標(biāo)題: Titlebook: Edge Intelligence; From Theory to Pract Javid Taheri,Schahram Dustdar,Shuiguang Deng Textbook 2023 The Editor(s) (if applicable) and The Au [打印本頁] 作者: intensify 時間: 2025-3-21 19:58
書目名稱Edge Intelligence影響因子(影響力)
書目名稱Edge Intelligence影響因子(影響力)學(xué)科排名
書目名稱Edge Intelligence網(wǎng)絡(luò)公開度
書目名稱Edge Intelligence網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Edge Intelligence被引頻次
書目名稱Edge Intelligence被引頻次學(xué)科排名
書目名稱Edge Intelligence年度引用
書目名稱Edge Intelligence年度引用學(xué)科排名
書目名稱Edge Intelligence讀者反饋
書目名稱Edge Intelligence讀者反饋學(xué)科排名
作者: 投射 時間: 2025-3-21 20:30
Containerized Edge Computing Platforms,ighting container use case scenarios. We review what a container engine is and what alternatives are available in the market. We also provide details on automated container management processes that free operators from tasks such as re-creating and scaling containers. We also elaborate how container作者: 意外 時間: 2025-3-22 03:29 作者: Hallmark 時間: 2025-3-22 07:10
AI/ML for Computation Offloading,th the aim to resolve latency and bandwidth bottlenecks. Edge computing brings computation closer to end users for improving network stability, as well as enabling task offloading for device terminals. Because designing efficient offloading mechanisms is complicated, due to their stringent real-time作者: CEDE 時間: 2025-3-22 09:36
AI/ML Data Pipelines for Edge-Cloud Architectures,ear added values into business scenarios. We will motivate how high-speed inter-regional networks and Internet of Things (IoT) devices enabled data processing in the edge-tier network as an effective solution for real-time processing of raw data produced by IoT devices. We will also elaborate on how作者: 讓你明白 時間: 2025-3-22 16:04
AI/ML on Edge,(caching, training, inference, and offloading) of edge intelligence, we first give a fundamental introduction to core concepts and analyze their current inevitable development processes. We will then focus on the overall workflow and architecture of the intelligent edge system and present general di作者: 讓你明白 時間: 2025-3-22 18:41
AI/ML for Service-Level Objectives,providers to define complex, high-level SLOs in an orchestrator-independent manner. SLO Scripts are created and introduced because most approaches focus on low-level SLOs that are closely related to resources (e.g., average CPU or memory usage) and thus are usually bound to specific elasticity contr作者: Cardioversion 時間: 2025-3-22 21:23 作者: sinoatrial-node 時間: 2025-3-23 02:48
Roland Benedikter,Verena Nowotnyge computing platforms. Elaborating on how AI/ML technologies can deliver more accurate offloading strategies while lowering the computing decision-making costs, we will cover long-term optimization and Markov decision optimization for binary offloading, partial offloading, and complex jobs’ offloading problems.作者: GEM 時間: 2025-3-23 05:50
AI/ML for Computation Offloading,ge computing platforms. Elaborating on how AI/ML technologies can deliver more accurate offloading strategies while lowering the computing decision-making costs, we will cover long-term optimization and Markov decision optimization for binary offloading, partial offloading, and complex jobs’ offloading problems.作者: 注入 時間: 2025-3-23 09:52
Containerized Edge Computing Platforms, orchestration helps in managing container networking and storage functions. We will discuss the Kubernetes orchestration platform, which is currently the most widely used container orchestrator, and provide a high-level overview of the Kubernetes API for various programming languages.作者: 炸壞 時間: 2025-3-23 15:50 作者: 嬰兒 時間: 2025-3-23 21:15 作者: 火車車輪 時間: 2025-3-23 23:57
ecially deep reinforcement learning methods, help in solving the job scheduling problems in the running mode of services. Finally, we study the sample-and-learning framework for the load balancing problems in the operation mode of services.作者: 傷心 時間: 2025-3-24 05:39 作者: 手術(shù)刀 時間: 2025-3-24 09:42 作者: 等待 時間: 2025-3-24 12:44 作者: 休息 時間: 2025-3-24 18:32 作者: BILL 時間: 2025-3-24 19:23 作者: Adrenal-Glands 時間: 2025-3-25 01:27
AI/ML on Edge,s, followed by demonstrating a typical case example on federated learning. For model inference on the edge, we will provide various lightweight/simplified networks suitable for resource constraints and introduce inference learning models suitable for edge devices.作者: acquisition 時間: 2025-3-25 03:29
AI/ML concepts can benefit from Edge Computing platforms.Com.This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. It starts from basics and gradually advances, step-b作者: 終點 時間: 2025-3-25 08:21
Javid Taheri,Schahram Dustdar,Shuiguang DengIdeally suited for lecturing Edge Computing and its ties to AI and ML approaches.Starts from basics and advances, step-by-step, to ways how AI/ML concepts can benefit from Edge Computing platforms.Com作者: stroke 時間: 2025-3-25 15:26 作者: 燦爛 時間: 2025-3-25 19:32 作者: Heart-Rate 時間: 2025-3-25 20:13 作者: Blanch 時間: 2025-3-26 04:05
are concurrently executed on multiple computing tiers: cloud, fog, edge, and IoT. This simple idea develops many challenges due to the inherent complexity of their underlying infrastructures, to the extent that current methodologies for managing Internet-distributed systems would no longer be appro作者: 都相信我的話 時間: 2025-3-26 07:55
ighting container use case scenarios. We review what a container engine is and what alternatives are available in the market. We also provide details on automated container management processes that free operators from tasks such as re-creating and scaling containers. We also elaborate how container作者: 尊嚴(yán) 時間: 2025-3-26 09:37 作者: 名次后綴 時間: 2025-3-26 13:56 作者: 傀儡 時間: 2025-3-26 18:47
ear added values into business scenarios. We will motivate how high-speed inter-regional networks and Internet of Things (IoT) devices enabled data processing in the edge-tier network as an effective solution for real-time processing of raw data produced by IoT devices. We will also elaborate on how作者: 技術(shù) 時間: 2025-3-26 22:55 作者: 進取心 時間: 2025-3-27 03:58 作者: aerobic 時間: 2025-3-27 07:29
ngedge computing platforms. Chapter 5 introduces AI/ML pipelines to efficiently process generated data on the edge. Chapter 6 introduces ways to implement AI/ML systems on the edge and ways to deal with their t978-3-031-22154-5978-3-031-22155-2作者: 別炫耀 時間: 2025-3-27 12:28 作者: formula 時間: 2025-3-27 14:32
AI/ML for Service-Level Objectives,nt SLO controller for periodically evaluating SLOs and triggering elasticity strategies. We evaluate SLO Script and our middleware by implementing a motivating use case, featuring a cost efficiency SLO for an application deployed on Kubernetes.作者: GIST 時間: 2025-3-27 18:48
Textbook 2023eper in the use of AI/ML and introduces ways to optimize spreading computational tasks alongedge computing platforms. Chapter 5 introduces AI/ML pipelines to efficiently process generated data on the edge. Chapter 6 introduces ways to implement AI/ML systems on the edge and ways to deal with their t作者: 激勵 時間: 2025-3-27 22:33
10樓作者: Mangle 時間: 2025-3-28 04:05
10樓作者: 脾氣暴躁的人 時間: 2025-3-28 07:32
10樓作者: Infuriate 時間: 2025-3-28 14:13
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