2017 IEEE Real-Time Systems Symposium (RTSS)最新文献_第4页

REC: Predictable Charging Scheduling for Electric Taxi Fleets 电动出租车车队的可预测充电计划

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00034

Zheng Dong, Cong Liu, Yanhua Li, Jie Bao, Y. Gu, T. He

{"title":"REC: Predictable Charging Scheduling for Electric Taxi Fleets","authors":"Zheng Dong, Cong Liu, Yanhua Li, Jie Bao, Y. Gu, T. He","doi":"10.1109/RTSS.2017.00034","DOIUrl":"https://doi.org/10.1109/RTSS.2017.00034","url":null,"abstract":"Due to the energy security concern, our society is witnessing a surge of EV fleet applications, e.g., public EV taxi fleet systems. A major issue impeding an even more widespread adoption of EVs is range anxiety, which is due to several factors including limited battery capacity, limited availability of battery charging stations, and long charging time compared to traditional gasoline vehicles. By analyzing our accessible real-world EV taxi system-wide datasets, we observe that current EV taxi drivers often suffer from unpredictable, long waiting times at charging stations, due to temporally and spatially unbalanced utilization among charging stations. This is mainly because current taxi fleet management system simply rely on taxi drivers to make charging decisions. In this paper, In this paper, we develop REC, a Real-time Ev Charging scheduling framework for EV taxi fleets, which informs each EV taxi driver at runtime when and where to charge the battery. REC is able to analytically guarantee predictable and tightly bounded waiting times for all EVs in the fleet and temporally/spatially balanced utilization among charging stations, if each driver follows the charging decision made by REC. Moreover, REC can further efficiently handle real-life issues, e.g., allowing a taxi driver to charge at its preferred charging station while still guaranteeing balanced charging station utilization.We have extensively evaluated REC using our accessible real-world EV taxi system-wide datasets. Experimental results show that REC is able to address the unpredictability and unbalancing issues existing in current EV taxi fleet systems, yielding predictable and tightly bounded waiting times, and equally important, temporally/spatially balanced charging station utilization.","PeriodicalId":407932,"journal":{"name":"2017 IEEE Real-Time Systems Symposium (RTSS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133960311","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: 41

Work-in-Progress: Networked Control of Autonomous Underwater Vehicles with Acoustic and Radio Frequency Hybrid Communication 正在进行的工作:自主水下航行器的网络控制与声学和射频混合通信

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00051

Mehrullah Soomro, Saeed Nourizadeh Azar, Ö. Gürbüz, A. Onat

{"title":"Work-in-Progress: Networked Control of Autonomous Underwater Vehicles with Acoustic and Radio Frequency Hybrid Communication","authors":"Mehrullah Soomro, Saeed Nourizadeh Azar, Ö. Gürbüz, A. Onat","doi":"10.1109/RTSS.2017.00051","DOIUrl":"https://doi.org/10.1109/RTSS.2017.00051","url":null,"abstract":"Underwater control applications, such as control of Autonomous Underwater Vehicles (AUV)s, have recently gained significant interest, and there is a growing demand for high-speed wireless communication between AUVs and base station. Acoustic communication provides low data rates and high propagation delays, both of which are not suitable for employing high control gains for the navigation of AUVs. On the other hand, Radio Frequency (RF) communication provides high data rate, but it is constrained by high attenuation due to high conductivity and permittivity of water resulting in a short working range. In this work, we propose an underwater networked control system with acoustic and RF hybrid communication, where an acoustic link is employed for long range communication and RF link is applied in close range. Our performance results indicate that the acoustic and RF hybrid communication system takes up to 38.5% less time to dock, up to 91% smaller steady state error and spends up to 39% lower communication energy as compared to the acoustic only system at the expense of up to 22.35% higher motive energy.","PeriodicalId":407932,"journal":{"name":"2017 IEEE Real-Time Systems Symposium (RTSS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114068676","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: 11

Work-in-Progress: Dealing with Aperiodic Tasks on Quasi-Partitioning Scheduling 在制品:准分区调度中的非周期任务处理

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00048

Flávia Maristela Santos Nascimento, George Lima, Ernesto Massa

引用次数: 0

Sustainability in Mixed-Criticality Scheduling 混合临界调度的可持续性

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00010

Zhishan Guo, S. Sruti, Bryan C. Ward, Sanjoy Baruah

引用次数: 6

An Exact and Sustainable Analysis of Non-preemptive Scheduling 非抢占调度的精确和可持续分析

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00009

M. Nasri, Björn B. Brandenburg

引用次数: 37

Awakening Power of Physical Layer: High Precision Time Synchronization for Industrial Ethernet 物理层唤醒力:工业以太网高精度时间同步

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00021

Kun Qian, Tong Zhang, Fengyuan Ren

{"title":"Awakening Power of Physical Layer: High Precision Time Synchronization for Industrial Ethernet","authors":"Kun Qian, Tong Zhang, Fengyuan Ren","doi":"10.1109/RTSS.2017.00021","DOIUrl":"https://doi.org/10.1109/RTSS.2017.00021","url":null,"abstract":"High-precision time synchronization is critical for nowadays industrial Ethernet systems. Most existing time synchronization mechanisms are implemented based on packet communication. This interaction pattern, however, greatly limits their synchronizing frequency. In order to achieve microsecond-level synchronization precision, expensive high-quality oscillator is necessary for maintaining low clock skew under this long synchronization period (usually several seconds). Furthermore, packet processing introduces many nondeterministic variances (e.g. network stack overhead), which needs to be carefully eliminated. In this paper, we propose the brand-new Industrial Time Protocol (ITP). We deploy the entire ITP in the physical layer, so it eliminates most time uncertainties caused by network stack processing. Furthermore, ITP leverages the InterFrame Gap (IFG), which is the inherent interval between any two Ethernet frames, to carry the synchronization message. With this novel design, ITP can synchronize peer devices at very high frequency without degrading the goodput. The accuracy of ITP is bounded by 16ns for two adjacent devices with only intrinsic cheap oscillator. Furthermore, our theoretical analysis deduces that ITP guarantees 16N-nanosecond accuracy for N-hop network. We implement ITP design with NetFPGA. Experiments show that ITP can provide about 76-nanosecond accuracy for #hops=16 network under severe congestions. In addition, the design of ITP is scalable. It only consumes about 0.67% of logic cells in the low-end FPGA for supporting every ITP-aware port increase.","PeriodicalId":407932,"journal":{"name":"2017 IEEE Real-Time Systems Symposium (RTSS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134088311","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 Paper: An Analysis of the Impact of Dependencies on Probabilistic Timing Analysis and Task Scheduling 在研论文:依赖性对概率时序分析和任务调度的影响分析

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00042

E. Mezzetti, J. Abella, Carles Hernández, F. Cazorla

引用次数: 1

Work-in-Progress: Maximizing Model Accuracy in Real-time and Iterative Machine Learning 正在进行的工作:在实时和迭代机器学习中最大化模型准确性

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00055

Rui Han, Fan Zhang, L. Chen, Jianfeng Zhan

{"title":"Work-in-Progress: Maximizing Model Accuracy in Real-time and Iterative Machine Learning","authors":"Rui Han, Fan Zhang, L. Chen, Jianfeng Zhan","doi":"10.1109/RTSS.2017.00055","DOIUrl":"https://doi.org/10.1109/RTSS.2017.00055","url":null,"abstract":"As iterative machine learning (ML) (e.g. neural network based supervised learning and k-means clustering) becomes more ubiquitous in our daily life, it is becoming increasingly important to complete model training quickly to support real-time decision making, while still achieving high model accuracy (e.g. low prediction errors) that is critical for profits of ML tasks. Motivated by the observation that the small proportions of accuracy-critical input data can contribute to large parts of model accuracy in many iterative ML applications, this paper introduces a system middleware to maximize model accuracy by spending the limited time budget on the most accuracy-related input data. To achieve this, our approach employs a fast method to divide the input data into multiple parts of similar points and represents each part with an aggregated data point. Using these points, it quickly estimates the correlations between different parts and model accuracy, thus allowing ML tasks to process the most accuracy-related parts first. We incorporate our approach with two popular supervised and unsupervised ML algorithms on Spark and demonstrate its benefits in providing high model accuracy under short deadlines.","PeriodicalId":407932,"journal":{"name":"2017 IEEE Real-Time Systems Symposium (RTSS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125334363","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

GPU Scheduling on the NVIDIA TX2: Hidden Details Revealed NVIDIA TX2上的GPU调度:隐藏的细节揭示

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00017

Tanya Amert, Nathan Otterness, Ming Yang, James H. Anderson, F. D. Smith

引用次数: 151

The Virtual Deadline Based Optimization Algorithm for Priority Assignment in Fixed-Priority Scheduling 固定优先级调度中基于虚拟截止日期的优先级分配优化算法

2017 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2017-12-01 DOI: 10.1109/RTSS.2017.00018

Yecheng Zhao, Haibo Zeng

{"title":"The Virtual Deadline Based Optimization Algorithm for Priority Assignment in Fixed-Priority Scheduling","authors":"Yecheng Zhao, Haibo Zeng","doi":"10.1109/RTSS.2017.00018","DOIUrl":"https://doi.org/10.1109/RTSS.2017.00018","url":null,"abstract":"This paper considers the problem of design optimization for real-time systems scheduled with fixed priority, where task priority assignment is part of the decision variables, and the timing constraints and/or objective function linearly depend on the exact value of task response times (such as end-to-end deadline constraints). The complexity of response time analysis techniques makes it difficult to leverage existing optimization frameworks and scale to large designs. Instead, we propose an efficient optimization framework that is three magnitudes (1,000×) faster than Integer Linear Programming (ILP) while providing solutions with the same quality. The framework centers around three novel ideas: (1) An efficient algorithm that finds a schedulable task priority assignment for minimizing the average worst-case response time; (2) The concept of Maximal Unschedulable Deadline Assignment (MUDA) that abstracts the schedulability conditions, i.e., a set of maximal virtual deadline assignments such that the system is unschedulable; and (3) A new optimization procedure that leverages the concept of MUDA and the efficient algorithm to compute it.","PeriodicalId":407932,"journal":{"name":"2017 IEEE Real-Time Systems Symposium (RTSS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115437667","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: 12