 36

Work-in-Progress: Version-Aware Video Caching Strategy for Multi-version VoD Systems 多版本视频点播系统的版本感知视频缓存策略

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00058

Huiyan Zhao, Zili Wu, Quan Wang, Jing Wang, Weizhan Zhang

{"title":"Work-in-Progress: Version-Aware Video Caching Strategy for Multi-version VoD Systems","authors":"Huiyan Zhao, Zili Wu, Quan Wang, Jing Wang, Weizhan Zhang","doi":"10.1109/RTSS46320.2019.00058","DOIUrl":"https://doi.org/10.1109/RTSS46320.2019.00058","url":null,"abstract":"Recently, many video-on-demand (VoD) providers store multiple versions of the same videos to offer multiple-quality video services with different bitrates to users, called as multi-version VoD. To decrease the start-up delay for users, it is a good idea to cache videos at caching server that is in close proximity. However, how to decide which versions of which videos should be cached and replaced in caching server is still one major challenge for multi-version VoD systems because of limited caching storage. In this paper, we propose a version-aware video caching strategy for multi-version VoD systems, which aims to reduce start-up delay and improve cache hit ratio. First, we take into account the transcoding delay among versions and transmit delay from content server to caching server to calculate version-aware caching profit when caching a certain version or multiple versions of a video. It is the basis for the following caching replacement algorithm. Second, we propose version-aware video caching (VaVC) algorithm to decide which versions of which videos will be replaced based on the version-aware caching profit dynamically. In this way, VaVC can reduce start-up delay and improve the cache hit ratio. Our simulation results have shown that VaVC outperforms the others in both the start-up delay and the cache hit ratio.","PeriodicalId":102892,"journal":{"name":"2019 IEEE Real-Time Systems Symposium (RTSS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128129249","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}

引用次数: 2

Work-in-Progress: Pricing Mechanism and Workload Scheduling to Optimize Social Welfare and Cost for Fog Computing Systems 进行中的工作:优化雾计算系统的社会福利和成本的定价机制和工作量调度

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00060

Niraj Kumar, Arijit Mondal

引用次数: 5

Reconciling Compiler Optimizations and WCET Estimation Using Iterative Compilation 使用迭代编译协调编译器优化和WCET估计

2019 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2019-12-01 DOI: 10.1109/RTSS46320.2019.00022

Mickaël Dardaillon, Stefanos Skalistis, I. Puaut, Steven Derrien

{"title":"Reconciling Compiler Optimizations and WCET Estimation Using Iterative Compilation","authors":"Mickaël Dardaillon, Stefanos Skalistis, I. Puaut, Steven Derrien","doi":"10.1109/RTSS46320.2019.00022","DOIUrl":"https://doi.org/10.1109/RTSS46320.2019.00022","url":null,"abstract":"Static Worst-Case Execution Time (WCET) estimation techniques operate upon the binary code of a program in order to provide the necessary input for schedulability analysis techniques. Compilers used to generate this binary code include tens of optimizations, that can radically change the flow information of the program. Such information is hard to be maintained across optimization passes and may render automatic extraction of important flow information, such as loop bounds, impossible. Thus, compiler optimizations, especially the sophisticated optimizations of mainstream compilers, are typically avoided. In this work, we explore for the first time iterative-compilation techniques that reconcile compiler optimizations and static WCET estimation. We propose a novel learning technique that selects sequences of optimizations that minimize the WCET estimate of a given program. We experimentally evaluate the proposed technique using an industrial WCET estimation tool (AbsInt aiT) over a set of 46 benchmarks from four different benchmarks suites, including reference WCET benchmark applications, image processing kernels and telecommunication applications. Experimental results show that WCET estimates are reduced on average by 20.3% using the proposed technique, as compared to the best compiler optimization level applicable.","PeriodicalId":102892,"journal":{"name":"2019 IEEE Real-Time Systems Symposium (RTSS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122331448","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}

引用次数: 4