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2023 World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States 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 Huazhong University of Science and Technology China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalab.lity, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalab.e. © 2023 ACM.

关键词： Graph neural networks

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PipeEdge: A Trusted Pipelining Collab.rative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collab.rate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collab.rate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collab.rative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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A Semantic Double-Buffer Based Approach to Enhance Semantic Web Search

A Semantic Double-Buffer Based Approach to Enhance Semantic ...

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International Conference on the Digital Society

作者： Ling Chen Hai Jin Sheng Di Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

With the development of semantic Web, search efficiency is becoming a challenging issue. To address this problem, we design an approach which mainly has two features. First, we explore potential semantic relationship between different objects and make full use of them to build a semantic buffer on server end, enhancing search speed. Second, we put another relatively small semantic buffer on each client so as to adapt to each individual user's interests, further improving the whole search efficiency and reducing server's load. Through testing on a practical platform, we testified that this semantic double-buffer based search approach can not only effectively reduce user's request response time, but considerably release server's load, bringing a high scalab.lity.

关键词： Semantic Web Testing Resource description framework Algorithm design and analysis Indexing grid computing Computers Delay Scalab.lity Information retrieval

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Building dynamic integrity protection for multiple independent authorities in virtualization-based infrastructure

Building dynamic integrity protection for multiple independe...

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2009 10th IEEE/ACM International Conference on grid computing, grid 2009

作者： Cheng, Ge Jin, Hai Zou, Deqing Zhang, Xinwen Li, Min Yu, Chen Xiang, Guofu Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Samsung Information Systems America San Jose CA United States

ISBN: (纸本)9781424451494

In grid and cloud computing infrastructures, the integrity of a computing platform is a critical security requirement in order to provide secure and honest computing environments to service providers and resource consumers. However, due to the fact that software components running on a single platform are usually provided and maintained by different authorities which are potentially untrusted to each other, the problem to monitor and protect runtime system integrity become very challenging and has not been well addressed yet. In this paper, we present a virtualization based dynamic integrity protection method which ensures that only appropriate authorities can control over their components without interfering with other component providers or authorities. In our solution, integrity requirements defined by the authorities of upper components (e.g., service middleware and applications) are respected by preventing the underlying components (e.g., operating system) from exposing their sensitive data, which can be caused by update of the underlying components or other malicious actions. We implement our solution on Xen-based platform, and our evaluation results show that the solution is effective for integrity protection with acceptable performance overhead. © 2009 IEEE.

关键词： Virtualization

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POSTER: Layrub: Layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems

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ACM SIGPLAN Notices 2018年第1期53卷 405-406页

作者： Liu, Bo Jiang, Wenbin Jin, Hai Shi, Xuanhua Ma, Yang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450349116

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities of memory reuse on both intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that cannot be run on one single GPU. © 2018 Owner/Author.

关键词： Deep neural networks

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VMgrid: A Virtual Machine supported grid middleware

VMGrid: A Virtual Machine supported grid middleware

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IEEE International Conference on Granular computing (GRC)

作者： Song Wu Wei Zhu Wenbin Jiang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424425129

Many grid middleware such as Globus Toolkit and GPE have started to support virtual machines. They implement the remote control of virtual machine by grid service and make use of virtual machinespsila function of quick deployment to construct the execution environment of grid application fast. In order to support virtual machine in grid environment, VMgrid (Virtual Machine grid) is presented. All services in VMgrid are implemented based on secure shell host protocol (SSH) while Globus Toolkitpsilas Virtual Workspace is on WSRF. This paper gives a clear description of VMgrid. Some comparisons with Globus Toolkitpsilas Virtual Workspace in performance are made in this paper. Performance results show that VMgrid virtual workspace atomic services are more effective than that of Globus.

关键词： Virtual machining Middleware grid computing Operating systems Software Libraries Computers

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Adaptive multi-round scheduling strategy for divisible workloads in grid environments

Adaptive multi-round scheduling strategy for divisible workl...

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International Conference on Information Networking

作者： Yongcai Tao Hai Jin Song Wu Xuanhua Shi Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424445264

Scheduling is the key to divisible workload execution. UMR (uniform multi-round) algorithm potentially performs near optimal by improving overlap of communication and computation. However, it is questioned how a static schedule works effectively in dynamic grid environment. The paper proposes an adaptive divisible workload scheduling system, which can adjust the schedule in a proactive way. An adaptive UMR-based multi-round algorithm (called AUMR) is presented and evaluated. In AUMR, if the run-time resource monitor notifies the scheduler of any resource changes, the scheduler will evaluate its impact and adjust the schedule if necessary. The experiment results show a considerable performance improvement by AUMR in dynamic grid environment.

关键词： Adaptive scheduling Processor scheduling grid computing Dynamic scheduling Monitoring Scheduling algorithm Robustness Delay Computers clustering algorithms

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Multi-version Execution for the Dynamic Updating of Cloud Applications

Multi-version Execution for the Dynamic Updating of Cloud Ap...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Feng Chen Weizhong Qiang Hai Jin Deqing Zou Duoqiang Wang Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

Softwares usually need to be updated to fix bugs or add new features. On the other hand, some critical softwares, such as cloud applications, need to provide service continuously, thus should be updated without downtime. Conventional Dynamic Software Updating (DSU) systems try to update programs while running, but they hardly consider the communication of the program to be updated with other programs, which may lead to some inconsistency problems. We handle the problem with an improved DSU system by using multi-version execution. When a new update arrives, instead of updating the application to the new version, we fork a new process of the old version and dynamically update it to the new version, then make these two versions run concurrently until the update finishes. We implement a prototype system called MUC (Multi-vesion for Updating of Cloud) on Linux. To verify our prototype, we apply MUC to cloud applications Redis and Ice cast, and evaluate the overhead of MUC at runtime.

关键词： Servers Software Synchronization Prototypes Linux Analytical models Process control

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Evaluating Latency-Sensitive Applications' Performance Degradation in Datacenters with Restricted Power Budget

Evaluating Latency-Sensitive Applications' Performance Degra...

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International Conference on Parallel Processing

作者： Song Wu Chuxiong Yan Haibao Chen Hai Jin Wei Guo Zhen Wang Deqing Zou Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Tencent Inc

ISBN: (纸本)9781467375887

For datacenters with limited power supply, restricting the servers' power budget (i.e., the maximal power provided to servers) is an efficient approach to increase the server density (the server quantity per rack), which can effectively improve the cost-effectiveness of the datacenters. However, this approach may also affect the performance of applications in servers. Hence, the prerequisite of adopting the approach in datacenters is to precisely evaluate the application performance degradation caused by restricting the servers' power budget. Unfortunately, existing evaluation methods are inaccurate because they are either improper or coarse-grained, especially for the latency-sensitive applications widely deployed in datacenters. In this paper, we analyze the reasons why state-of-the-art methods are not appropriate for evaluating the performance degradation of latency-sensitive applications in case of power restriction, and we propose a new evaluation method which can provide a fine-grained way to precisely describe and evaluate such degradation. We verify our proposed method by a real-world application and the traces from Tencent's datecenter with 25328 servers. The experimental results show that our method is much more accurate compared with the state of the art, and we can significantly increase datacenter efficiency by saving servers' power budget while maintaining the applications' performance degradation within controllab.e and acceptable range.

关键词： Servers degradation of performance power budget evaluation method state of the art Budgets

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A high-performance inter-domain data transferring system for virtual machines

Journal of Software

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Journal of Software 2010年第2期5卷 206-213页

作者： Li, Dingding Jin, Hai Shao, Yingzhe Liao, Xiaofei Han, Zongfen Chen, Kai Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Virtual machine technology can enhance server utilization and consolidation on an individual physical machine. In a growing number of contexts, many of which re-quire high-performance networking inside of the system. Unfortunately, the development of virtualization technology is inclined to application isolation as yet, leads high communication overheads between virtual machines which are resident on the same physical machine, and also limits the achievable network performance. Some efforts have been paid to bridge the network I/O performance gap between the virtualization and native environment, but the amelioration is not satisfactory. In this paper, we propose a system called IDTS (Inter-Domain Transferring system) which uses the shared memory between different domains (also called hosted or guest OSes) to expand the communication band-width and shorten the latency for network applications running in the co-resident VMs. Evaluations are also presented to prove IDTS's availab.lity and highly efficiency. We believe that IDTS is an effective solution for the inter-domain communication on Xen virtualization platform. © 2010 Academy Publisher.

关键词： Virtualization

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