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Low-Carbon Geographically distributed Cloud-Edge Task Scheduling 1

引用

24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Zhu, Yingjie Qi, Ji Wang, Zehao Wei, Shengjie Chen, Yan Cao, Tuo Luo, Gangyi Qian, Zhuzhong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Technology Co. Ltd. Jiangsu Suzhou China

ISBN: (数字)9789819615513

ISBN: (纸本)9789819615506

Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers are powered by renewable energy sources, due to the difficulties of using traditional power supplies or because of advancements in energy harvesting technologies. These green edge servers can cut down carbon emissions by processing tasks locally, but the inherent limitations of their computing capacity result in some tasks having to be uploaded to a data center to meet service-level agreement (SLA) requirements. To further reduce carbon emissions in cloud-edge systems, scheduling tasks to those low-carbon data centers while meeting latency constraints is highly beneficial. In this paper, we propose a low-carbon cloud-edge scheduling algorithm that utilizes Lyapunov optimization techniques and Markov approximation to address the long-term optimization problem of carbon emissions. Our algorithm guarantees provable performance, and simulation results demonstrate its effectiveness in striking a balance between carbon emissions and task latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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A PGAS-Based Implementation for the parallel Minimum Spanning Tree Algorithm 13th

A PGAS-Based Implementation for the Parallel Minimum Spannin...

引用

13th international conference on Large-Scale ScientificComputations (LSSC)

作者： Bejanyan, Vahag Astsatryan, Hrachya Natl Acad Sci Republ Armenia Inst Informat & Automat Problems 1 Paruyr Sevak St Yerevan 0014 Armenia

ISBN: (纸本)9783030975494;9783030975487

The minimum spanning tree is a critical problem for many applications in network analysis, communication network design, and computer science. The parallel implementation of minimum spanning tree algorithms increases the simulation performance of large graph problems using high-performance computational resources. The minimum spanning tree algorithms generally use traditional parallel programming models for distributed and shared memory systems, like Massage Passing Interface or OpenMP. Furthermore, the partitioned global address space model offers new capabilities in the form of asynchronous computations on distributed shared memory, positively affecting the performance and scalability of the algorithms. The paper aims to present a new minimum spanning tree algorithm implemented in a partitioned global address space model. The experiments with diverse parameters have been conducted to study the efficiency of the asynchronous implementation of the algorithm.

关键词： Minimum spanning tree PGAS model parallel algorithms Large-scale graphs HPC

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Assessing the Lakehouse: Analysis, Requirements and Definition 25

Assessing the Lakehouse: Analysis, Requirements and Definiti...

引用

25th international conference on Enterprise Information systems, ICEIS 2023

作者： Schneider, Jan Gröger, Christoph Lutsch, Arnold Schwarz, Holger Mitschang, Bernhard Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany Robert Bosch GmbH Borsigstraße 4 Stuttgart70469 Germany

ISBN: (纸本)9789897586484

The digital transformation opens new opportunities for enterprises to optimize their business processes by applying data-driven analysis techniques. For storing and organizing the required huge amounts of data, different types of data platforms have been employed in the past, with data warehouses and data lakes being the most prominent ones. Since they possess rather contrary characteristics and address different types of analytics, companies typically utilize both of them, leading to complex architectures with replicated data and slow analytical processes. To counter these issues, vendors have recently been making efforts to break the boundaries and to combine features of both worlds into integrated data platforms. Such systems are commonly called lakehouses and promise to simplify enterprise analytics architectures by serving all kinds of analytical workloads from a single platform. However, it remains unclear how lakehouses can be characterized, since existing definitions focus almost arbitrarily on individual architectural or functional aspects and are often driven by marketing. In this paper, we assess prevalent definitions for lakehouses and finally propose a new definition, from which several technical requirements for lakehouses are derived. We apply these requirements to several popular data management tools, such as Delta Lake, Snowflake and Dremio in order to evaluate whether they enable the construction of lakehouses. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Data warehouses

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TEngine: A Native distributed Table Storage Engine

TEngine: A Native Distributed Table Storage Engine

引用

international conference on Data Engineering

作者： Xiaopeng Fan Song Yan Yuchen Huang Chuliang Weng School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the rapid development of storage and network technology, emerging high-performance hardware is being widely applied to the distributed storage cluster. However, existing distributed storage systems employing multi-layer abstractions to provide table data services result in leaving high-speed hardware under-exploited. In this paper, we propose TEngine, a native distributed table storage engine designed for NVMe SSD and RDMA. The key is that TEngine removes the file abstraction to construct table structures on the device directly. For metadata service, TEngine designs a decoupled single metadata server, reducing distributed coordination, easing the burden on the metadata node, and enabling localized data node access. For data service, TEngine optimizes the parallel processing capability of NVMe devices by integrating upper-level multi-thread parallel operations with lower-level NVMe devices' parallel I/O processing. Moreover, TEngine introduces a periodic pull-based data synchronization approach to transform data pushing into periodic data pulling, which offloads the synchronization burden from the leader to the followers. The experimental results show that TEngine outperforms state-of-the-art distributed storage systems using the same hardware environment.

关键词： distributed databases Transforms Metadata parallel processing Data engineering Hardware Synchronization

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A Queueing System to Jobs Scheduling Problem in AGVs

A Queueing System to Jobs Scheduling Problem in AGVs

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Karol Marszałek Adam Domański Rafał Cupek Jerry Chun-Wei Lin Andrzej Kwiecień Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Automated Guided Vehicles are mobile robots de-signed for transportation purposes, and one of the most important problems associated with intelligent logistics is the problem of job scheduling. The goal is to find the optimal allocation of job execution by the number of available devices. The problem can be resolved with a simulation in which the different scenarios are evaluated. However, creating such a simulation model requires a statistical description of the problem. In this paper, we implement the simulation model for the AGV environment. Based on the mathematical description of the model, the discrete event simulation is created using the Python programming language and the SimPy library. We use the simulation to compare the solution of the job scheduling problem using the simulated annealing and genetic algorithms.

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Optimizing CNNs Throughput on Bandwidth-Constrained distributed Multi-FPGA Architectures

Optimizing CNNs Throughput on Bandwidth-Constrained Distribu...

引用

international conference on parallel and distributed systems (ICPADS)

作者： Yuzhu Zhou Bo Peng Qiang Li Maosong Lin Xiang Lei Qiao Wang School of Information Engineering Southwest University of Science and Technology Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

Existing multi-FPGA architectures often leverage high-speed interconnect technologies to achieve higher performance by exploiting ample communication bandwidth. In this paper, we propose an effective mapping approach for accelerating CNNs on bandwidth-constrained distributed multi-FPGA architectures. We formulate the system-level mapping problem and then introduce a method based on Genetic Algorithm (GA) and Mixed-Integer Nonlinear Programming (MINLP) to attain optimal solutions.

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distributed Healthcare Information System Based on High-Concurrency Microservice Architecture

Distributed Healthcare Information System Based on High-Conc...

引用

Information Science, parallel and distributed systems (ISPDS), international conference on

作者： Kaifeng Guo Rongjian Gao Fuzhou University Fuzhou Fujian China

With the digital transformation and increasing demand for informatization in the healthcare industry, traditional centralized information systems can no longer meet the requirements of high-concurrency access. However, microservices architecture offers a flexible and scalable solution that effectively addresses the complexity and high-concurrency access demands of healthcare information systems. In this paper, we propose a microservices system that adopts a distributed architecture combined with high-concurrency processing mechanisms. The system disperses various modules across different servers as micro-services, and each microservice can independently handle requests. This distributed and parallel processing approach improves system responsiveness and throughput while reducing the risk of single-point failures. To validate the feasibility and performance of the system, we conducted a series of experiments and evaluations. The results demonstrate that the distributed healthcare information system based on microservices architecture performs exceptionally well in handling large-scale data and high-concurrency access. The system not only provides efficient data storage and retrieval capabilities but also exhibits good scalability and fault tolerance.

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Probabilistic Resources Allocation with Group Dependencies in distributed Computing 17th

Probabilistic Resources Allocation with Group Dependencies i...

引用

parallel Computing Technologies - 17th international conference, PaCT 2023, Proceedings

作者： Toporkov, Victor Yemelyanov, Dmitry Bulkhak, Artem National Research University "MPEI" Moscow Russia

ISBN: (纸本)9783031416729

In this work, we introduce and study a set of tree-based algorithms for resources allocation considering group dependencies between their parameters. Real world distributed and high-performance computing systems often operate under conditions of the resources availability uncertainty caused by uncertainties of jobs execution, inaccuracies in runtime predictions and other global and local utilization events. In this way we can observe an availability over time function for each resource and use it as a scheduling parameter. As a single parallel job usually occupies a set of resources, they shape groups with common probabilities of usage and release events. The novelty of the proposed approach is an efficient algorithm considering groupings of resources by the common availability probability for the resources’ co-allocation. The proposed algorithm combines dynamic programming and greedy methods for the probability-based multiplicative knapsack problem with a tree-based branch and bounds approach. Simulation results and analysis are provided to compare different approaches, including greedy and brute force solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Resource allocation

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Exploiting CXL-based Memory for distributed Deep Learning 51

Exploiting CXL-based Memory for Distributed Deep Learning

引用

51st international conference on parallel Processing (ICPP)

作者： Arif, Moiz Assogba, Kevin Rafique, M. Mustafa Vazhkudai, Sudharshan Rochester Inst Technol Rochester NY 14623 USA Micron Technol Inc Austin TX USA

ISBN: (纸本)9781450397339

Deep learning (DL) is being widely used to solve complex problems in scientific applications from diverse domains, such as weather forecasting, medical diagnostics, and fluid dynamics simulation. DL applications consume a large amount of data using large-scale high-performance computing (HPC) systems to train a given model. These workloads have large memory and storage requirements that typically go beyond the limited amount of main memory available on an HPC server. This significantly increases the overall training time as the input training data and model parameters are frequently swapped to slower storage tiers during the training process. In this paper, we use the latest advancements in the memory subsystem, specifically Compute Express Link (CXL), to provide additional memory and fast scratch space for DL workloads to reduce the overall training time while enabling DL jobs to efficiently train models using data that is much larger than the installed system memory. We propose a framework, called DeepMemoryDL, that manages the allocation of additional CXL-based memory, introduces a fast intermediate storage tier, and provides intelligent prefetching and caching mechanisms for DL workloads. We implement and integrate DeepMemoryDL with a popular DL platform, TensorFlow, to show that our approach reduces read and write latencies, improves the overall I/O throughput, and reduces the training time. Our evaluation shows a performance improvement of up to 34% and 27% compared to the default TensorFlow platform and CXL-based memory expansion approaches, respectively.

关键词： Deep Learning Prefetching Caching TensorFlow Data Pipeline

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Evaluation of a distributed deep learning framework as a reference architecture for a cloud environment 10

Evaluation of a distributed deep learning framework as a ref...

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10th IEEE Jubilee international conference on Computational Cybernetics and Cyber-Medical systems, ICCC 2022

作者： Farkas, Attila Póra, Krisztián Szénási, Sándor Kertész, Gábor Lovas, Róbert Hungary Óbuda University John von Neumann Faculty of Informatics Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Hungary

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

distributed deep learning is becoming increasingly important due to the size of deep neural networks. The sheer volume of the input datasets used in the process can have a significant negative effect on the training time of the model. As a consequence of physical limitations, the resources of a single node cannot be scaled to a sufficient level for efficient training. As such, different parallel and distributed solutions exist in order to solve this problem. In this paper, we measured the scalability of Horovod, a distributed deep learning framework (available as a reference architecture) with different parameters on the research infrastructure called ELKH Cloud. The experiment was conducted in order to verify the scalability of the framework on the general purpose infrastructure, which was not designed primarily for distributed deep learning applications. Measurements assessing model accuracy were also performed, for the purpose of validating the distributed training process. © 2022 IEEE.

关键词： Scalability

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