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Layer-aware Collaborative Microservice Deployment toward Maximal Edge Throughput 41

Layer-aware Collaborative Microservice Deployment toward Max...

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41st ieee conference on Computer Communications (ieee INFOCOM)

作者： Gu, Lin Chen, Zirui Xu, Honghao Zeng, Deze Li, Bo Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan Peoples R China China Univ Geosci Sch Comp Sci Wuhan Hubei Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781665458221

Lightweight container-based microservice has been widely advocated to promote the elasticity of edge cloud. The inherent layered structure of containers offers a compelling way to cope with the resource scarcity of edge servers through layer sharing, which can significantly increase storage utilization and improve the edge throughput. Recent studies show that it is possible to share layers not only within the same server but also between servers, which microservice deployment can take full advantage of. In this paper, we investigate the problem of how to collaboratively deploy microservices by incorporating both intra-server and inter-server layer sharing to maximize the edge throughput. We formulate this problem into an integer linear programming form and prove it as NP-hard. We propose a randomized rounding based heuristic algorithm, and conduct formal analysis on the guaranteed approximation ratio. Through extensive experiments, we verify the efficiency of our proposed algorithm, and the results demonstrate that it can deploy 6x and 12x more microservice instances, and improve the edge throughput by 27.74% and 38.46% in comparison with state-of-the-art strategies.

关键词： Heuristic algorithms conferences Microservice architectures Collaboration Integer linear programming Elasticity Containers

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PerfHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing

PerfHD: Efficient ViT Architecture Performance Ranking using...

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2023 ieee/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ma, Dongning Zhao, Pengfei Jiao, Xun Villanova University VillanovaPA19085 United States Beijing Xiaochuan Technology Co. Ltd. Beijing100191 China

ISBN: (纸本)9798350302493

Neural Architecture Search (NAS) aims at identifying the optimal network architecture for a specific need in an automated manner, which serves as an alternative to the manual process of model development, selection, evaluation and performance estimation. However, evaluating performance of candidate architectures in the search space during NAS, which often requires training and ranking a mass amount of architectures, is often prohibitively computation-demanding. To reduce this cost, recent works propose to estimate and rank the architecture performance with-out actual training or inference. In this paper, we present PerfHD, an efficient-while-accurate architecture performance ranking approach using hyperdimensional computing for the emerging vision transformer (ViT), which has demonstrated state-of-the-art (SOTA) performance in vision tasks. Given a set of ViT models, PerfHD can accurately and quickly rank their performance solely based on their hyper-parameters without training. We develop two encoding schemes for PerfHD, Gram-based and Record-based, to encode the features from candidate ViT architecture parameters. Using the VIMER-UFO benchmark dataset of eight tasks from a diverse range of domains, we compare PerfHD with four SOTA methods. Experimental results show that PerfHD can rank nearly 100K ViT models in about just 1 minute, which is up to 10X faster than SOTA methods, while achieving comparable or even superior ranking accuracy. We open-source PerfHD in PyTorch implementation at https://***/VU-DETAIL/PerfHD. © 2023 ieee.

关键词： network architecture

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Safety-Preserving Filters Against Stealthy Sensor and Actuator Attacks 62

Safety-Preserving Filters Against Stealthy Sensor and Actuat...

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62nd ieee conference on Decision and Control (CDC)

作者： Escudero, Cedric Murguia, Carlos Massioni, Paolo Zamai, Eric Univ Lyon Univ Claude Bernard Lyon 1 UMR5005CNRSINSA Lyon Ecole Cent LyonAmpere F-69621 Villeurbanne France Eindhoven Univ Technol Dept Mech Engn Dynam & Control Grp Eindhoven Netherlands

ISBN: (纸本)9798350301243

This article proposes a novel strategy based on control input filtering for mitigating the effects of deception attacks on control and sensor measurement signals. We assume an adversary who can tamper data transmitted from a communication network in order to degrade the plant performance. The proposed strategy consists on adding multiple-input multiple-output (MIMO) filters to the control loop, between the received control actions and the plant actuators. The filter's goal is to dynamically steer the reachable set induced by the attack signals to a safe region of the state space. The article provides a filter synthesis method under the form of a semidefinite programming problem, yielding such filters in a way that attack-free control signals are distorted as little as possible, and plant trajectories are contained in the safe set. At the end of the paper, a set of simulations demonstrate the effectiveness of the approach.

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Deep Single Shot Musical Instrument Identification using Scalograms 5

Deep Single Shot Musical Instrument Identification using Sca...

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5th International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Chatterjee, Debdutta Dutta, Arindam Sil, Dibakar Chandra, Aniruddha Natl Inst Technol Dept Elect & Commun Engn Durgapur 713209 India Indian Inst Sci Dept Computat & Data Sci Bangalore India Steradian Semicond Pvt Ltd Bangalore India

ISBN: (纸本)9781665456456

Musical instrument identification has for long had a reputation of being one of the most ill-posed problems in the field of musical information retrieval. Despite several robust attempts made at solving the problem, a timeline spanning over the last five odd decades, the problem remains an open conundrum. In this work, we take on a further complex version of the traditional problem, we attempt to solve the problem with minimal data available - one audio excerpt per class. We propose to use a convolutional Siamese network and a residual variant of the same to identify musical instruments based on the corresponding scalograms of their audio excerpts. Results obtained for two publicly available datasets validate our algorithm, achieving over 80% accuracy with only 5 sets of training data. Moreover, our proposed architectures work for both spectrograms as well as scalograms, and exhibit improvements, albeit marginal (approximate to 3%), for the later input class.

关键词： Audio excerpts scalogram one-shot learning convolutional Siamese network

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DNF: Decouple and Feedback network for Seeing in the Dark

DNF: Decouple and Feedback Network for Seeing in the Dark

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jin, Xin Han, Ling-Hao Li, Zhen Guo, Chun-Le Chai, Zhi Li, Chongyi Nankai Univ VCIP CS Tianjin Peoples R China Hisilicon Technol Co Ltd Shenzhen Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

The exclusive properties of RAW data have shown great potential for low-light image enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of existing architectures in both single-stage and multi-stage methods. Mixed mapping across two different domains, noise-to-clean and RAW-to-sRGB, misleads the single-stage methods due to the domain ambiguity. The multi-stage methods propagate the information merely through the resulting image of each stage, neglecting the abundant features in the lossy image-level dataflow. In this paper, we probe a generalized solution to these bottlenecks and propose a Decouple aNd Feedback framework, abbreviated as DNF. To mitigate the domain ambiguity, domain-specific subtasks are decoupled, along with fully utilizing the unique properties in RAW and sRGB domains. The feature propagation across stages with a feedback mechanism avoids the information loss caused by image-level dataflow. The two key insights of our method resolve the inherent limitations of RAW data-based low-light image enhancement satisfactorily, empowering our method to outperform the previous state-of-the-art method by a large margin with only 19% parameters, achieving 0.97dB and 1.30dB PSNR improvements on the Sony and Fuji subsets of SID.

关键词： Low-level vision

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Online Knowledge Distillation with History-Aware Teachers

Online Knowledge Distillation with History-Aware Teachers

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Li, Chengcheng Wang, Zi Qi, Hairong Univ Tennessee Knoxville TN USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

In this work, we propose a novel online knowledge distillation (OKD) approach, built upon the classical deep mutual learning framework in which peer networks (students) treat each other as teachers by learning from their predictions. The proposed method traces and leverages two levels of information encoded in each peer's learning trajectory to dynamically construct superior teachers to supervise other students. We first build a recurrent neural network associated with each peer, which takes both the network's current and previous logits as input and outputs integrated logits with the same dimension as the transferred knowledge. By doing so, the teachers provide an enhanced representation of knowledge. Beyond that, we also build a weight-averaged surrogate for each network, which maintains the exponential moving average of its learned parameters during the online training procedure. The proposed approach exploits the hidden information behind the online learning process instead of myopically learning from peers' outputs at a single time/iteration step. It potentially reduces uncertainties from peers as suffered in previous OKD studies with more stabilized transferred knowledge. We evaluate the proposed approach with benchmark image classification datasets and network architectures. Experimental results demonstrate its effectiveness with clear performance improvement over state-of-the-arts.

关键词： online knowledge distillation history-aware knowledge network-based OKD learning trajectory

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An Architecture-Independent CGRA Compiler enabling openMP Applications 36

An Architecture-Independent CGRA Compiler enabling OpenMP Ap...

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36th ieee International Parallel and Distributed Processing Symposium (ieee IPDPS)

作者： Kojima, Takuya Adhi, Boma Cortes, Carlos Tan, Yiyu Sano, Kentaro Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan RIKEN Ctr Computat Sci R CCS Kobe Hyogo Japan

ISBN: (纸本)9781665497473

Coarse-Grained reconfigurable architecture (CGRA) is a promising platform for HPC systems in the post-Moore's era. A single-source programming model is essential for practical heterogeneous computing. However, we do not have a canonical programming model and a frontend compiler for it. Existing versatile CGRAs, in respect to their execution model, computational capability, and system structure, magnify the difficulty of orchestrating the compiler techniques. It consequently forces designers of the CGRAs to develop the compiler from scratch, working only for their architectures. Such an approach is outdated, given other successful accelerators like GPU and FPGAs. This paper presents a new CGRA compiler framework in order to reduce development efforts of CGRA applications. openMP annotated codes are fed into the proposed compiler, as recent openMP support device offloading to the accelerators. This property improves the reusability of the existing source code for HPC workloads. The design of the compiler is inspired by LLVM, which is the most famous compiler framework so that the frontend is built to be architecture-independent. In this work, we demonstrate that the proposed compiler can handle different types of CGRAs without changing the source codes. In addition, we discuss the effect of architecture-independent optimization algorithms. We also provide an open-source implementation of the compiler framework at https://***/hal-lab-u-tokyo/CGRAOmp.

关键词： Codes Runtime Computational modeling Graphics processing units programming Heterogeneous networks Reconfigurable architectures

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Orchestration Procedures for the network Intelligence Stratum in 6G networks

Orchestration Procedures for the Network Intelligence Stratu...

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Joint European conference on networks and Communications / 6G Summit (EuCNC/6G Summit)

作者： Chatzieleftheriou, Livia Elena Gramaglia, Marco Camelo, Miguel Garcia-Saavedra, Andres Kosmatos, Evangelos Gucciardo, Michele Soto, Paola Iosifidis, George Fuentes, Lidia Garcia-Aviles, Gines Lutu, Andra Baldoni, Gabriele Fiore, Marco IMDEA Networks Inst Madrid Spain Univ Carlos III Madrid UC3M Madrid Spain UC3M Getafe Spain Univ Antwerp Imec Antwerp Belgium NEC Labs Europe Heidelberg Germany WINGS ICT Solut Nea Smirni Greece Delft Univ Technol Delft Netherlands Univ Malaga Malaga Spain i2CAT Fdn Barcelona Spain Telefonica Res Madrid Spain Zettascale Technol St Aubin France

ISBN: (纸本)9798350311020

The quest for autonomous mobile networks introduces the need for fully native support for network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stratum. The NI stratum is responsible for the full automation of the NI operation in the network, including the management of the life-cycle of NI algorithms, in a way that is synergic with traditional network management and orchestration framework. In this regard, the NI stratum must accommodate the unique requirements of NI algorithms, which differ from the ones of, e.g., virtual network functions, and thus plays a critical role in the native integration of NI into current network architectures. In this paper, we leverage the recently proposed concept of network Intelligence Orchestrator (NIO) to (i) define the specific requirements of NI algorithms, and (ii) discuss the procedures that shall be supported by an NIO sitting in the NI stratum to effectively manage NI algorithms. We then (iii) introduce a reference implementation of the NIO defined above using cloud-native open-source tools.

关键词： network Intelligence network Intelligence Orchestration Intelligence Plane

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Performance Evaluation of Heterogeneous GPU programming Frameworks for Hemodynamic Simulations

Performance Evaluation of Heterogeneous GPU Programming Fram...

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2023 International conference on High Performance Computing, network, Storage, and Analysis, SC Workshops 2023

作者： Martin, Aristotle Liu, Geng Ladd, William Lee, Seyong Gounley, John Vetter, Jeffrey Patel, Saumil Rizzi, Silvio Mateevitsi, Victor Insley, Joseph Randles, Amanda Biomedical Engineering Duke University DurhamNC United States Leadership Computing Facility Argonne National Laboratory LemontIL United States Computer Science and Mathematics Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798400707858

Preparing for the deployment of large scientific and engineering codes on upcoming exascale systems with GPU-dense nodes is made challenging by the unprecedented diversity of device architectures and heterogeneous programming models. In this work, we evaluate the process of porting a massively parallel, fluid dynamics code written in CUDA to SYCL, HIP, and Kokkos with a range of backends, using a combination of automated tools and manual tuning. We use a proxy application along with a custom performance model to inform the results and identify additional optimization strategies. At scale performance of the programming model implementations are evaluated on pre-production GPU node architectures for Frontier and Aurora, as well as on current NVIDIA device-based systems Summit and Polaris. Real-world workloads representing 3D blood flow calculations in complex vasculature are assessed. Our analysis highlights critical trade-offs between code performance, portability, and development time. © 2023 ACM.

关键词： Graphics processing unit

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Monitoring programming Styles in Massive open Online Courses Using Source Embedding 26

Monitoring Programming Styles in Massive Open Online Courses...

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26th International conference Information Visualisation (IV)

作者： Matsrostefano, Stefano Sciarrone, Filippo Tuscia Univ Fac Econ Via Santa Maria Gradi 4 Viterbo Italy Univ Mercatorum Fac Econ Piazza Mattei 10 Rome Italy

ISBN: (纸本)9781665490078

In recent years there has been an exponential growth of distance learning, provided by both public and private institutions. As a matter of fact, the number of students enrolled in courses delivered through the network, has dramatically grown, also due to the COVID-19 pandemic, which has forced millions of people not to move. Consequently, more and more courses delivered in a remote modality have been attended by a huge number of people, producing an increasing number of Massive open Online Courses (MOOC)s. These kind of courses are imposing new challenges for teachers, especially for monitoring and assessing the community learning processes. On the one hand, the learning assessment cannot be carried out based solely on closed-ended tests, while, on the other hand, teachers cannot evaluate thousands of open-answer assignments: they should have at their disposition a set of tools helping them monitor the community learning progress. This paper investigates the possibility of using some of the Source Code Embedding techniques, to give teachers useful information about their learners' programming styles in Massive open Online Courses. We propose a method to visualize each student's program, included the teacher's one, as a point in a 2-D space, using the doc2vec embeddings technique. Thanks to this representation, teachers can identify in the 2-D space groups of students having similar programming styles and reason on them to start a suitable didactic feedback. Moreover, teachers can reason on the relationship between each point compared to their own point as well, considered as the truth programming style. A first experimentation using Python as the programming language is performed with encouraging results.

关键词： Deep Learning Massive open Online Courses Source Code Embedding

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