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14th IEEE Int. Conf. on Computational Science and Engineering, CSE 2011, the 11th International Symposium on Pervasive systems, Algorithms, and Networks, I-SPAN 2011, and the 10th IEEE Int. Conf. on Ubiquitous computing and Communications, IUCC 2011

作者： Shi, Xuanhua Jiang, Hongbo He, Ligang Jin, Hai Wang, Chonggang Yu, Bo Wang, Fei Service Computing Technology and System Lab./Cluster and Grid Computing Lab. School of Computer Huazhong University of Science and Technology China Department of Electronics and Information Engineering Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom InterDigital Communications Corp. United States

ISBN: (纸本)9780769544779

Emerging data centers may host a large number of applications that consume CPU power, memory, and I/O resources. Previous studies focus on the allocation of resources in order to perfectly satisfy the demands seen in the current cycle, and the existing application placement algorithms are all based on applications. The existing application placement algorithms in literature assume that the consumption of system resources is proportional to the level of workloads submitted to the system. In this paper, we revealed that it may not be the case in some circumstances. Based on this observation, we design and implement an application placement framework, called EAPAC, for data centers. The developed framework is able to judiciously allocate to application servers a proper mixture of different types of application requests as well as an appropriate number of requests in each type. Further, we investigate the issue of resource conflicts among different applications when there exist concurrent requests in the system. We have conducted extensive experiments to evaluate the performance of the developed framework. The experiment results show that compared with the existing studies, EAPAC can improve the performance by 30% in terms of the reply rate. Especially, when there are concurrent requests in the system, the performance can be improved by 100%. © 2011 IEEE.

关键词： Computer programming

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UaaS: Software Update as a Service for the IaaS Cloud

UaaS: Software Update as a Service for the IaaS Cloud

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IEEE International Conference on services computing (SCC)

作者： Kai Liu Deqing Zou Hai Jin Cluster and Grid Computing Lab Services Huazhong University of Science and Technology Wuhan China

With the development of cloud computing, there is a growing number of virtual machines (VMs) in the IaaS cloud. The VM owners can install different kinds of software on demand. However, if the software is not updated in time, it would be a great threat to the security of the cloud. But for the VM owners, it is a tedious task to keep all of the installed software up to date. In this paper we present a new software update model called UaaS (Update as a Service) to handle the VM (online or offline) update automatically. Multiple VMs share one software update service and multiple update strategies are provided for single software, which can be customized at any time. The ability of UaaS has been evaluated by our experiments, and the results show that UaaS can provide software update service successfully and complete update task for lots of VMs with multiple update strategies efficiently.

关键词： Software Databases Security Linux Monitoring Computer architecture Algorithm design and analysis

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Autonomic and Trusted computing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Laurence T. Yang Hai Jin Jianhua Ma Theo Ungerer

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Ubiquitous Intelligence and computing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Jianhua Ma Hai Jin Laurence T. Yang Jeffrey J.-P. Tsai

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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VNIX: Managing Virtual Machines on clusters

VNIX: Managing Virtual Machines on Clusters

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Japan-china Joint Workshop on Frontier of Computer Science and technology (FCST)

作者： Xuanhua Shi Haoyu Tan Song Wu Hai Jin Services Computing Technology and System Laboratory/Cluster and Grid Computing Laboratory School of Computer Huazhong University of Science and Technology Wuhan China

With the development of virtualization technology, it¿s desirable to deploy virtual machines to high performance clusters used for data centers. VNIX, developed in services computing technology and system lab, tries to help cluster administrators to manage a large number of virtual machines (VMs) distributed on clusters. To reduce the complexity of virtualization management, VNIX provides a whole-set of tools for monitoring, deploying, controlling, and configuring virtual machines on clusters. In addition to those basic management functions, VNIX also offers a number of specialized tools for clustering VMs. Due to the complex dynamic environment in clusters, it¿s challenging to design such tools. In this paper, we present the design of VNIX, and we describe several use cases of managing VMs in clusters with VNIX. Such use cases illustrate various ways of using VNIX to simplify the management work and to improve resource utilization.

关键词： Virtual machining Voice mail Resource management grid computing Virtual machine monitors Virtual manufacturing technology management Computers Condition monitoring Resource virtualization

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Duo: Improving Data Sharing of Stateful Serverless Applications by Efficiently Caching Multi-Read Data

Duo: Improving Data Sharing of Stateful Serverless Applicati...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Zhuo Huang Hao Fan Chaoyi Cheng Song Wu 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 Computer Science and Technology Huazhong University of Science and Technology China

A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ***, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1×-2.1× and reduces the data sharing overhead by 25%-62%.

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MeG2: In-Memory Acceleration for Genome Graphs Analysis

MeG2: In-Memory Acceleration for Genome Graphs Analysis

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Design Automation Conference

作者： Yu Huang Long Zheng Haifeng Liu Zhuoran Zhou Dan Chen Pengcheng Yao Qinggang Wang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG 2 , a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG 2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG 2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG 2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1× ), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG 2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.

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Scalable Optimal Margin Distribution Machine

arXiv

引用

arXiv 2023年

作者： Wang, Yilin Cao, Nan Zhang, Teng Shi, Xuanhua Jin, Hai 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

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the latest margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts. However, it suffers from the ubiquitous scalability problem regarding both computation time and memory storage as other kernel methods. This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method. For nonlinear kernels, we put forward a novel distribution-aware partition method to make the local ODM trained on each partition be close and converge fast to the global one. When linear kernel is applied, we extend a communication efficient SVRG method to accelerate the training further. Extensive empirical studies validate that our proposed method is highly computational efficient and almost never worsen the generalization. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Enabling Complete Atomicity for Cross-Chain Applications Through Layered State Commitments

Enabling Complete Atomicity for Cross-Chain Applications Thr...

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Reliable Distributed systems

作者： Yuandi Cai Ru Cheng Yifan Zhou Shijie Zhang Jiang Xiao Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Cross-chain Decentralized Applications (dApps) are increasingly popular for their ability to handle complex tasks across various blockchains, extending beyond simple asset transfers or swaps. However, ensuring all dependent transactions execute correctly together, known as complete atomicity, remains a challenge. Existing works provide financial atomicity, protecting against monetary loss, but lack the ability to ensure correctness for complex tasks. In this paper, we introduce Avalon, a transaction execution framework for cross-chain dApps that guarantees complete atomicity for the first time. Avalon achieves this by introducing multiple state layers above the native one to cache state transitions, allowing for efficient management of these state transitions. Most notably, for concurrent cross-chain transactions, Avalon resolves not only intra-chain conflicts but also addresses potential inconsistencies between blockchains via a novel state synchronization protocol, enabling serializable cross-chain execution. We implement Avalon using smart contracts in Cosmos ecosystem and evaluate its commitment performance, demonstrating acceptable latency and gas consumption even under conflict cases.

关键词： Atomic layer deposition Smart contracts Ecosystems Decentralized applications Consensus protocol Synchronization Reliability Interoperability

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