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检索条件"任意字段=Applied Computing 2004 - Proceedings of the 2004 ACM Symposium on Applied Computing"

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An End-to-end Multi-task Object Detection using Embedded GPU in Autonomous Driving 22

An End-to-end Multi-task Object Detection using Embedded GPU...

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22nd International symposium on Quality Electronic Design (ISQED)

作者： Zhou, Shanglin Xie, Mimi Jin, Yufang Miao, Fei Ding, Caiwen Univ Connecticut Storrs CT 06269 USA Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9781728176413

Autonomous driving has gained popularity due to its high reliability compared to human drivers. Autonomous vehicles combine variety of sensors to perceive their surroundings, and use deep learning (DL) to extract complicated information from the sensing data. However, there are several challenges: Many DL models have explosive model sizes, and therefore not only time consuming but also power consuming when implementing on embedded systems on vehicles, further degrading the battery life-cycle. The current on-board AI treats lane detection and car location separately. In this paper, we propose an end-to-end multi-task environment detection framework. We fuse the 3D point cloud object detection model and lane detection model, with model compression technique applied. As on-board sensors forward information to the multi-task network, it not only parallel two detection tasks to extract combination information, but also reduces entire running time of the DL model. Experiments show by adding the model compression technique, the running speed of multi-task model improves more than 2x. Also, running time of lane detection model on Nvidia Jetson TX2 is almost 6x less comparing with running on CPU, which shows reasonableness of using embedded AI computing device on autonomous vehicle.

关键词： 3D Object Detection LIDAR Point Cloud Lane Detection Weight Pruning Embedded computing Device

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TrustEV: Trustworthy Electric Vehicle Charging and Billing 20

TrustEV: Trustworthy Electric Vehicle Charging and Billing

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35th Annual acm symposium on applied computing (SAC)

作者： Fuchs, Andreas Kern, Dustin Krauss, Christoph Zhdanova, Maria Fraunhofer SIT Darmstadt Germany

ISBN: (纸本)9781450368667

In the emerging Electric Vehicle (EV) charging infrastructure, vehicles authenticate themselves for charging and billing using Plug-and-Charge (PnC) protocols such as ISO 15118 and the respective cryptographic credentials stored in the vehicle. These credentials are an attractive target for adversaries and must be protected against illegitimate access and misuse. However, PnC standards currently demand only some basic protection of the data transfer and do not specify any system security requirements. In this paper, we propose TrustEV, a security architecture for secure provisioning, storage and usage of ISO 15118 credentials in an EV based on the Trusted Platform Module (TPM) 2.0. Our approach can be integrated into ISO 15118 requiring only minimal changes in the protocol's standard and is compatible with its second edition ISO 15118-20. It also assures backwards compatibility in case a component does not support TrustEV. We implemented and evaluated TrustEV to show the feasibility of our approach.

关键词： ISO 15118 Trusted computing TPM

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Mining Data on computing Majors Knowledge Game 2022

Mining Data on Computing Majors Knowledge Game

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proceedings of the 53rd acm Technical symposium on Computer Science Education V. 2

作者： Sam Thach Cecily Heiner Oregon Institute of Technology Klamath Falls OR USA

ISBN: (纸本)9781450390712

computing as a discipline is still very young, and there is much confusion about distinguishing the majors within the discipline. In the early 2000's, paper surveys were administered to distinguish between five computing majors: Computer Science (CS), Information Technology (IT), Information Systems (IS), Computer Engineering (CE), and Software Engineering (SE). Since then, Data Science, Cybersecurity, and other new majors have joined the computing discipline. We explored participants' current knowledge of modern computing majors at our institution via a modern web-based word cloud matching game. Word clouds were created by scraping Oregon Institute of Technology course descriptions for computing majors: applied mathematics (Math), data science (Math), cybersecurity (Business Management), and Computer Systems Engineering Technology (CSET) majors including embedded systems engineering technology, software engineering technology, and computing engineering technology. We invited 97 incoming freshmen to participate; 26 did. Our results showed that computing majors within departments are more difficult for participants to distinguish than computing majors across departments. Majors within the CSET department were most varied in results with embedded systems technology being the most confusing to participants. The applied mathematics major in the mathematics department was the easiest for participants to distinguish. Limitations of our work include a small sample and computing disciplines specific to our institution. Our contributions include an automated way to generate major descriptions using existing resources (catalog entries), an updated approach to collecting data that may be more motivating for students, and an updated list of computing majors including the emerging majors Data Science and Cybersecurity.

关键词： data science catalog parsing computing majors cybersecurity

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On Making Xen Detect Hypercalls and Memory Accesses for Simulating Virtualization-Enabled Processors 20

On Making Xen Detect Hypercalls and Memory Accesses for Simu...

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35th Annual acm symposium on applied computing (SAC)

作者： Mhatre, Swapneel C. Chandran, Priya Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut Kerala India

ISBN: (纸本)9781450368667

Due to the widespread use of cloud-based systems that are shared by multiple users, new processors are enhanced for security in virtualization. However, it is a challenging task to develop simulators for such processors, mainly, due to the lack of relevant information at the application level for computing performance parameters. This paper proposes a methodology to extract the required information from the hypervisor and transfer it to the hardware-based simulator for simulation of such processors. The paper proposes an approach to detect hypercalls made by the guest operating system during the execution of the guest application program on Xen hypervisor. This paper considers five hypercalls, viz., mmu_update, memory_op, update_va_mapping, grant_table_op and mmuext_op. The results show that for the guest application executed, among these five hypercalls, mmu_update is called the maximum number of times, while memory_op is called the minimum number of times. The paper also proposes an approach to signal the memory accesses by Xen on the communications (COM) port. This signal is observed on the cathode ray oscilloscope (CRO) and may be utilized for simulation. This research is valuable as it concludes that virtualization-enabled processors may be effectively simulated by extracting the required information from the hypervisor.

关键词： Cloud computing Virtualization Security Simulation Computer Architecture

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An Overview of Reference Architectures for Cloud of Things 20

An Overview of Reference Architectures for Cloud of Things

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35th Annual acm symposium on applied computing (SAC)

作者： Dias, Diogenes Delicato, Flavia C. Pires, Paulo F. Rocha, Atslands R. Nakagawa, Elisa Y. Univ Sao Paulo Sao Carlos Brazil Fluminense Fed Univ Niteroi RJ Brazil Fed Univ Ceara UFC Fortaleza Ceara Brazil

ISBN: (纸本)9781450368667

Cloud of Things (CoT) is a new paradigm that explores the synergy between Internet of Things (IoT) and cloud computing and has increasingly promoted the development of complex software intensive systems in diverse and even critical application domains. In parallel, reference architectures have contributed as a means to capture the knowledge of how to develop, standardize, and evolve software-intensive systems and, as a consequence, have promoted quality issues in different domains. However, there is still a lack of investigation on reference architectures for CoT, which could be impacting the lack of standardization in CoT systems. Hence, the main contribution of this paper is to present an overview of reference architectures for CoT and, for this, we conducted a systematic mapping study (SMS) and found eight architectures proposed quite recently in different application domains. As a result, these architectures have advanced mainly in terms of standardization, but they have not still large dissemination and wider adoption by the software industry. Several open issues can be pointed out, including actions to promote the sustainability of these architectures.

关键词： Internet of Things Cloud computing Cloud of Things Reference Architecture Systematic Mapping Study

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Evaluation of federated learning aggregation algorithms: Application to human activity recognition 20

Evaluation of federated learning aggregation algorithms: App...

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2020 acm International Joint Conference on Pervasive and Ubiquitous computing and 2020 acm International symposium on Wearable Computers, UbiComp/ISWC 2020

作者： Ek, Sannara Portet, François Lalanda, Philippe Vega, German Grenoble Alpes University France

ISBN: (纸本)9781450380768

Pervasive computing promotes the integration of connected electronic devices in our living spaces in order to assist us through appropriate services. Two major developments have gained significant momentum recently: a better use of fog resources and the use of AI techniques. Specifically, interest in machine learning approaches for engineering applications has increased rapidly. \ This paradigm seems to fit the pervasive environment well. However, federated learning has been applied so far to specific services and remains largely conceptual. It needs to be tested extensively on pervasive services partially located in the fog. In this paper, we present experiments performed in the domain of Human Activity Recognition on smartphones in order to evaluate existing algorithms. © 2020 acm.

关键词： Edge computing

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DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 50th

DAHA: Accelerating GNN Training with Data and Hardware Aware...

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Li, Zhiyuan Jian, Xun Wang, Yue Shao, Yingxia Chen, Lei The Hong Kong University of Science and Technology Hong Kong Shenzhen Institute of Computing Sciences China Beijing University of Posts and Telecommunications China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou Hong Kong

Graph neural networks (GNNs) have been gaining a reputation for effective modeling of graph data. Yet, it is challenging to train GNNs efficiently. Many frameworks have been proposed but most of them suffer from high batch preparation cost and data transfer cost for mini-batch training. In addition, existing works have limitations on the device utilization pattern, which results in fewer opportunities for pipeline parallelism. In this paper, we present DAHA, a GNN training framework with data and hardware aware execution planning to accelerate end-to-end GNN training. We first propose a data and hardware aware cost model that is lightweight and gives accurate estimates on per-operation time cost for arbitrary input and hardware settings. Based on the cost model, we further explore the optimal execution plan for the data and hardware with three optimization strategies with pipeline parallelism: (1) group-based in-turn pipelining of batch preparation neural training to explore more optimization opportunities and prevent batch preparation bottlenecks;(2) data and hardware aware rewriting for intra-batch execution planning to improve computation efficiency and create more opportunities for pipeline parallelism;and (3) inter-batch scheduling to further boost the training efficiency. Extensive experiments demonstrate that DAHA can consistently and significantly accelerate end-to-end GNN training and generalize to different message-passing GNN models. © 2024, VLDB Endowment. All rights reserved.

关键词： Cost benefit analysis

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Using Application Knowledge to Reduce Cold Starts in FaaS Services 20

Using Application Knowledge to Reduce Cold Starts in FaaS Se...

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35th Annual acm symposium on applied computing (SAC)

作者： Bermbach, David Karakaya, Ahmet-Serdar Buchholz, Simon TU Berlin Berlin Germany Einstein Ctr Digital Future Mobile Cloud Comp Res Grp Berlin Germany

ISBN: (纸本)9781450368667

In Function-as-a-Service platforms (FaaS), which have become very popular lately, code is deployed in the unit of single functions and the cloud provider handles resource management. There, a key problem is the so-called cold start problem: when a request comes in and no idle container can be found for the execution of the target function, then a new container needs to provisioned. In that case, the request incurs an extra latency - the cold start latency. Recent work has largely focused on reducing the duration of cold starts. In this paper, we present three approaches, complementary to related work, that reduce the number of cold starts while treating the FaaS service as a black box. In the approaches, implemented as part of a lightweight choreography middleware, we use knowledge on the composition of functions to trigger cold starts and, thus, the provisioning of new containers before the application process invokes the respective function. In experiments on AWS Lambda and OpenWhisk, we could show that our approaches remove an average of about 40% (in some cases up to 80%) of all cold starts while causing only a small cost overhead.

关键词： Serverless computing Function-as-a-Service Cold Starts

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Onion: Identifying Incident-Indicating Logs for Cloud Systems 2021

Onion: Identifying Incident-Indicating Logs for Cloud System...

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29th acm Joint Meeting on European Software Engineering Conference and symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Zhang, Xu Xu, Yong Qin, Si He, Shilin Qiao, Bo Li, Ze Zhang, Hongyu Li, Xukun Dang, Yingnong Lin, Qingwei Chintalapati, Murali Rajmohan, Saravanakumar Zhang, Dongmei Microsoft Res Beijing Peoples R China Microsoft Azure Redmond WA USA Univ Newcastle Callaghan NSW Australia Microsoft 365 Redmond WA USA

ISBN: (纸本)9781450385626

In cloud systems, incidents affect the availability of services and require quick mitigation actions. Once an incident occurs, operators and developers often examine logs to perform fault diagnosis. However, the large volume of diverse logs and the overwhelming details in log data make the manual diagnosis process time-consuming and error-prone. In this paper, we propose Onion, an automatic solution for precisely and efficiently locating incident-indicating logs, which can provide useful clues for diagnosing the incidents. We first point out three criteria for localizing incident-indicating logs, i.e., Consistency, Impact, and Bilateral-Difference. Then we propose a novel agglomeration of logs, called log clique, based on which these criteria are satisfied. To obtain log cliques, we develop an incident-aware log representation and a progressive log clustering technique. Contrast analysis is then performed on the cliques to identify the incident-indicating logs. We have evaluated Onion using well-labeled log datasets. Onion achieves an average F1-score of 0.95 and can process millions of logs in only a few minutes, demonstrating its effectiveness and efficiency. Onion has also been successfully applied to the cloud system of Microsoft. Its practicability has been confirmed through the quantitative and qualitative analysis of the real incident cases.

关键词： Log analysis Incident-indicating logs identification Cloud computing Fault diagnosis

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Resource Allocation for Edge computing with Multiple Tenant Configurations 20

Resource Allocation for Edge Computing with Multiple Tenant ...

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35th Annual acm symposium on applied computing (SAC)

作者： Araldo, Andrea Di Stefano, Alessandro Di Stefano, Antonella Telecom SudParis Inst Polytech Paris Paris France Univ Catania Dept Elect Elect & Informat Engn Catania Italy

ISBN: (纸本)9781450368667

Edge computing (EC) consists in deploying computational resources, e.g., memory, CPUs, at the Edge of the network, e.g., base stations, access points, and run there a part of the computation currently running on the Cloud. This approach promises to reduce latency, inter-domain traffic and enhance user experience. Since resources at the Edge are scarce, resource allocation is crucial for EC. While most of the studies assume users interact directly with the Edge submitting a sequence of tasks, we instead consider that users will interact with different Service Providers (SPs), as they currently do in the Web. We therefore consider the case of a Network Operator (NO) that owns the resources at the Edge and must decide how much resource to allocate to the different tenants (SPs). We propose MORA, a polynomial time strategy which allows the NO to maximize its utility, which can be inter-domain traffic savings, improved users' QoE or other metrics of interest. The core of MORA is that (i) it exploits service elasticity, i.e., the fact that services can adapt to the resources allocated by the NO and rely on a remote Cloud for the excess of computation, (ii) it is suitable for micro-services architecture, which decomposes a single service in a set of components, which MORA places in the different computational nodes of the Edge and (iii) it copes with multi-dimensional resources, e.g., memory and CPUs. After analyzing the properties of the algorithm, we show numerically that it performs close to the optimum. To guarantee reproducibility, the numerical evaluation is performed on publicly available traces from Google and Alibaba clusters and in synthetic scenarios and our code is open source.

关键词： Cloud computing Edge computing Resource allocation Container systems Network optimization

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