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

作者： Zhang, Naihan Yi, Xinxin Pang, Ran Han, Mengyao Cao, Chang Tang, Xiongyan Huang, Tao China Unicom Res Inst Beijing Peoples R China Natl Engn Res Ctr Next Generat Internet Broadband Beijing Peoples R China China United Network Commun Grp Corp Ltd Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350378412

A novel hybrid computing-aware traffic steering scheme is proposed for high-speed Internet of Vehicles (IoV) application to overcome the problems of existing centralized and distributed scheme. And a simulation experiment is conducted to verify the performance of proposed hybrid scheme. In the proposed scheme, computing and network status are jointly considered to make routing decision. A computing-aware network orchestration system is used to aware and advertise computing status, schedule service, and compute optimal path in a centralized mode. Besides, distributed mode of service selection and path computation, which is employed in network devices, is introduced into the scheme. Simulation results show that the centralized computing advertisement mode in the proposed scheme is more suitable for employing a large-scale computing-aware network and the centralized and distributed routing decision modes each have their own unique advantages. Since both routing decision modes coexist in the proposed scheme, hybrid scheme can provide differentiated capabilities for different service types. In high-speed IoV scenario, the distributed routing decision mode provides guarantees of fast service switching and low delay for delay-sensitive services. The centralized routing decision mode provides optimal resource utilization of entire network for other media or entertainment services.

关键词： computing-aware Traffic Steering Hybrid Scheme High-speed Internet of Vehicle

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Evaluation of Trust Metrics in an Artificial Hormone System 27

Evaluation of Trust Metrics in an Artificial Hormone System

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27th international Symposium on Real-Time distributed computing (ISORC)

作者： Homann, Philipp Diegelmann, Jonas Pacher, Mathias Brinkschulte, Uwe Goethe Univ Frankfurt Inst Informat Frankfurt Germany

ISBN: (纸本)9798350371291;9798350371284

Organic computing (OC) is a well-known research field aiming to build dependable embedded systems. OC systems often employ self-X properties such as self-configuration, self-healing, etc. The artificial hormone system (AHS) is a decentralized OC middleware, designed to autonomously distribute tasks on a heterogeneous processor network. The decentralized task distribution is controlled through short messages called hormones. The ever-growing array of application scenarios, as well as the large number of heterogeneous connected devices, enables a broad array of attack vectors, both inside and outside of the AHS's control. Security mechanisms for the AHS have long become a necessity. In this paper, we propose the application of inter-processor trust, to address this issue on the AHS level. The presented trust concept is explained in detail, demonstrated in different attack scenarios and evaluated concerning its memory usage, communication overhead and efficiency.

关键词： Organic computing middleware Artificial DNA trust

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3rd ieee 2022 international conference on computing, Communication, and Intelligent systems, ICCCIS 2022

3rd IEEE 2022 International Conference on Computing, Communi...

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3rd ieee international conference on computing, Communication, and Intelligent systems, ICCCIS 2022

ISBN: (纸本)9781665462006

The proceedings contain 168 papers. The topics discussed include: communication induced checkpointing based fault tolerance mechanism – a review and CIAC-FTM framework in IoT environment;a semantic review on challenges, trends towards defensive ids in Internet of things;semantic segmentation in medical image based on hybrid Dlinknet and UNet;smart car - one stop for all automobile needs;hybrid cryptography algorithm for the internet of things based plant information security;5G network: architecture, protocols, challenges and opportunities;fiber Bragg grating temperature sensor and calibration scheme in high magnetic field environment : an application for aluminum electrolysis cell in potline;federated learning with blockchain: a study of the latest decentralized couple;an enhanced copy-deterrence scheme for secure image outsourcing in cloud;decentralized data privacy protection and cloud auditing security management;land registry and title management using blockchain and smart contracts;breast cancer modeling and prediction combining machine learning and artificial neural network approaches;and confluence of artificial intelligence and blockchain powered smart contract in finance system.

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A simulation framework for distributed on-board computing for small-satellite clusters

A simulation framework for distributed on-board computing fo...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Bhaumik, Anik Basu Dey, Ayush Biswas, Swagata Paul, Himadri Sekhar Univ Calcutta Kolkata India Heritage Inst Technol Kolkata India TCS Res Kolkata India

ISBN: (纸本)9798350304367;9798350304374

Small satellite clusters are increasingly being used as cost-effective earth observation system. Small satellites, with state-of-the-art sensing payload enable high fidelity imaging, at the cost of high data volume and velocity. Orbital Edge computing (OEC) was proposed to utilize computing power available on-board to analyze data in-situ to reduce data volume and thereby effectively utilize communication bandwidth. With an extension of this idea od OEC, distributed Orbital Edge computing (DOEC) was proposed to collaboratively utilize all computing resource available in a satellite cluster. However, with limited and constrained communication poses challenges for effective utilization of free computation cycles of on-board computers. However, researchers have expressed interest in this technology with the expectation to improve remote sensing capability with better system utilization. Recently, several ideas and proposals around DOEC are presented. Therefore, a standard framework is essential to implement and demonstrate effectiveness of proposals in a realistic setup. In this direction, we present a simulation framework capable of evaluating different performance metrics in a realistic space-like environment. As a demonstration we implement and execute a distributed forest-fire detection workflow on satellite images using the framework.

关键词： Small Satellite Constellation System Simulation On-Board computing distributed Scheduling

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Evaluation and Optimization of Gradient Compression for distributed Deep Learning 43

Evaluation and Optimization of Gradient Compression for Dist...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Zhang, Lin Zhang, Longteng Shi, Shaohuai Chu, Xiaowen Li, Bo Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Hong Kong Peoples R China

ISBN: (纸本)9798350339864

To accelerate distributed training, many gradient compression methods have been proposed to alleviate the communication bottleneck in synchronous stochastic gradient descent (S-SGD), but their efficacy in real-world applications still remains unclear. In this work, we first evaluate the efficiency of three representative compression methods (quantization with Sign-SGD, sparsification with Top-k SGD, and low-rank with Power-SGD) on a 32-GPU cluster. The results show that they cannot always outperform well-optimized S-SGD or even worse due to their incompatibility with three key system optimization techniques (all-reduce, pipelining, and tensor fusion) in S-SGD. To this end, we propose a novel gradient compression method, called alternate compressed Power-SGD (ACP-SGD), which alternately compresses and communicates low-rank matrices. ACP-SGD not only significantly reduces the communication volume, but also enjoys the three system optimizations like S-SGD. Compared with Power-SGD, the optimized ACP-SGD can largely reduce the compression and communication overheads, while achieving similar model accuracy. In our experiments, ACP-SGD achieves an average of 4.06x and 1.43x speedups over S-SGD and Power-SGD, respectively, and it consistently outperforms other baselines across different setups (from 8 GPUs to 64 GPUs and from 1Gb/s Ethernet to 100Gb/s InfiniBand).

关键词： distributed Deep Learning Gradient Compression Power-SGD System Optimization

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distributed Control Application for Smart Grids using RIAPS 9

Distributed Control Application for Smart Grids using RIAPS

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ieee international conference on Smart computing (SMARTCOMP)

作者： Ghosh, Purboday Barua, Niloy Krentz, Timothy Karsai, Gabor Dubey, Abhishek Lukic, Srdjan Vanderbilt Univ Inst Software Integrated Syst 221 Kirkland Hall Nashville TN 37235 USA North Carolina State Univ FREEDM Syst Ctr Raleigh NC USA

ISBN: (纸本)9798350322811

The integration of computational capabilities with the electrical infrastructure of the grid can be envisioned as a societal scale Cyber Physical System (CPS). Middleware frameworks can act as a layer of abstraction that manages the interaction between disparate applications to facilitate intelligent control and management of energy production and consumption. This demonstration showcases Resilient Information Architecture Platform for Smart Grid (RIAPS), a distributed software platform that combines a domain specific modeling language with framework-level services such as communication, remote deployment of applications, distributed coordination, time synchronization, and fault tolerance, to develop and run distributed applications. An example of an energy demand curtailment scheme called load shedding is presented, to highlight how the RIAPS framework can be used to implement distributed algorithms to control elements of a power system, which runs as a simulation using OpenDSS.

关键词： component distributed systems smart grid cyber pysical system opendss simulation load shedding middleware

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Cohabitation of Intelligence and systems: Towards Self-reference in Digital Anatomies 18

Cohabitation of Intelligence and Systems: Towards Self-refer...

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18th ieee international conference on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Morichetta, Andrea Lackinger, Anna Dustdar, Schahram TU Wien Vienna Austria

ISBN: (纸本)9798331539580

The modern computing scenario of the computing Continuum exhibits large and complex applications with heterogeneous requirements running on distributed infrastructure. Still, when it comes to coordinating and controlling such applications and infrastructures, it is common to rely on centralized or ad-hoc solutions. While these approaches are robust, scaling management solutions, managing local changes, and having a holistic perspective can be challenging. Additionally, they could be better suited for addressing new problems in dynamic environments. Therefore, new approaches are needed. In this paper, we present DICT, a novel method for managing the computing Continuum, i.e., the infrastructure and the applications. The proposed approach encompasses a series of modules for automatic management. The core idea is to develop a method for applying the intents coming from the infrastructure and application managers in an autonomic and dynamic way. The modules can communicate through coordinators that take observable inputs and send them back predictions on the next actions to take. These coordinators have the role of summarizing the sensed observation and extracting high-level information in light of the AI advancement that shows how discrete space representation of inputs improves generalization. Thus, they can have models that build their own semantics and "language." We envision that, through DICT, both the application and the infrastructure management will only have to specify high-level intents and not focus on defining encoded and difficult-to-change strategies.

关键词： computing Continuum Holistic Management distributed Intelligence Intent Coordination

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SocialEdge: Socialized Learning-Based Request Scheduling for Edge-Cloud systems 43

SocialEdge: Socialized Learning-Based Request Scheduling for...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Wang, Ziwei Zhao, Yunfeng Qiu, Chao He, Qiang Wang, Xin Wang, Xiaofei Hu, Qinghua Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

ISBN: (纸本)9798350339864

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

关键词： Request dispatch service orchestration cloud-edge collaboration edge computing deep reinforcement learning

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Productivity, Portability, Performance, and Reproducibility: Data-Centric Python

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ieee TRANSACTIONS ON PARALLEL AND distributed systems 2025年第5期36卷 804-820页

作者： Ziogas, Alexandros Nikolaos Schneider, Timo Ben-Nun, Tal Calotoiu, Alexandru De Matteis, Tiziano de Fine Licht, Johannes Lavarini, Luca Hoefler, Torsten Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci CH-8092 Zurich Switzerland Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA Vrije Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands NextSilicon CH-8005 Zurich Switzerland 1plusX CH-8005 Zurich Switzerland

Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High-Performance computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. This work presents a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes. Our benchmarks were reproduced in the Student Cluster Competition (SCC) during the Supercomputing conference (SC) 2022. We present and discuss the student teams' results.

关键词： Productivity Codes Semantics Computer architecture Supercomputers Software Field programmable gate arrays Optimization Python Computer languages high-performance computing dataflow computing parallel programming distributed computing distributed computing

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A Stable Matching Approach to Energy Efficient and Sustainable Serverless Scheduling for the Green Cloud Continuum 18

A Stable Matching Approach to Energy Efficient and Sustainab...

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18th ieee international conference on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Patel, Yashwant Singh Townend, Paul Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9798331539580

Cloud infrastructures are evolving from centralised systems to geographically distributed federations of edge devices, fog nodes, and clouds - often known as the Cloud-Edge Continuum. Continuum systems are dynamic, often massive in scale, and feature disparate infrastructure providers and platforms;this greatly increase the complexity of developing and managing applications. The Serverless paradigm shows the potential to greatly simplify the process of building Continuum applications - however, current scheduling mechanisms for Serverless Continuum platforms pay little attention to reducing the energy consumption and improving the sustainability of function execution. This is a significant omission, made worse as computing nodes within a Continuum may be powered by renewable energy sources that are intermittent and unpredictable, making low-powered and bottleneck nodes unavailable. There is great opportunity to design a decentralized energy management scheme for scheduling Serverless functions that takes advantage of the different layers of the Continuum, such as IoT devices located at the Edge, on-premises clusters closer to the data sources, or directly on large Cloud infrastructures. To achieve this, we formally model a green energy-aware Serverless workload scheduling problem for the multi-provider Cloud-Edge Continuum. We then design stable matching based technique for decentralized energy management (utilising a distributed controller) that considers the availability of green energy nodes and the QoS requirements of Serverless functions. We prove the complexity, stability and termination of the proposed heuristic algorithm, and also compare its performance with baseline scheduling techniques.

关键词： Cloud-Edge Continuum Serverless computing Renewable energy Scheduling Function-as-a-Service Matching

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