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作者： Lu, Feng Liu, Ruoxue Li, Wei Jin, Hai Zomaya, Albert Y. Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Center for Distributed and High Performance Computing School of Information Technologies The University of Sydney Sydney Australia

Mobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with *** technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data-intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m2. Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3× over the single application and can save an average of 25% power over the single application. © 2019 John Wiley & Sons, Ltd.

关键词： Heterogeneous networks

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi 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 School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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Short: CICC: A fine-grained, semantic-aware, and transparent approach to preventing permission leaks for android permission managers 8

Short: CICC: A fine-grained, semantic-aware, and transparent...

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8th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2015

作者： Wang, Daibin Yao, Haixia Li, Yingjiu Jin, Hai Zou, Deqing Deng, Robert H. 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 School of Information Systems Singapore Management University Singapore

ISBN: (纸本)9781450336239

Android's permission system offers an all-or-nothing installation choice for users. To make it more flexible, users may choose a popular app tool, called permission manager, to selectively grant or revoke an app's permissions at runtime. A fundamental requirement for such permission manager is that the granted or revoked permissions should be enforced faithfully. However, we discover that none of existing permission managers meet this requirement due to permission leaks. To address this problem, we propose CICC, a finegrained, semantic-aware, and transparent approach for any permission managers to defend against the permission leaks. Compared to existing solutions, CICC is fine-grained because it detects the permission leaks using call-chain information at the component instance level, instead of at the app level or component level. The fine-grained feature enables it to generate a minimal impact on the usability of running apps. CICC is semantic-aware in a sense that it manages call-chains in the whole lifecycle of each component instance. CICC is transparent to users and app developers, and it requires minor modification to permission managers. Our evaluation shows that CICC incurs relatively low performance overhead and power consumption. Copyright 2015 ACM.

关键词： Android Call-chain Permission leaks Permission manager

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Network and Parallel computing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Hai Jin Daniel Reed Wenbin Jiang

ISBN: (数字)9783540322467

ISBN: (纸本)9783540298106

These proceedings contain the papers presented at the 2005 IFIP International Conference on Network and Parallel computing (NPC 2005), held in Beijing, China, between November 30 and December 3, 2005. The goal of the conference was to establish an international forum for engineers and scientists to present their ideas and experiences in network and parallel computing. A total of 320 submissions were received in response to our Call for Papers. These papers were from the following countries or regions: Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Lux- burg, Nepal, Netherlands, Taiwan, United Arab Emirates, and United States. Each submission was sent to at least three *** paper was judged - cording to its originality, innovation, readability, and relevance to the expected ***,atotalof68paperswereretainedfor- clusionin *** the 68 papers,48 wereacceptedasfull papers for presentation at the conference. We also accepted 20 papers as short papers for a possible brief presentation at the conference, followed by discussion during a poster session. Thus, only 21% of the total submissions could be included in the ?nal program.

关键词： Theory of Computation Computer Communication Networks Software Engineering Operating systems Algorithm Analysis and Problem Complexity Information systems Applications (incl. Internet)

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SHelp: Automatic self-healing for multiple application instances in a virtual machine environment

SHelp: Automatic self-healing for multiple application insta...

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作者： Chen, Gang Jin, Hai Zou, Deqing Zhou, Bing Bing Qiang, Weizhong Hu, Gang Cluster and Grid Computing Lab. Services Computing Technology and System Lab. Huazhong University of Science and Technology Wuhan 430074 China Centre for Distributed and High Performance Computing School of Information Technologies University of Sydney NSW 2006 Australia

ISBN: (纸本)9780769542201

When multiple instances of an application running on multiple virtual machines, an interesting problem is how to utilize the fault handling result from one application instance to heal the same fault occurred on other sibling instances, and hence to ensure high service availability in a cloud computing environment. This paper presents SHelp, a lightweight runtime system that can survive software failures in the framework of virtual machines. It applies weighted rescue points and error virtualization techniques to effectively make applications bypass the faulty path. A two-level storage hierarchy is adopted in the rescue point database for applications running on different virtual machines to share error handling information to reduce the redundancy and to more effectively and quickly recover from future faults caused by the same bugs. A Linux prototype is implemented and evaluated using four web server applications that contain various types of bugs. Our experimental results show that SHelp can make server applications to recover from these bugs in just a few seconds with modest performance overhead. © 2010 IEEE.

关键词： Software reliability

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D-overlay: A delay-aware overlay for gaming services

D-overlay: A delay-aware overlay for gaming services

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3rd International Conference on Innovative computing Information and Control, ICICIC'08

作者： Xiaofei, Liao Linchen, Yu Hai, Jin Hong, Yao Cluster and Grid Computing Lab. Services Computing Technology and System Lab. Huazhong University of Science and Technology Wuhan 430074 China School of Computer Science and Technology China University of Geosciences Wuhan 430074 China

ISBN: (纸本)9780769531618

Peer-to-peer (P2P) has become an important computing model because of its adaptation, selforganization and autonomy. But how to organize dynamic nodes in P2P networks efficiently for gaming message passing services is still a challenge. This paper present an approach, named D-overlay, a delay-aware overlay for gaming services, to implement node clustering based on link delay of nodes communications in the P2P network. This approach measures the delays among peers, explores the node connectivity together with the connection quality. Then, it builds a service ring for each peer according to its latency status, and keeps the low latency among neighbors by periodical adjustments. D-overlay improves the extensibility and reliability. One system, called PKTown, has been deployed based on D-overlay in CERNET of China. © 2008 IEEE.

关键词： Message passing

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei 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 Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction 37

ES-Mask: Evolutionary Strip Mask for Explaining Time Series ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Sun, Yifei Song, Cheng Lu, Feng Li, Wei Jin, Hai Zomaya, Albert Y. 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 Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia

ISBN: (纸本)9781577358800

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

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RTGA: A Redundancy-free Accelerator for High-Performance Temporal Graph Neural Network Inference 24

RTGA: A Redundancy-free Accelerator for High-Performance Tem...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Yu, Hui Zhang, Yu Tan, Andong Lu, Chenze Zhao, Jin Liao, Xiaofei Jin, Hai Liu, Haikun National Engineering Research Center for Big Data Technology and System Service 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)9798400706011

Temporal Graph Neural Network (TGNN) has attracted much research attention because it can capture the dynamic nature of complex networks. However, existing solutions suffer from redundant computation overhead and excessive off-chip communications for TGNN inference because they often rely on redundant graph sampling and repeatedly fetching the features and vertex memory. This paper proposes a redundancy-free accelerator, RTGA, for high-performance TGNN inference. Specifically, RTGA proposes a redundancy-aware execution approach with temporal tree into a novel accelerator design to effectively eliminate unnecessary data processing for fewer redundant computations and off-chip communications and also designs a temporal-aware data caching method to improve data locality for TGNN. We have implemented and evaluated RTGA on a Xilinx Alveo U280 FPGA card. Compared with cutting-edge software solutions (i.e., TGN and TGL) and hardware solutions (i.e., BlockGNN and FlowGNN), RTGA improves the performance of TGNN inference by an average of 473.2x, 87.4x, 8.2x, and 6.9x and saves energy by 542.8x, 102.2x, 9.4x, and 8.3x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spatio-temporal data

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HMvisor: dynamic hybrid memory management for virtual machines

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Science China(Information Sciences) 2021年第9期64卷 74-89页

作者： Dang YANG Haikun LIU Hai JIN Yu ZHANG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and Technology Huazhong University of Science and Technology

Emerging non-volatile memory(NVM) technologies promise high density, low cost and dynamic random access memory(DRAM)-like performance, at the expense of limited write endurance and high write energy consumption. It is more practical to use NVM combining with the traditional DRAM. However, the hybrid memory management such as page migration becomes more challenging in a virtualization environment because virtual machines(VMs) are unaware of the memory heterogeneity. In this paper, we propose HMvisor, a hypervisor and VM coordinated hybrid memory management mechanism to better utilize DRAM and NVM resources. HMvisor exposes the memory heterogeneity to VMs by mapping virtual NUMA nodes to different physical NUMA nodes. We propose a lightweight and efficient page migration mechanism by decoupling page hotness tracking from page migration. HMvisor performs those operations in the hypervisor and VMs separately, without disrupting the execution of VMs. We also propose a memory resource trading policy to adjust the capacity of DRAM and NVM for each VM, with the monetary cost unchanged. We implement our prototype system based on QEMU/KVM and evaluate it with several benchmarks. Experimental results show that HMvisor can reduce 50% of write traffic to NVM with less than 5% performance overhead. Moreover, the hybrid memory adjustment scheme in HMvisor can significantly improve application performance by up to 30×.

关键词： hypervisor virtual machine non-volatile memory hybrid memory management

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