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检索条件"任意字段=IEEE International Conference on Service-Oriented Computing and Applications"

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Cross-layer Adaptable Contention Window for High-definition Map QoS Enhancement 20

Cross-layer Adaptable Contention Window for High-definition ...

引用

20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Redondo, Jeffrey Yuan, Zhenhui Aslam, Nauman Zhang, Juan Univ Northumbria Newcastle Upon Tyne Tyne & Wear England Univ Warwick Coventry W Midlands England

ISBN: (纸本)9798350361261;9798350361278

The adoption of High-Definition (HD) mapping applications represents a critical step towards achieving Level-5 autonomous driving, revolutionizing road safety and paving the way for unprecedented advancements in transportation technology. However, HD mapping imposes significant computational demands in processing the raw data generated by autonomous vehicle sensors. To mitigate this issue, researchers have opted to offload the data reducing the processing time. Unfortunately, the current de-facto standard ieee802.11p in Vehicular Adhoc Network (VANET) does not provide the best latency or throughput for applications with low latency and heavy data transfer requirements. This is because of the fixed Contention Window (CW). To address this problem, solutions have been developed to dynamically allocate the CW nowadays with the help of Machine Learning (ML) paradigms. Nonetheless, these solutions do not include a strategy to dynamically allocate an optimal CW per service type. Instead, they focus on sharing the wireless channel fairly. In this paper, we have developed a cross-layer Reinforcement Learning (RL) algorithm between the application and Medium Access Control (MAC) layer that allocates CW per service type. Results showed improvement with a different gap in the latency Cumulative Distribution Function (CDF) of 181%, 120%, 107%, and 119% for Voice, Video, HD Map, and Best-effort respectively compared to other different approaches.

关键词： HD Map latency throughput machine learning q-learning reinforcement learning fairness VANET Quality of service

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Securing AI Inference in the Cloud: Is CPU-GPU Confidential computing Ready? 17

Securing AI Inference in the Cloud: Is CPU-GPU Confidential ...

引用

ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Mohan, Apoorve Ye, Mengmei Franke, Hubertus Srivatsa, Mudhakar Liu, Zhuoran Gonzalez, Nelson Mimura IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA

ISBN: (纸本)9798350368543;9798350368536

Many applications have been offloaded onto cloud environments to achieve higher agility, access to more powerful computational resources, and obtain better infrastructure management. Although cloud environments provide solid security solutions, users with highly sensitive data or regulatory compliance requirements, such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation), still hesitate to move such application domains to the cloud. To address these concerns, cloud service providers have started to offer solutions to protect data confidentiality and integrity through trusted execution environments (TEEs). While so far these were limited to CPU TEEs only, NVIDIA's Hopper architecture has shifted the landscape by enabling confidential computing features essential to protecting confidentiality and integrity for real-world applications offloaded to GPUs, such as large language models (LLMs). However, there lacks a sufficient study on how much performance overhead confidential computing introduces in a TEE comprised of a CPU-GPU configuration. In this paper we evaluate a confidential computing environment comprised of an Intel TDX system and NVIDIA H100 GPUs through various micro benchmarks and real workloads including BERT, LLaMA, and Granite large language models and provide discussions on the overhead incurred by confidential computing when GPUs are utilized. We show that while LLMs are sensitive to the model types and batch sizes, when larger models with pipelined processing are deployed, the performance of LLM inference in CPU-GPU TEEs can be close to par with their non-confidential setups.

关键词： confidential computing cloud security cloud computing foundation models large language models high performance computing

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Proceedings - 18th ieee international conference on service-oriented System Engineering, SOSE 2024

Proceedings - 18th IEEE International Conference on Service-...

引用

18th ieee international conference on service-oriented System Engineering, SOSE 2024

ISBN: (纸本)9798331539580

The proceedings contain 13 papers. The topics discussed include: an efficient storage solution for cloud/edge computing infrastructures;state of practice and challenges of issue management for component-based and service-oriented systems: an empirical study;WIP: towards cloud-based wildland fire simulation service;a stable matching approach to energy efficient and sustainable serverless scheduling for the green cloud continuum;time series predictions for cloud workloads: a comprehensive evaluation;towards an API-driven approach for universal and lightweight cloud-edge orchestration;distributed model serving for real-time opinion detection;DLT-based verifiable receipt credentials for traceability in EV battery supply chains;and MCBA: a matroid constraint-based approach for composite service recommendation considering compatibility and diversity.

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Edge-LLM: A Collaborative Framework for Large Language Model Serving in Edge computing

Edge-LLM: A Collaborative Framework for Large Language Model...

引用

ieee international conference on Web services (ieee ICWS)

作者： Cai, Fenglong Yuan, Dong Yang, Zhe Cui, Lizhen Shandong Univ Sch Software Jinan Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C F Jinan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia

ISBN: (纸本)9798350368567;9798350368550

The rapid advancement and extensive implementation of Large Language Models (LLMs) are milestones in the realm of artificial intelligence. Although Parameter-Efficient Transfer Learning (PETL), a.k.a. Adapter, methods have reduced the barrier for fine-tuning and inference on LLMs, it becomes a challenge to efficiently deploy and fine-tuning different adapter models needed for massive AI applications. With the popularity of SoC chips, the computing power of edge devices has improved significantly. To meet the computational resources required by LLM applications and improve quality of service (QoS), we propose Edge-LLM, a server-node collaboration framework for large-scale language model serving, to efficiently utilize edge resources to accelerate LLM fine-tuning and inference in resource-constrained scenarios. In the framework, we implement an adaptive quantization strategy, FM cache mechanism, and value density first (VDF) scheduling algorithm to reduce GPU overhead and accelerate LLM computation. The experimental results demonstrate that Edge-LLM can significantly improve overall computational speed by a factor of 17, decrease the number of tasks experiencing timeouts by 63%, and reduce GPU overhead by up to 43%.

关键词： service large language model collaborative framework online scheduling algorithm edge computing

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Advanced Serverless Edge computing

Advanced Serverless Edge Computing

引用

21st international conference on service-oriented computing (ICSOC)

作者： Ticongolo, Inacio Gaspar Baresi, Luciano Quattrocchi, Giovanni Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Univ Eduardo Mondlane Dept Matemat & Informat Maputo Mozambique

ISBN: (纸本)9789819709885;9789819709892

Serverless computing is becoming an attractive means to implement applications on top of edge infrastructures. Developers break applications into small components (functions), and this modularity allows one to cope with the limited resources of edge nodes and meet the stringent response times typical of edge applications. Different frameworks already support serverless edge computing, that is, the management and operation of serverless applications on top of edge infrastructures, but they usually cope with the different problems in isolation: for example, function placement, dependency management, cold starts, data management, and resource allocation. In contrast, we claim that these aspects must be dealt with all together. This work borrows from NEPTUNE and aims to fill the gap. We plan to complement NEPTUNE with dependency-aware function placement and resource allocation, to tackle image instantiation and cold start mitigation, and to address data management. The first results on the use of function dependencies to amelio-rate resource allocation indicate significant improvements with respect to the state of the art.

关键词： serverless edge computing function dependencies resource allocation placement image instantiation cold start data management

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PlanX: A Toolbox for Building and Integrating AI Planning Systems 17

PlanX: A Toolbox for Building and Integrating AI Planning Sy...

引用

17th ieee international Congress on Intelligent and service-oriented Systems Engineering (CISOSE)

作者： Georgievski, Ilche Univ Stuttgart Serv Comp Dept IAAS Stuttgart Germany

ISBN: (纸本)9798350322392

We introduce the PlanX toolbox to support and promote building and integrating AI planning systems. The prototype toolbox is aimed at researchers and developers who need to build and use advanced AI planning systems to address planning problems. It is also well suited for composing AI planning systems that need to be integrated into larger software architectures. The toolbox offers an initial set of planning services realised using existing tools and our own implementations. The planning services are based on technologies that enable the resulting system to be easily set up and run on different platforms. We show on a use case that building and expanding an AI planning system with the PlanX toolbox is not only possible but also quick. PlanX is released as open-source to encourage its wider use and contributions to the code base.

关键词： AI Planning System Engineering service-oriented computing Toolbox

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Enabling Automated service Orchestration in a computing Continuum with User-Owned Devices 2

Enabling Automated Service Orchestration in a Computing Cont...

引用

2nd ieee international conference on Software services Engineering (SSE)

作者： Herrera, Juan Luis Berrocal, Javier Chen, Hsiao-Yuan Julien, Christine Univ Bologna Bologna Italy Univ Texas Austin Austin TX 78712 USA Univ Extremadura Badajoz Spain

ISBN: (纸本)9798350368529;9798350368512

The urgency in the adoption of the computing Continuum paradigm, which allows computing services to be deployed closer to users, requires a plethora of powerful, distributed devices that host such services. In this context, user-owned devices, such as phones or gaming devices, massively distributed by nature and experiencing a continuous growth in computing resources, are naturally fit to host services, and thus, their incorporation to the Continuum to provide the urgently needed infrastructural support is inevitable. In this future, two key challenges must be addressed: automating service orchestration across a massive number of devices, and ensuring device owners maintain agency over the circumstances and conditions under which services can be hosted on their devices and consumed by other users. As a first step towards this future, we present ATMOS, an automated service orchestration platform for integrating user-owned devices in the computing Continuum. ATMOS enforces user-defined policies, automatically adjusting the placement and replication of services across devices. The evaluation of ATMOS shows that it enforces all user policies, compared to state-of-the-art service orchestration systems, which violate up to 90.3% of user policies, with minimal impact on the experienced QoS.

关键词： computing Continuum service-oriented computing pervasive computing user-centric service orchestration

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Design and Development of Power Grid Digital Model Based on Artificial Intelligence 4

Design and Development of Power Grid Digital Model Based on ...

引用

4th ieee international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Li, Zhipeng Wang, Jun Shen, Shuo Li, Fupeng Zhang, Jianfeng State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Company Digital Division Lanzhou China

ISBN: (纸本)9798350359589

A microservices-oriented grid digital dispatching data service platform is proposed. It is a multi-level integration method of object-oriented, user and application. This paper realizes the separation of data processing business and exchange function. This method can effectively reduce the complexity and coupling of information exchange between service systems in power network scheduling. At the same time, this platform can also eliminate the differences between different manufacturers, different operating systems, different programming languages in different systems, and realizes the seamless docking of power grid data. © 2024 ieee.

关键词： grid digitization information integration platform information interaction service-oriented architecture (SOA) Smart grid

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Edge computing-Assisted Joint Offloading and Migration in Parking Lots Scenarios

Edge Computing-Assisted Joint Offloading and Migration in Pa...

引用

ieee/CIC international conference on Communications in China (ICCC)

作者： Du, Yanxi Zhang, Ke Lu, Yunlong Leng, Supeng He, Jianhua Univ Elect Sci & Technol China Sch Informat & Commun Engn Shenzhen Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen Peoples R China Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Univ Essex Colchester CO4 3SQ Essex England

ISBN: (纸本)9798350378412

Nowadays intelligent vehicles are equipped with increasing resources for computing intensive driving applications. When these vehicles are parked, their unused computing resources can form a large pool of edge computing resources and be provided to support other parked vehicles or mobile devices with needs of offloading computing tasks. While the parked vehicles based edge computing assistance presents a novel computing paradigm, there is a challenging problem of potential computing service disruption as the parked intelligent vehicles (PIVs) leave the parking lots. To address this issue, we propose a joint offloading and service migration framework for vehicular edge computing in parking lots. In the framework, unfinished tasks can be migrated to another PIV or remained until completion to avoid task failures, and the processing results will be transmitted back through multi-hop relays between vehicles. Considering computing resource requirements of tasks and vehicle movement characteristics, we formulate the problem as a Markov Decision Process (MDP) to minimize task completion delays, with offloading stability constraints established for improving service completion sustainability. Due to dynamic multi-user scenarios and heterogeneous vehicular computing capabilities, the problem is difficult to model and solve. We thus propose a joint offloading and migration decision algorithm (JOMD3) based on dueling double deep Q network. Numerical results demonstrate that the proposed framework and algorithm can achieve lower average delay and faster convergence in comparison to baseline algorithms.

关键词： Vehicular network task migration parked vehicle-assisted edge computing

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When Edge Meets FaaS: Opportunities and Challenges 7

When Edge Meets FaaS: Opportunities and Challenges

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7th ieee international conference on Edge computing and Communications (ieee EDGE) / ieee World Congress on services (serviceS)

作者： Jin, Runyu Yang, Qirui Zhao, Ming IBM Almaden Res Ctr San Jose CA 95120 USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9798350304831

The proliferation of edge devices and the rapid growth of IoT data have called forth the edge computing paradigm. Function-as-a-service (FaaS) is a promising computing paradigm to realize edge computing. This paper explores the feasibility and advantages of FaaS-based edge computing. It also studies the research challenges that should be addressed in the design of such systems, which are 1) the quick decomposing and recomposing of applications, 2) the trade-off between performance and isolation of sandbox mechanisms, and 3) distributed scheduling. The challenges are illustrated by evaluating existing FaaS-based edge platforms, AWS IoT Greengrass, and OpenFaaS.

关键词： Function-as-a-service Edge computing Cloud computing function

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