检索结果-内蒙古大学图书馆

Supercomputing Conference

作者： Jin Zhao Yu Zhang Xiaofei Liao Liaang He Binzsheng He Haikun Liu Hai Jin Huazhong University of Science and Technology China 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 Wuhan China University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

In modern data centers, massive concurrent graph processing jobs are being processed on large graphs. However, existing hardware/-software solutions suffer from irregular graph traversal and intense resource contention. In this paper, we propose LCCG, a Locality-entric programmable accelerator that augments the many-core processor for achieving higher throughput of Concurrent Graph processing jobs. Specifically, we develop a novel topology-aware execution approach into the accelerator design to regularize the graph traversals for multiple jobs on-the-fly according to the graph topology, which is able to fully consolidate the graph data accesses from concurrent jobs. By reusing the same graph data among more jobs and coalescing the accesses of the vertices' states for these jobs, LCCG can improve the core utilization. We conduct extensive experiments on a simulated 64-core processor. The results show that LCCG improves the throughput of the cutting-edge software system by 11.3~23.9 times with only 0.5% additional area cost. More-over, LCCG gains the speedups of 4.7~10.3, 5.5~13.2, and 3.8~8.4 times over state-of-the-art hardware graph processing accelerators (namely, HATS, Minnow, and PHI, respectively).

关键词： Data centers Costs High performance computing Throughput Software systems Topology Hardware acceleration

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Memory Architecture Supporting Fine-Grained Data Migration

SSRN

引用

SSRN 2022年

作者： Chi, Ye Yue, Jianhui Liao, Xiaofei Liu, Haikun 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 Wuhan430074 China Computer Science Michigan Technological University HoughtonMI United States

We propose Mocha, a non-hierarchical caching architecture that organizes DRAM and NVM in a flat address space physically, but manages DRAM/NVM in a cache/memory hierarchy in this paper. Since the commercial NVM device-Intel Optane DC Persist Memory Modules (DCPMM) actually access the physical media at a granularity of 256 bytes (an Optane block), we manage the DRAM cache at the 256-byte size to adapt to this device feature of Optane. This design not only enables fine-grained data migration and management for the DRAM cache, but also avoids write amplification for Optane DCPMM. We also create a the \textit{ Indirect Address Cache} (IAC) in \textit{Hybrid Memory Controller}(HMC) and propose a reverse address mapping table in the DRAM to speed up address translation and cache replacement. Besides, we exploit an utility-based caching mechanism to filter cold blocks in the NVM, and further improve DRAM cache effectiveness. We implement Mocha in an architecture simulator. Experimental results indicate that MOCHA can improve performance of applications by 8.2\% on average (up to 24.6\%), provide 6.9\% reduction in average energy consumption and 25.9\% reduction in average data migration traffic, compared to the most advanced hybrid memory architecture-HSCC. © 2022, The Authors. All rights reserved.

关键词： Dynamic random access storage

来源：评论

学校读者我要写书评

暂无评论

grid Security Interoperation between CGSP and VEGA

Grid Security Interoperation between CGSP and VEGA

引用

International Conference on Future Generation Communication and Networking (FGCN)

作者： Deqing Zou Hai Jin Xueguang Chen Zongfen Han Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769530482;0769530486

grid computing emerges as an effective technology to couple geographically distributed resources and solve large-scale computational problems in wide area networks. There are several popular grid platforms, such as UNICORE and Globus Toolkit. Many famous grid projects are constructed with these platforms, each of which manages different distributed resources. In order to aggregate resources managed by different grid platforms, it is necessary to implement the interoperation among these grid platforms. Security interoperation is one of key issues for the interoperation. CGSP (chinagrid Support Platform) and VEGA are two popular grid platforms in china. To achieve security interoperation between these two platforms, four mapping policies, including user-user mapping, user-agora mapping, role-user mapping, role-agora mapping, are proposed in this paper. The security interoperation process is proposed based on SAML specification and the mapping policies. We also analyze the flexibility of mapping policies and the security of the interoperation process.

关键词： Security Resource management grid computing Authentication Large-scale systems Computer networks Distributed computing Wide area networks Web services Middleware

来源：评论

学校读者我要写书评

暂无评论

Event-Driven Scheduling for P2P VoD

Event-Driven Scheduling for P2P VoD

引用

chinagrid Annual Conference (chinagrid)

作者： Bin Cheng Hai Jin Xiaofei Liao Bo Li Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

Scheduling is essential in peer-to-peer (P2P) video-on-demand (VoD) applications to obtain desirable user experience and high sharing efficiency. Existing systems and proposals largely utilize scheduling mechanisms that are periodical in nature. We refer these as time-driven scheduling. In this paper we argue that the time-driven scheduling algorithm is not efficient for P2P VoD due to its inadequate resource utilization and inability in quickly adapting to the potential dynamics in such systems. We propose an event-driven scheduling algorithm for P2P VoD systems, in which the scheduler is triggered by events such as peer churn or/and random seeks. We evaluate our design through extensive simulations, and the experimental results show that event-driven scheduling outperforms time-driven scheduling in terms of both user experience and system scalability.

关键词： Scheduling algorithm Dynamic scheduling Processor scheduling Peer to peer computing Large-scale systems Bandwidth Computer science Resource management Discrete event simulation Scalability

来源：评论

学校读者我要写书评

暂无评论

The Content Pollution in Peer-to-Peer Live Streaming systems: Analysis and Implications

The Content Pollution in Peer-to-Peer Live Streaming Systems...

引用

International Conference on Parallel Processing (ICPP)

作者： Sirui Yang Hai Jin Bo Li Xiaofei Liao Hong Yao Xuping Tu Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781424432134

There has been significant progress in the development and deployment of peer-to-peer (P2P) live video streaming systems. However, there has been little study on the security aspect in such systems. Our prior experiences in Anysee exhibit that existing systems are largely vulnerable to intermediate attacks, in which the content pollution is a common attack that can significantly reduce the content availability, and consequently impair the playback quality. This paper carries out a formal analysis of content pollution and discusses its implications in P2P live video streaming systems. Specifically, we establish a probabilistic model to capture the progress of content pollution. We verify the model using a real implementation based on Anysee system; we evaluate the content pollution effect through extensive simulations. We demonstrate that (1) the number of polluted peers can grow exponentially, similar to random scanning worms. This is vital that with 1% polluters, the overall system can be compromised within minutes; (2) the effective bandwidth utilization can be sharply decreased due to the transmission of polluted packets; (3) Augmenting the number of polluters does not imply a faster progress of content pollution, in which the most influential factors are the peer degree and access bandwidth. We further examine several techniques and demonstrate that a hash-based signature scheme can be effective against the content pollution, in particular when being used during the initial phase.

关键词： Pollution Peer to peer computing Bandwidth Streaming media Availability Grippers Topology

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning for Robust Android Malware Familial Classification

引用

IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA 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 Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud

LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce...

引用

IEEE International Conference on Cloud computing technology and Science (CloudCom)

作者： Shadi Ibrahim Hai Jin Lu Lu Song Wu Bingsheng He Li Qi Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China School of Computer Engineering Nanyang Technological University Singapore Information and Technology Department China Development Bank Beijing China

This paper investigates the problem of Partitioning Skew in MapReduce-based system. Our studies with Hadoop, a widely used MapReduce implementation, demonstrate that the presence of partitioning skew causes a huge amount of data transfer during the shuffle phase and leads to significant unfairness on the reduce input among different data nodes. As a result, the applications experience performance degradation due to the long data transfer during the shuffle phase along with the computation skew, particularly in reduce phase. We develop a novel algorithm named LEEN for locality-aware and fairness-aware key partitioning in MapReduce. LEEN embraces an asynchronous map and reduce scheme. All buffered intermediate keys are partitioned according to their frequencies and the fairness of the expected data distribution after the shuffle phase. We have integrated LEEN into Hadoop-0.18.0. Our experiments demonstrate that LEEN can efficiently achieve higher locality and reduce the amount of shuffled data. More importantly, LEEN guarantees fair distribution of the reduce inputs. As a result, LEEN achieves a performance improvement of up to 40% on different workloads.

关键词： Partitioning algorithms Distributed databases Cloud computing Performance evaluation Time factors Degradation Kernel

来源：评论

学校读者我要写书评

暂无评论

Multi-task representation learning with multi-view graph convolutional networks

arXiv

引用

arXiv 2021年

作者： Huang, Hong Song, Yu Wu, Yao Shi, Jia Xie, Xia Jin, Hai National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks. © 2021, CC BY.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Ripple: Toward a Substrate Middleware for Peer-to-Peer Applications

Ripple: Toward a Substrate Middleware for Peer-to-Peer Appli...

引用

International Conference on Innovative computing, Information and Control (ICICIC)

作者： Hong Yao Linchen Yu Hai Jin Sirui Yang Qi Huang Wenjian Jiang Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Geoscience Wuhan China France Telecom Research and Development Center Beijing China

Peers in multiple P2P applications execute overlapped operations with different implementation, like neighbour management, resource publish/lookup, delay investigation, which leads to poor interoperability and unnecessary network load. Peers contribute their resources only when they stay in same application. We propose a P2P middleware, called Ripple, to accomplish common functions in a layered architecture. Ripple manages all peers running various applications in a uniform substrate overlay network. The substrate overlay is designed with a hybrid topology. This hybrid topology does not aim at only a particular type of applications, but also enables the flexibility for various applications. Our work is an attempt to address common needs of P2P applications, which may simplify the development and improve the performance of P2P networks, as well as mitigate the sampling stress to physical networks.

关键词： Peer to peer computing Delay Tin Topology Indexes Substrates Network topology

来源：评论

学校读者我要写书评

暂无评论

A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

引用

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue 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 Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：