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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149810 条记录，以下是431-440 订阅

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Dynamic Scheduling of AES Cores for Aperiodic Tasks on Multi-tenant Cloud FPGAs 22

Dynamic Scheduling of AES Cores for Aperiodic Tasks on Multi...

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Donchez, Stephen Wang, Xiaofang Villanova Univ Dept Elect & Comp Engn Villanova PA 19085 USA

ISBN: (纸本)9798350381993;9798350382006

Field Programmable Gate Arrays (FPGAs) have long been utilized in systems benefiting from hardware acceleration of processes unsuitable for execution on a traditional processor. Accordingly, as much of the world pivots from on-site datacenters and computing resources to hybrid or cloud based platforms, Multi-processing System-on-Chip (MPSoC) FPGAs are increasingly being employed in cloud computing systems to speed up many computation-intensive applications. In cloud computing, multi-tenant FPGAs are constantly space- and time-shared among multiple tenants dynamically by leveraging the partial reconfiguration property of FPGAs. With increasing security and privacy concerns introduced by these memory-based volatile devices, most countermeasures rely on encryption and decryption engines such as AES (Advanced Encryption Standard) cores for user data protection. However, their high-resource requirements and long latency limit the number of such engines that can be implemented in hardware. They often become a performance bottleneck during peak time. In this paper, we propose a scheduling algorithm for aperiodic tasks to dynamically share multiple AES cores and hence to improve their utilization and overall system performance. Extensive experimental measurements on an FPGA development board featuring a Xilinx Ultrascale+ FPGA demonstrate the efficacy of our mechanism.

关键词： FPGA Security Scheduling Implementation AES Multi-tenancy Cloud computing

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Enhancing Internet-based Resource Discovery: The Efficacy of distributed Quadtree Overlay 3

Enhancing Internet-based Resource Discovery: The Efficacy of...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Alqaraleh, Muhyeeddin Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa13110 Jordan

ISBN: (纸本)9798350375190

distributed systems based on the Internet allow resources widely dispersed throughout the network to be utilized cooperatively and shared. This allows the end-user to obtain massive computational power to perform a computationally intensive task. This is where the essential function of resource finding comes into play. Even though different procedures have been presented to identify multi-attribute resources, these processes have been plagued by many problems, including significant overhead and inefficiency as the number of resources increases. distributed Quadtree Overlay is the innovative technology presented in this academic study. With DOTO, distance-aware querying is used. Moreover, since DOTO is constructed via the local network, it does not need any communication. As a result of the trials, the distributed Quadtree Overlay performed better than the Chord. According to the findings, distributed Quadtree Overlay is superior to Chord in terms of communication time since it needs fewer hopes and messages, uses less memory, and produces better (shortened) communication times. © 2024 ieee.

关键词： distributed computer systems

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Harmonia: Accurate Federated Learning with All-Inclusive Dataset 17

Harmonia: Accurate Federated Learning with All-Inclusive Dat...

引用

ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Choi, Wonmi Ahn, Juyoung Yoo, Yeonho Yoo, Chuck Yang, Gyeongsik Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350368543;9798350368536

Federated learning (FL) is an appealing model training technique that utilizes heterogeneous datasets and user devices, ensuring user data privacy. Existing FL research proposed device selection schemes to balance the computing speeds of devices. However, we observe that these schemes compromise prediction accuracy by similar to 57.7%. To solve this problem, we present Harmonia that enhances prediction accuracy, while also balancing the diverse computing speeds of devices. Our evaluation shows that Harmonia improves prediction accuracy by similar to 1.7x over existing schemes.

关键词： Federated Learning Data Privacy distributed Machine Learning Collaborative Learning Client Selection

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Quantum-Enabled distributed Transient Stability Assessment of Power systems 5

Quantum-Enabled Distributed Transient Stability Assessment o...

引用

2024 international conference on Quantum computing and Engineering

作者： Yu, Sijia Zhou, Yifan Wang, Lizhi SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA Siemens Technol Sustainable Bldg & Grid Grp Princeton NJ 08540 USA

ISBN: (纸本)9798331541378

Transient stability assessment (TSA) is an indispensable routine in power system operation and control. The increasing integration of distributed energy resources highlights the necessity of distributed transient stability assessment which can effectively capture the complicated stability characteristics of the entire power system without compromising the data privacy of individual local subsystems. This paper devises a quantum-enabled distributed transient stability assessment (Q-dTSA) method to enable data-driven transient stability prediction of power grids in a distributed, expressive and privacy-preserving manner. Our contributions include: 1) A quantum federated learning (QFL) architecture, which enables local power grids to jointly realize the data-driven TSA for the entire system using shallow-depth quantum circuits;2) A distributed quantum gradient descent (d-QGD) algorithm, which supports effective coordination between local subsystems to perform distributed training of the QNNs without leaking local power system information. 3) Extensive experiments in real-scale power grids obtained from both noise-free simulators and noisy IBM quantum computers, which validate the accuracy, fidelity, and noise-resilience of Q-dTSA, as well as its superiority over centralized quantum computing algorithms.

关键词： Quantum machine learning power system stability transient stability assessment distributed quantum processing quantum federated learning

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Efficient Multi-network Community Search Method for distributed Graph Iterative Computation 48

Efficient Multi-network Community Search Method for Distribu...

引用

48th Annual ieee international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Sun, Xiaochen Wang, Wei Huang, Tao Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350376975;9798350376968

Graph is often used for data analysis. distributed graph processing is gaining traction as it becomes more difficult for a single machine to store and process the complete graph due to the growing volume of data. We investigated 26 popular distributed graph processing systems and the graph algorithms and datasets provided by these systems. The computational logic of these graph algorithms does not distinguish between the types of vertices and edges, so distributed graph processing systems treat all vertices and edges in an undifferentiated way. However, using the hidden data connections of different types of vertices in multi-networks can greatly improve the accuracy of the community search algorithm. So we describe the challenges for the existing distributed graph processing systems to deal with different types of vertices and edges in multi-networks, and propose an index-based multi-network storage abstraction to store various vertices and edges, and a heuristic greedy algorithm to complete the partition job for different vertices and edges. Base on the two jobs, we finish the research of efficient community search for distributed graph processing in multi-networks, making it possible for future research of more algorithms for distributed graph processing in multi-networks.

关键词： distributed graph processing community search graph iterative computation

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Performance Modeling of Weather Forecast Machine Learning for Efficient HPC 42

Performance Modeling of Weather Forecast Machine Learning fo...

引用

42nd ieee international conference on distributed computing systems (ICDCS)

作者： Selvam, Karthick Panner Brorsson, Mats Univ Luxembourg SnT SEDAN Luxembourg Luxembourg

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

High-performance computing is a prime area for many applications. Majorly, weather and climate forecast applications use the HPC system because it needs to give a good result with low latency. In recent years machine learning and deep learning models have been widely used to forecast the weather. However, to the best of the author's knowledge, many applications do not effectively utilise the HPC system for training, testing, validation, and inference of weather data. Our experiment is to conduct performance modeling and benchmark analysis of weather and climate forecast machine learning models and determine the characteristics between the application, model and the underlying HPC system. Our results will help the researchers improvise and optimise the weather forecast system and use the HPC system efficiently.

关键词： High-Performance computing Deep Learning Weather Forecast distributed computing Performance modeling Benchmark Analysis

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Preserving Near-Optimal Gradient Sparsification Cost for Scalable distributed Deep Learning 24

Preserving Near-Optimal Gradient Sparsification Cost for Sca...

引用

24th ieee/ACM international Symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Yoon, Daegun Oh, Sangyoon ETRI Daejeon South Korea Ajou Univ Suwon South Korea

ISBN: (纸本)9798350395679;9798350395662

Communication overhead is a major obstacle to scaling distributed training systems. Gradient sparsification is a potential optimization approach to reduce the communication volume without significant loss of model fidelity. However, existing gradient sparsification methods have low scalability owing to inefficient design of their algorithms, which raises the communication overhead significantly. In particular, gradient build-up and inadequate sparsity control methods degrade the sparsification performance considerably. Moreover, communication traffic increases drastically owing to workload imbalance of gradient selection between workers. To address these challenges, we propose a novel gradient sparsification scheme called ExDyna. In ExDyna, the gradient tensor of the model comprises fined-grained blocks, and contiguous blocks are grouped into non-overlapping partitions. Each worker selects gradients in its exclusively allocated partition so that gradient build-up never occurs. To balance the workload of gradient selection between workers, ExDyna adjusts the topology of partitions by comparing the workloads of adjacent partitions. In addition, ExDyna supports online threshold scaling, which estimates the accurate threshold of gradient selection on-the-fly. Accordingly, ExDyna can satisfy the user-required sparsity level during a training period regardless of models and datasets. Therefore, ExDyna can enhance the scalability of distributed training systems by preserving near-optimal gradient sparsification cost. In experiments, ExDyna outperformed state-of-the-art sparsifiers in terms of training speed and sparsification performance while achieving high accuracy.

关键词： distributed deep learning gradient sparsification scalability

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AdaDiffAD: Adaptively Segmenting Diffusion Models for Time Series Anomaly Detection in Dynamic JointCloud Environment 30

AdaDiffAD: Adaptively Segmenting Diffusion Models for Time S...

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30th ieee international conference on Parallel and distributed systems, ICPADS 2024

作者： Ma, Chao Yi, Lin Zhou, Linjiang Wang, Zepeng Shi, Xiaochuan Zhu, Weiping Wuhan University School of Cyber Science and Engineering Wuhan China Wuhan University School of Computer Science Wuhan China

ISBN: (纸本)9798331515966

Time series anomaly detection is one kind of critical time series analytical tasks, which is widely applied to various real-world applications. Recently, the diffusion models have shown promising performance on time series imputation for anomaly detection. And the high computing requirements of the diffusion models naturally extend their computation paradigm from the centralized computing to the cloud computing, and then further to JointCloud computing which allows the diffusion models to be deployed across multiple clouds. However, the JointCloud computing paradigm faces the challenge of task offloading over multiple nodes across multiple clouds. Unfortunately, most of existing task offloading methods overlook the dynamic nature of network conditions among clouds. To address this issue, we propose a time series anomaly detection approach named AdaDiffAD by adaptively segmenting diffusion models in JointCloud environment with dynamic network conditions. Specifically, we design a task offloading strategy by segmenting the denoising process of the diffusion model onto both the edge clouds and central clouds, and utilizing the edge cloud results directly for anomaly detection when the network condition is not ideal. By conducting comprehensive experiments on seven datasets, the experimental results demonstrate that our proposed AdaDiffAD always achieves lower time consumption while maintaining competitive anomaly detection performance compared to the state-of-the art without adaptive task offloading strategy. © 2024 ieee.

关键词： Anomaly Detection Diffusion Model JointCloud computing Model Segmentation Task Offloading Time Series

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Combining distributed and Central Control for Self-Adaptive systems of systems 42

Combining Distributed and Central Control for Self-Adaptive ...

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Kroeher, Christian Gerling, Lea Schmid, Klaus Univ Hildesheim Inst Comp Sci Hildesheim Germany

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

distributed and central control are two complementary paradigms to establish self-adaptation in software systems. Both approaches have their individual benefits and drawbacks, which lead to significant trade-offs regarding certain software qualities when designing such systems. The significance of these trade-offs even increases the more complex the target system becomes. In this paper, we present our work-in-progress towards an integrated control approach, which aims at providing the best of both control paradigms. We present the basic concepts of this multi-paradigm approach and outline its inherent support for complex system hierarchies. Further, we illustrate the vision of our approach using application scenarios from the smart energy grid as an example for self-adaptive systems of systems.

关键词： Control design self-adaptation self-adaptive system system of systems emergent systems

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Revelations and Roadmaps: A Chronicle of Success, Insights Gained, and Future Horizons in the landscape of Indian Undergraduate PDC Education 30

Revelations and Roadmaps: A Chronicle of Success, Insights G...

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30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Bayyapu, Neelima Manipal Acad Higher Educ Manipal Inst Technol Dept CSE Manipal India

ISBN: (纸本)9798350383782;9798350383799

At present, there is a notable focus on Parallel and distributed computing (PDC) initiatives within the realm of undergraduate engineering education in India. Owing to differences in education systems across borders, along with variations in university policies, these efforts must be curated to cater to specific stakeholders, ensuring the achievement of the desired outcomes. Understanding such scenarios is crucial for the landscape of Indian undergraduate PDC education. This paper unveils a success story of implementing PDC at the undergraduate level for the past decade and a half, offering valuable insights gathered along this extended journey. Reflecting the idea that "every master was once a beginner," the narrative unfolds to inspire and empower educators who are just starting out. Whether introducing or already incorporating PDC education into the curriculum, this account is crafted to uplift and guide. Amidst the ongoing initiatives across the country, the time has come to progress and elevate PDC education beyond its current status. This paper presents a summary of potential efforts that the PDC community in India can explore for such initiatives.

关键词： PDC (Parallel and distributed computing) Undergraduate (UG) engineering education PDC efforts Curriculum design Empower educators University policies Success story of PDC Elevate PDC education

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