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26th International Symposium on Stabilization, Safety, and Security of distributed Systems

作者： Biswas, Umesh Chakraborty, Trisha Young, Maxwell Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Amazon Web Serv Minneapolis MN 55401 USA

ISBN: (纸本)9783031744976;9783031744983

Contention resolution addresses the problem of coordinating access to a shared communication channel. Time is discretized into synchronized slots, and a packet can be sent in any slot. If no packet is sent, then the slot is empty;if a single packet is sent, then it is successful;and when multiple packets are sent at the same time, a collision occurs, resulting in the failure of the corresponding transmissions. In each slot, every packet receives ternary channel feedback indicating whether the current slot is empty, successful, or a collision. Much of the prior work on contention resolution has focused on optimizing the makespan, which is the number of slots required for all packets to succeed. However, in many modern systems, collisions are also costly in terms of the time they incur, which existing contention-resolution algorithms do not address. In this paper, we design and analyze a randomized algorithm, COLLISION-AVERSION BACKOFF (CAB), that optimizes both the makespan and the collision cost. We consider the static case where an unknown n >= 2 packets are initially present in the system, and each collision has a known cost C, where 1 <= C <= n(k) for a known constant k >= 0. With error probability polynomially small in n, CAB guarantees that all packets succeed with makespan and a total expected collision cost of (O) over tilde (n root C). We give a lower bound for the class of fair algorithms: where, in each slot, every packet executing the fair algorithm sends with the same probability (and the probability may change from slot to slot). Our lower bound is asymptotically tight up to a poly(log n)-factor for sufficiently large C.

关键词： distributed computing contention resolution collision cost

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Research on a Fog computing Architecture and BP Algorithm Application for Medical Big Data

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Intelligent Automation & Soft computing 2023年第7期37卷 255-267页

作者： Baoling Qin College of Electronic Information Engineering Foshan UniversityFoshanChina

Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical *** with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network *** order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are *** architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of *** diagnosis results are designed to reduce the business processing delay and improve the diagnosis *** the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment *** the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of *** results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment *** not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduce

关键词： Medical big data IoT fog computing distributed computing BP algorithm model

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Health Care Equity Through Intelligent Edge computing and Augmented Reality/Virtual Reality A Review

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JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2023年 16卷 2839-2859页

作者： Lakshminarayanan, Vishal Ravikumar, Aswathy Sriraman, Harini Alla, Sujatha Chattu, Vijay Kumar Vellore Inst Technol Sch Comp Sci & Engn Chennai TN India Old Dominion Univ Frank Batten Coll Engn Dept Engn Management & Syst Engn Norfolk VA 23529 USA Global Hlth Res & Innovat Canada Ctr Technol & Innovat Toronto ON M1J 2W8 Canada Univ Toronto Temerty Fac Med Dept Occupat Sci & Occupat Therapy Toronto ON Canada Datta Meghe Inst Med Sci Fac Med Dept Community Med Wardha India

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing.

关键词： internet of things machine learning distributed computing equitable healthcare digital health

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Chorus: More Efficient Machine Learning on Serverless Platform 35th

Chorus: More Efficient Machine Learning on Serverless Platfo...

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35th International Conference on Database and Expert Systems Applications (DEXA)

作者： Yang, Guang Liu, Jie Wang, Shuai Qu, Muzi Ye, Dan Zhong, Hua Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Syst Software Inst Software Beijing 100190 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Nanjing 211135 Peoples R China

ISBN: (纸本)9783031683084;9783031683091

Executing machine learning jobs on serverless platform could gain higher performance in a simplified manner. But current solutions have not fully utilized the flexibility of serverless computing. The widely used Bulk Synchronous Parallel (BSP) mode has significant resource waste, the parameter server nodes suffer bottleneck pressure from both networking and performance aspects. This paper presents Chorus, a machine learning framework on serverless platform based on the parameter server architecture. In Chorus we propose the Lambda Synchronous Parallel (LSP) model to coordinate Lambda workers. It simultaneously utilizes functions with different resource level to collaborate and dynamically adjusts resource allocation to maintain balance of model training. To alleviate the bottleneck pressure on the parameter server, we build a buffer system in memory database to exchange gradient data between the parameter server and workers. We set up multiple buffer slots in the buffer system to alleviate network pressure and design a buffer merging strategy to disperse computational pressure of parameter server among Lambda workers. In the experiments on different ML algorithms with different synchronous parallel models, Chorus shows outstanding performance improvements and budget-saving capacities.

关键词： Serverless Machine Learning distributed computing

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Orthogonal Layers of Parallelism in Large-Scale Eigenvalue Computations

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ACM TRANSACTIONS ON PARALLEL computing 2023年第3期10卷 1-31页

作者： Alvermann, Andreas Hager, Georg Fehske, Holger Univ Greifswald Inst Phys Felix Hausdorff Str 6 D-17489 Greifswald Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Natl High Performance Comp Ctr Martensstr 1 D-91058 Erlangen Germany

We address the communication overhead of distributed sparse matrix-(multiple)-vector multiplication in the context of large-scale eigensolvers, using filter diagonalization as an example. The basis of our study is a performance model, which includes a communication metric that is computed directly from the matrix sparsity pattern without running any code. The performance model quantifies to which extent scalability and parallel efficiency are lost due to communication overhead. To restore scalability, we identify two orthogonal layers of parallelism in the filter diagonalization technique. In the horizontal layer the rows of the sparse matrix are distributed across individual processes. In the vertical layer bundles of multiple vectors are distributed across separate process groups. An analysis in terms of the communication metric predicts that scalability can be restored if, and only if, one implements the two orthogonal layers of parallelism via different distributed vector layouts. Our theoretical analysis is corroborated by benchmarks for application matrices from quantum and solid state physics, road networks, and nonlinear programming. We finally demonstrate the benefits of using orthogonal layers of parallelism with two exemplary application cases-an exciton and a strongly correlated electron system-which incur either small or large communication overhead.

关键词： distributed computing sparse matrix-vector multiplication communication-avoiding algorithms

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From distributed Algorithms to Machine Learning and Back 23

From Distributed Algorithms to Machine Learning and Back

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42nd ACM SIGACT-SIGOPS Symposium on Principles of distributed computing (PODC)

作者： Wattenhofer, Roger Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798400701214

In the realm of computer science, it may seem that distributed computing and machine learning exist on opposite ends of the spectrum. However, there are many connections between the two domains, both in theory and practice. Recently, machine learning research has become excited about graphs. And when machine learning meets graphs, researchers familiar with distributed algorithms may experience a sense of deja vu, as many classic distributed computing paradigms are being rediscovered. It feels a bit like "machine learning + graphs = distributed algorithms." In my talk, I am going to introduce some key concepts in graph machine learning such as underreaching and oversquashing. These concepts have been known in the distributed computing community as local and congest, respectively. In the main part of the talk, I am going to present some recent breakthroughs in this exciting intersection of fields. Finally, I will also present some intriguing open problems.

关键词： Graph neural networks distributed computing networks algorithms

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Partitioned Reduction for Heterogeneous Environments 32

Partitioned Reduction for Heterogeneous Environments

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32nd Euromicro International Conference on Parallel, distributed and Network-Based Processing (PDP)

作者： De Rango, Alessio Utrera, Gladys Gil, Marisa Martorell, Xavier Giordano, Andrea D'Ambrosio, Donato Mendicino, Giuseppe Univ Calabria Dept Environm Engn DIAm I-87036 Arcavacata Di Rende CS Italy Univ Politecnica Catalunya BarcelonaTECH Comp Architecture Dept Barcelona 08034 Spain Natl Res Counsil CNR Inst High Performance Comp ICAR Arcavacata Di Rende CS Italy Univ Calabria Dept Math & Comp Sci DEMACS I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9798350363074;9798350363081

Nowadays, performance in HPC applications focuses on MPI efficiency as the de facto message-passing library to exploit parallelism. Features such as multithread and communication and processing overlap are continuously studied to adapt to new platforms and a more significant number of processing units like GPU platforms. In this sense, recently, the MPI-4.0 standard introduced the partitioned point-to-point communication primitives to potentiate computation and communication overlapping. This paper introduces an innovative extension to MPI, specifically addressing partitioned communication for MPI-reduction primitives. Traditional reduction tasks conventionally involve processing the complete input vector following the conclusion of GPU computations. In contrast, our proposed methodology exploits message partitioning to process reduction tasks in real-time incrementally. This approach allows the system to process individual partitions of the input vector as they become available, removing the necessity to await the full completion of GPU computations before initiating the reduction. Our results demonstrate promising benefits, particularly for large message sizes. However, it is essential to acknowledge that optimizations at synchronization points remain potential bottlenecks, requiring meticulous analysis and consideration.

关键词： MPI partitioned communication GPU programming distributed computing

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Combining Lossy Compression with Multi-level Caching for Data Staging over Network

Combining Lossy Compression with Multi-level Caching for Dat...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Aoyagi, Rei Takahashi, Keichi Shimomura, Yoichi Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ Cybersci Ctr Sendai Miyagi Japan

ISBN: (纸本)9798350364613;9798350364606

Researchers conduct post-processing on the simulation results by running an interactive data analysis tool on a High-Performance computing (HPC) system installed at an HPC center and retrieving the post-processed results. Certain data analysis scenarios require to transfer the simulation results directly from the center. in such scenarios, a portion of the data would usually be streamed over the network to achieve interactivity. However, there still exist two challenges in maintaining interactivity: (1) limited network bandwidth and (2) long network latency. To tackle these challenges, we propose a system to enable interactive array analysis over the network. We employ error-bounded lossy compression to increase the effective network bandwidth. Furthermore, we employ multi-level caching to hide the network latency and combine prefetching to improve the cache hit ratio. The cache replacement and prefetching policies are designed considering the data access pattern of interactive analysis. We compared our proposed system with TileDB, one of the state-of-the-art array databases, by measuring the average latency for various access patterns. Compared to TileDB, the proposed system reduces the average latency by up to 91.6% by allowing 10% of error because the cache hit ratio was improved by more than 40% due to the proper cache replacement and prefetching policy and network transfer time was reduced more than 75% by using lossy compression.

关键词： HPC distributed computing Data Staging Lossy Compression Interactive Analysis Bandwidth Software Caching

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Mitigating Demographic Bias of Federated Learning Models via Robust-Fair Domain Smoothing: A Domain-Shifting Approach 44

Mitigating Demographic Bias of Federated Learning Models via...

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44th IEEE International Conference on distributed computing Systems (ICDCS)

作者： Zeng, Huimin Yue, Zhenrui Jiang, Qian Zhang, Yang Shang, Lanyu Zong, Ruohan Wang, Dong Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350386066;9798350386059

Federated learning (FL) emerges as a promising solution to train machine learning (ML) models from distributed data sources. In FL, the heterogeneous and imbalanced data distribution of local clients could severely hurt the fairness of the aggregated global model. In this paper, we identify two key obstacles of developing fair FL models w.r.t. the global distribution: the domain shifts from local clients to the global data distribution and the fairness heterogeneity across local clients. Therefore, considering these two obstacles, we present a novel fairness-aware FL training framework Robust-Fair Domain Smoothing (RFDS) to address the bias issue of FL models from a unique domain-shifting perspective. In particular, we design two novel components to build RFDS: 1) local robust-fair training, and 2) reference domain smoothing. Local robust-fair training aims to train robust-fair local models whose fairness is robust against the domain shifts from local distributions to the global distribution. Reference domain smoothing reduces the heterogeneity of fairness across clients to improve the fairness of the aggregated global model. We further provide a theoretical analysis to show the connection between the domain discrepancy of local data distributions and the heterogeneity of fairness across clients. Empirical evaluation results on multiple real-world datasets show that RFDS achieves promising performance gains in improving demographic fairness compared to state-of-the-art baselines.

关键词： Federated Learning Group Fairness Domain Shift distributed computing

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Design of Cluster-computing Architecture to Improve Training Speed of the Neuroevolution Algorithm 9th

Design of Cluster-Computing Architecture to Improve Training...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Omelianenko, Iaroslav Inst Software Syst Kiev Ukraine

ISBN: (纸本)9789819735556;9789819735563

In this paper, we review the key features and major drawbacks of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, such as slow training speed that limits its area of application. The main reason for the performance issues of the NEAT algorithm is the huge number of calculations required at the end of each epoch to estimate the fitness of each organism in the population. We propose a software system architecture that can be implemented to solve NEAT performance problems based on Ray cluster-computing framework. Finally, we demonstrate how fitness estimation computations can be distributed across stateless distributed workers deployed either on-premise or in the cloud using Ray framework.

关键词： Genetic algorithms NeuroEvolution of Augmenting Topologies Ray framework distributed computing Cluster computing Cloud computing

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