检索结果-内蒙古大学图书馆

Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

学校读者我要写书评

暂无评论

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

IEEE conference on Global communications (GLOBEcoM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

关键词：

Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks

学校读者我要写书评

暂无评论

Green Federated Learning over Cloud-RAN with Limited Frontha...

communications and Networking (Meditcom), IEEE International Mediterranean conference on

作者： Jiali Wang Yijie Mao Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN.

关键词：

CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xia, Zeke Hu, Ming Yan, Dengke Xie, Xiaofei Li, Tianlin Li, Anran Zhou, Junling Chen, Mingsong The MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application East China Normal University Shanghai200062 China Singapore Management University Singapore Nanyang Technological University Singapore Yale University United States Nanjing University of Science and Technology Nanjing China

Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massive AIoT devices, respectively. To address the above challenges, we present a novel asynchronous FL approach named CaBaFL, which includes a hierarchical Cache-based aggregation mechanism and a feature Balance-guided device selection strategy. CaBaFL maintains multiple intermediate models simultaneously for local training. The hierarchical cache-based aggregation mechanism enables each intermediate model to be trained on multiple devices to align the training time and mitigate the straggler issue. In specific, each intermediate model is stored in a low-level cache for local training and when it is trained by sufficient local devices, it will be stored in a high-level cache for aggregation. To address the problem of imbalanced data, the feature balance-guided device selection strategy in CaBaFL adopts the activation distribution as a metric, which enables each intermediate model to be trained across devices with totally balanced data distributions before aggregation. Experimental results show that compared to the state-of-the-art FL methods, CaBaFL achieves up to 9.26X training acceleration and 19.71% accuracy improvements. copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China

In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device adopts quantized neural networks (QNNs) to train a local FL model and transmits the quantized model parameter to the remote radio heads (RRHs). Each RRH receives the signals from devices over the wireless link and forwards the signals to the CS via the fronthaul link. We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN. Based on the proposed energy consumption model, we formulate an energy minimization problem that optimizes the fronthaul rate allocation, user transmit power allocation, and QNN precision levels while satisfying the limited fronthaul capacity constraint and ensuring the convergence of the proposed FL model to a target accuracy. To solve this problem, we analyze the convergence rate and propose efficient algorithms based on the alternative optimization technique. Simulation results show that the proposed FL framework can significantly reduce energy consumption compared to other conventional approaches. We draw the conclusion that the proposed framework holds great potential for achieving a sustainable and environmentally-friendly FL in Cloud-RAN. copyright © 2023, The Authors. All rights reserved.

关键词： Radio access networks

Hyperbolic Graph Diffusion Model

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wen, Lingfeng Tang, Xuan Ouyang, Mingjie Shen, Xiangxiang Yang, Jian Zhu, Daxin Chen, Mingsong Wei, Xian MoE Engineering Research Center of Hardware/Software Co-design Technology and Application East China Normal University China School of Communication and Electronic Engineering East China Normal University China Information Engineering University Quanzhou Normal University China

Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a 48% improvement in the quality of graph generation with highly hierarchical structures. copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

Archer: adaptive memory compression with page-association-rule awareness for high-speed response of mobile devices 25

学校读者我要写书评

暂无评论

Archer: adaptive memory compression with page-association-ru...

Proceedings of the 23rd USENIX conference on File and Storage Technologies

作者： Changlong Li Zongwei Zhu Chao Wang Fangming Liu Fei Xu Edwin H.-M. Sha Xuehai Zhou School of Computer Science and Technology East China Normal University and Jianghuai Advance Technology Center Hefei China and MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application School of Software Engineering University of Science and Technology of China Hefei China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Huazhong University of Science and Technology and Peng Cheng Laboratory School of Computer Science and Technology East China Normal University

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate associationrule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art.

关键词：

Federated Linear Bandit Learning via Over-the-air computation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Jiali Jiang, Yuning Liu, Xin Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Automatic Control Laboratory EPFL Switzerland ShanghaiTech University China

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (Aircomp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings. © 2023, CC BY-NC-SA.

关键词： Fading channels

SAMPLE EFFICIENT LUNG SEGMENTATION USING GROUP STRUCTURED coNDITIONAL VARIATIONAL DATA IMPUTATION

学校读者我要写书评

暂无评论

SAMPLE EFFICIENT LUNG SEGMENTATION USING GROUP STRUCTURED CO...

2021 IEEE International conference on Multimedia and Expo, ICME 2021

作者： Li, Yan Cao, Guitao Cao, Wenming MOE Research Center for Software/Hardware Co-Design Engineering East China Normal University China College of Information Engineering Shenzhen University China

ISBN: (纸本)9781665438643

Patients infected with coVID-19 can lead to their Chest X-rays (CXRs) with opacifications rendered regions, which may produce incomplete lung segmentation in automated image analysis models. To tackle this issue, we propose a Group structured conditional Variational data Imputation model to capture the missing data accurately with conditional distribution, where the high-dimensional probability distribution is narrow down to a small latent space to account for unobserved features. This work particularly arises in the fight against coVID-19 that effectively modeling a segmentation of plausible can be presented to a subsequent automated risk scoring and treatment. We train this model with limited CXRs data to demonstrate the abilities on the task of data imputation and proved to be effective though with relatively small datasets. © 2021 IEEE

关键词： coVID-19

DCFG: Discovering Directional counterFactual Generation for Chest X-rays

学校读者我要写书评

暂无评论

DCFG: Discovering Directional CounterFactual Generation for ...

2021 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Yan Liu, Shasha Wu, Chunwei Xi, Xidong Cao, Guitao Cao, Wenming East China Normal University MOE Research Center for Software/Hardware Co-Design Engineering China Shenzhen University College of Information Engineering China

ISBN: (纸本)9781665401265

While Deep Neural Networks (DNNs) are achieving state-of-the-art performance on medical domains across a variety of tasks, the need for explainability of model predictions in these high-stakes tasks is still lacking. Current for the explainability in model predictions potentially relies on the supervised counterfactual generation that is time-consuming and direction uncontrollable. Yet, the counterfactual generation needs to be easy to implement and have a controllable direction. In light of this trend, we propose an approach for the unsupervised latent direction search of black-box models that are steerable to the user by enabling the user to effectively explore counterfactual generation in a directional way, without relying on domain- or data-specific assumptions. To identify these explainable directions, we use Principal component Analysis (PCA), a general manifold learning framework to extract low-dimensional subspaces based on a local noise injection of the pre-trained generative model, so that a small perturbation in the subspaces would provide enough change in the resulting data. With experiments on three real-world CXR datasets involving 6 tasks, we find that our approach is capable of learning explainable predictions that discard unrelated confounding factors. Moreover, our method enables practitioners to edit directions to better understand which features are used for predictions. © 2021 IEEE.

关键词： Forecasting