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International Conference on big data and Information Analytics (bigDIA)

作者： Qun Zhang Jibing Wu Mengna Zhu Xuan Li Liwei Qian Lihua Liu Laboratory for Big Data and Decision Colleage of System Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

In the international relations and foreign policy research, international negotiation data is of great importance for assisting scholars in analyzing patterns of interaction between nations, conflicts of interest, and prospects for cooperation. The analysis of events and their storylines within international negotiation data is instrumental for better managing international relations, formulating relevant policies, and predicting economic trends, which holds substantial importance. Current research surrounding event comprehension is primarily geared towards general news or financial domains, focusing mainly on single-document studies, which do not effectively cater to the study of multi-source heterogeneous international negotiation documents as encountered in reality. Therefore, this work has involved the collection of relevant negotiation documents in the economic and agricultural domains. By the combination of key techniques including clustering algorithms, in-context learning and time series analysis, we completed the construction of event storylines from single to multiple documents in international negotiation data. Based on these event storylines, we also analyzed the relevant key issues and main trends, deepening the understanding of document information. We validated the effectiveness of our workflow on our collected data. Furthermore, we developed a system based on the proposed workflow and implemented visualization, making the analysis of international negotiation documents more convenient and user-friendly.

关键词： Economics Instruments Time series analysis Focusing data visualization Clustering algorithms International relations big data Market research

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Software-Defined, Fast and Strongly-Consistent data Replication for RDMA-Based PM datastores

Software-Defined, Fast and Strongly-Consistent Data Replicat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Haodi Lu Haikun Liu Chencheng Ye Xiaofei Liao Fubing Mao Yu Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology China

Modern storage systems typically replicate data on multiple servers to provide high reliability and availability. However, most commercially-deployed datastores often fail to offer low latency, high throughput, and strong consistency at the same time. This paper presents Whale, a Remote Direct Memory Access (RDMA) based primary-backup replication system for in-memory datastores. Whale achieves both low latency and strong consistency by decoupling metadata multicasting from data replication for all backup nodes, and using an optimistic commitment mechanism to respond to client write requests earlier. Whale achieves high throughput by propagating writes from the primary node to backup nodes asynchronously via RDMA-optimized chain replication. To further reduce the cost of data replication, we design a log-structured datastore to fully exploit the advantages of one-sided RDMA and Persistent Memory (PM). We implement Whale on a cluster equipped with PM and InfiniBand RDMA networks. Experimental results show that Whale achieves much higher throughput and lower latency than state-of-the-art replication protocols.

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GraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow

GraphFly: Efficient Asynchronous Streaming Graphs Processing...

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Supercomputing Conference

作者： Dan Chen Chuangyi Gui Yi Zhang Hai Jin Long Zheng Yu Huang Xiaofei Liao National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665454452

Existing streaming graph processing systems typically adopt two phases of refinement and recomputation to ensure the correctness of the incremental computation. However, severe redundant memory accesses exist due to the unnecessary synchronization among independent edge updates. In this paper, we present GraphFly, a high-performance asynchronous streaming graph processing system based on dependency-flows. GraphFly features three key designs: 1) Dependency trees (D-trees), which helps quickly identify independent graph updates with low cost; 2) Dependency-flow based processing model, which exploits the space-time dependent co-scheduling for cache efficiency; 3) Specialized graph data layout, which further reduces memory accesses. We evaluate GraphFly, and the results show that GraphFly significantly outperforms state-of-the-art systems KickStarter and GraphBolt by 5.81× and 1.78× on average, respectively. Also, GraphFly scales well with different sizes of update batch and compute resources.

关键词： Costs High performance computing Memory management Layout data models Synchronization

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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ChestBox: Enabling Fast State Sharing for Stateful Serverless computing with State Functions

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2024年

作者： Zhang, Xinmin Wu, Song Gu, Lin He, Qiang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan Hubei430074 China

This paper presents ChestBox, a novel approach that utilizes state functions to facilitate low-latency state sharing for stateful serverless computing. When an application function needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-The-Art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%. © 2016 IEEE.

关键词： Storage allocation (computer)

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Towards Efficient data-Centric Robust Machine Learning with Noise-based Augmentation

arXiv

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arXiv 2022年

作者： Liu, Xiaogeng Wang, Haoyu Zhang, Yechao Wu, Fangzhou Hu, Shengshan School Of Cyber Science And Engineering National Engineering Research Center For Big Data Technology And System Services Computing Technology And System Lab Hubei Engineering Research Center On Big Data Security Huazhong University Of Science And Technology Wuhan430074 China

The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noisedbased data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Finedge: A Dynamic Cost-Efficient Edge Resource Management Platform for NFV Network

Finedge: A Dynamic Cost-Efficient Edge Resource Management P...

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International Workshop on Quality of Service

作者： Miao Li Qixia Zhang Fangming Liu National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, a dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.

关键词： Resource management Noise measurement Packet loss Servers Throughput Quality of service

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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IEEE International Conference on systems, Man and Cybernetics

作者： Guotong Wu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC.

关键词： Training Accuracy Machine learning Approximation algorithms Graph neural networks Approximation methods Cybernetics Testing

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Heat to Power: Thermal Energy Harvesting and Recycling for Warm Water-Cooled datacenters

Heat to Power: Thermal Energy Harvesting and Recycling for W...

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Annual International Symposium on Computer Architecture, ISCA

作者： Xinhui Zhu Weixiang Jiang Fangming Liu Qixia Zhang Li Pan Qiong Chen Ziyang Jia National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Warm water cooling has been regarded as a promising method to improve the energy efficiency of water-cooled datacenters. In warm water-cooling systems, hot spots occur as a common problem where the hybrid cooling architecture integrating thermoelectric coolers (TECs) emerges as a new remedy. Equipped with this architecture, the inlet water temperature can be raised higher, which provides more opportunities for heat recycling. However, currently, the heat absorbed from the server components is ejected directly into the water without being recycled, which leads to energy wasting. In order to further improve the energy efficiency, we propose Heat to Power (H2P), an economical and energy-recycling warm water cooling architecture, where thermoelectric generators (TEGs) harvest thermal energy from the “used” warm water and generate electricity for reusing in datacenters. Specifically, we propose some efficient optimization methods, including an economical water circulation design, fine-grained adjustments of the cooling setting and dynamic workload scheduling for increasing the power generated by TEGs. We evaluate H2P based on a real hardware prototype and cluster traces from Google and Alibaba. Experiment results show that TEGs equipped with our optimization methods can averagely generate 4.349 W, 4.203 W, and 3.979 W (4.177 W averagely) electricity on one CPU under the drastic, irregular and common workload traces, respectively. The power reusing efficiency (PRE) can reach 12.8%~16.2% (14.23% averagely) and the total cost of ownership (TCO) of datacenters can be reduced by up to 0.57%.

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Hardware-Supported Remote Persistence for Distributed Persistent Memory

Hardware-Supported Remote Persistence for Distributed Persis...

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Supercomputing Conference

作者： Zhuohui Duan Haodi Lu Haikun Liu Xiaofei Liao Hai Jin Yu Zhang Song Wu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA prim-itives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.

关键词： High performance computing Throughput Proposals Network interfaces Guidelines

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