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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

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Applicability of Cloud Native-based Healthcare Monitoring Platform (CN-HMP) in Older Adult Facilities 44

Applicability of Cloud Native-based Healthcare Monitoring Pl...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Amin, Abu Bony Wang, Shiyang David, Uchechukwu Noh, Yeonsik Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA Univ Massachusetts Elaine Marieb Coll Nursing Amherst MA 01003 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Over the past few decades, the world has faced the huge demographic change in the aging population, which makes significant challenges in healthcare systems. the increasing older adult population along with the current health workforce shortage creates a struggling situation for current facilities and personnel to meet the demand. To tackle this situation, cloud computing is a fast-growing area in digital healthcare and it allows to settle up a modern distributed system environment, capable of scaling to tens of thousands of self healing multi-tenant nodes for healthcare applications. In addition, cloud native architecture is recently getting focused as an ideal structure for multi-node based healthcare monitoring system due to its high scalability, low latency, and rapid and stable maintainability. In this study, we proposed a cloud native-based rapid, robust, and productive digital healthcare platform which allows to manage and care for a large number of patient groups. To validate our platform, we simulated our Cloud Native-based Healthcare Monitoring Platform (CN-HMP) with real-time setup and evaluated the performance in terms of request response time, data packets delivery, and end-to-end latency. We found it showing less than 0.1 ms response time in at least 92.5% of total requests up to 3K requests, and no data packet loss along with more than 28% of total data packets with no latency and only approximate to 0.6% of those with maximum latency (3 ms) in 24-hour observation.

关键词： Cloud computing Scalability Sociology Medical services Real-time systems Electronic healthcare Time factors

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An Intelligent Parallel distributed Streaming Framework for near Real-time Science Sensors and High-Resolution Medical Images 21

An Intelligent Parallel Distributed Streaming Framework for ...

引用

50th international conference on Parallel Processing (ICPP)

作者： Shivadekar, Samit Mangalagiri, Jayalakshmi Phuong Nguyen Chapman, David Halem, Milton Gite, Rahul Univ Maryland Baltimore Cty Baltimore MD 21228 USA OpenKneck Inc Halethorpe MD USA

ISBN: (纸本)9781450384414

Our goals are to address challenges such as latency, scalability, throughput and heterogeneous data sources of streaming analytics and deep learning pipelines in science sensors and medical imaging applications. We present a prototype Intelligent Parallel distributed Streaming Framework (IPDSF) that is capable of distributed streaming processing as well as performing distributed deep training in batch mode. IPDSF is designed to run streaming Artificial Intelligent (AI) analytic tasks using data parallelism including partitions of multiple streams of short time sensing data and high-resolution 3D medical images, and fine grain tasks distribution. We will show the implementation of IPDSF for two real world applications, (i) an Air Quality Index based on near real time streaming of aerosol Lidar backscatter and (ii) data generation of Covid-19 computing Tomography (CT) scans using deep learning. We evaluate the latency, throughput, scalability, and quantitative evaluation of training and prediction compared against a baseline single instance. As the results, IPDSF scales to process thousands of streaming science sensors in parallel for Air Quality Index application. IPDSF uses novel 3D conditional Generative Adversarial Network (cGAN) training using parallel distributed Graphic Processing Units (GPU) nodes to generate realistic 3D high resolution Computed Tomography scans of Covid-19 patient lungs. We will show that IPDSF can reduce cGAN training time linearly with the number of GPUs.

关键词： distributed streaming Data Parallelism Deep Learning Lidar backscatter Computed Tomography cGAN

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Octopus: Experiences with a Hybrid Event-Driven Architecture for distributed Scientific computing 24

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology Guangdong China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science University of Chicago Chicago IL USA and Data Science and Learning Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Event-driven architecture

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Scalable and Consistent Graph Neural Networks for distributed Mesh-based Data-driven Modeling 24

Scalable and Consistent Graph Neural Networks for Distribute...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Shivam Barwey Riccardo Balin Bethany Lusch Saumil Patel Ramesh Balakrishnan Pinaki Pal Romit Maulik Venkatram Vishwanath Transportation and Power Systems Argonne National Laboratory Lemont USA Leadership Computing Facility Argonne National Laboratory Lemont USA Computational Science Division Argonne National Laboratory Lemont USA Information Sciences and Technology Pennsylvania State University University Park USA Mathematics and Computer Science Argonne National Laboratory Lemont USA

ISBN: (纸本)9798350355543

this work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). this concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer.

关键词： graph neural networks

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Multi-agent Reinforcement Learning for Task Allocation in Cooperative Edge Cloud computing

Multi-agent Reinforcement Learning for Task Allocation in Co...

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19th international conference on Service-Oriented computing (ICSOC)

作者： Ding, Shiyao Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9783031141355;9783031141348

Edge cloud computing has become a fundamental computation infrastructure supporting the resource-limited devices of Internet of things (IoT). An important problem in edge cloud computing is how to allocate tasks to the servers while minimizing various costs and satisfying task requirements. Studies to date usually assume a self-interested setting where each edge/cloud server is owned by one user who tries to maximize own interests. However, with the strong development of smart communities like smart factory, the servers are usually owned by an organization like an IT corporation. this triggers the necessity for edge/cloud server cooperation to maximize team interests. thus, in this paper, we consider a new problem called cooperative edge cloud computing where edge/cloud servers cooperate with each other to perform tasks to optimize the interests of the whole system. this problem is difficult due to some features such as 1) the tasks usually have high workloads which cannot be well performed by only one server;2) the tasks usually have a dependency relationship;3) edge servers are usually distributed where each server only has a partial observation. Our idea is to formulate the problem as a multiagent system, where each server is regarded as an agent who can learn to execute decision-making for task allocation based on its observation (e.g., current server status and arriving task). then, we employee multiagent reinforcement learning methods to make agents learn from the environment by themselves without previously designed rules. Our expected impact is that our algorithm can offer significantly better attributes such as low latency and low energy consumption in the cooperative edge cloud computing.

关键词： Internet of things (IoT) Edge cloud computing Task allocation Multiagent systems Reinforcement learning

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Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned using a Many-Task-Based Approach 24

Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Torben Kalkhof Andreas Koch Embedded Systems and Applications Group Technical University of Darmstadt Darmstadt Germany

ISBN: (纸本)9798350355543

Current programming models face challenges in dealing with modern supercomputers' growing parallelism and heterogeneity. Emerging programming models, like the task-based programming model found in the asynchronous many-task HPX programming framework, offer new ways to express parallelism, enhance scalability, and mask synchronization and communication latency on multi-core and distributed *** high-performance computing benchmarks are often unsuitable for comparing different programming models due to their limited code complexity. However, real-world scientific applications are usually too complex. As a middle ground, proxy applications model the behavior of actual scientific problems, while reducing code *** our research on using HPX to program machines with heterogeneous compute units (e.g., GPU and FPGA/AI Engines), we have also substantially optimized a pure HPX-based software baseline of the LULESH proxy application. this paper discusses the techniques we applied yielding single-node speed-ups of 1.33x to 2.25x for different problem sizes relative to the LULESH OpenMP reference implementation.

关键词： HPC

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An Enterprise Architecture based on Cloud, Fog and Edge computing for an Airfield Lighting Management System 24

An Enterprise Architecture based on Cloud, Fog and Edge Comp...

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24th IEEE international Enterprise distributed Object computing conference (IEEE EDOC)

作者： Mijuskovic, Adriana Bemthuis, Rob Aldea, Adina Havinga, Paul Univ Twente Enschede Netherlands

ISBN: (数字)9781728164717

ISBN: (纸本)9781728164717

Followed by the introduction of IoT and new sustainable technologies, energy management, Quality of Service and decrease of communication costs become important and complex for enterprise systems at airports. the aviation authorities' reports reveal that the airport ICT investments are mainly focused on travel safety, mobile commerce, and new technologies. the main idea behind a smart airport is to deploy IoT network managed through a Cloud-Fog-Edge paradigm for a smart platform and optimize the airport's efficiency. An IoT cloud-based platform solution supports multiple types of data, advanced analytics, artificial intelligence, and machine learning techniques. However, cloud computing has certain limitations such as increased delay in data reporting, increased latency in accessing user network, limited customization, increased reliance on external network and data privacy. Fog-Cloud and Edge-Cloud paradigms can overcome the weaknesses of cloud computing architectures. therefore, to understand the organizational impact of combining the usage of cloud, fog, and edge computing, we created an enterprise architecture that can be applied in a smart airport demonstration study. the enterprise architecture modelling was done by using ArchiMate and validated by means of an expert assessment and prototype implementation.

关键词： enterprise architecture smart airport solutions cloud computing fog computing edge computing

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Multi Task Multi-UAV Computation Offloading Enabled Mobile Edge computing systems 24th

Multi Task Multi-UAV Computation Offloading Enabled Mobile E...

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24th international conference on distributed Computer and Communication Networks, DCCN 2021

作者： Alzaghir, Abbas Koucheryavy, Andrey The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Pr. Bolshevikov 22 St. Petersburg193232 Russia

ISBN: (纸本)9783030971090

Mobile edge computing (MEC) is a technology that has found effective solutions in terms of ultra-low latency, low energy consumption, high data rate and high reliability. MEC is become actual appropriate for the devices that has a limited resource such IoT, UAVs etc. In this paper, we consider an algorithm to improve and minimize the energy consumption of Unmanned Aerial Vehicle by offloading and execution some tasks of UAV into nearby UAVs or into edge cloud server. the proposed algorithm is based on dynamic programming that uses a randomization and hamming distance termination to obtain approximately optimal solution. the algorithm can minimize the energy consumption of UAV and improving the total execution time by offload some tasks to nearby UAVs or to edge cloud server when transmission data rate of the network is high. the algorithm can find a nearly optimal offloading decision within a few repetitions. the results show that the proposed algorithm achieves a minimal energy while meeting energy and time constraints. © 2022, Springer Nature Switzerland AG.

关键词： Unmanned aerial vehicles (UAV)

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Loose Inter-Organizations Cooperation in Cloud computing: Process Chunks Configuration using Microservices 24

Loose Inter-Organizations Cooperation in Cloud Computing: Pr...

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24th IEEE international Enterprise distributed Object computing conference (IEEE EDOC)

作者： Lassoued, Yosra Nurcan, Selmin Gargouri, Faiez Univ Paris 1 Pantheon Sorbonne Ctr Rech Informat Paris France Univ Sfax MIRACL Lab Sfax Tunisia

ISBN: (纸本)9781728164717

Loose Inter-Organizational Cooperation in Cloud computing consists on dynamically constructing Cloud Workflow Services (CWS). CWS is a loosely coupled Inter-Organizational business process. the basic bricks of a CWS are its Process Chunks (PCs) which are dynamically discovered, selected and composed in the Cloud. A Process chunk is a dynamic configuration of microservices constructed, published, selected and composed through the Cloud. In this paper we are addressing how an organization playing the role of a PC-provider could construct loosely coupled PCs. To achieve this goal we are proposing a new approach that aims to configure a PC using microservices. this approach starts with PC specification and then dynamically selecting and composing the required microservices to execute the specified model. In order to validate our contribution a drug development case study is used.

关键词： Microservices Cloud computing Workflow Service

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Autonomous Execution for Multi-GPU systems: Compiler Support 24

Autonomous Execution for Multi-GPU Systems: Compiler Support

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Javid Baydamirli Tal Ben Nun Didem Unat Koç University Istanbul Turkey Lawrence Livermore National Laboratory Livermore California USA

ISBN: (纸本)9798350355543

Recent trends in HPC systems increasingly emphasize accelerators, particularly GPUs, as autonomous execution units, shifting control of entire program execution to GPUs. Communication libraries enable devices to move data independently among one another, bringing forth latency improvements, and first-party GPU runtimes expose APIs for kernels to organize their execution. Despite the trends and advancements, current high-level frameworks and compilers lack support for constructs enabling this autonomous execution. In this work, we aim to bridge this gap with a compiler and provide a productive method for writing efficient GPU-first code. We design and develop a code generator that efficiently fuses and schedules persistent kernels, provides high-level abstractions over device resources, and enables GPU-initiated communication within Python code using NVSHMEM to realize autonomous multi-GPU execution. We compare our implementation to other accelerated Python compilers including CuPy, DaCe, and cuNumeric on 22 NPBench kernels. We additionally perform a scaling study of distributed 2D/3D Jacobi and observe a speedup of 6.1x and 30.8x over DaCe and cuNumeric, respectively, on 8 GPUs for the 3D case with a scaling efficiency of 98%.

关键词： GPU-initiated communication

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