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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

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Contrastive Learning for Robust Android Malware Familial Classification

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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

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing systems

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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International Conference on Data Engineering

作者： Long Zheng Xiangyu Ye Haifeng Liu Qinggang Wang Yu Huang Chuangyi Gui 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 Laboratory Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering UNSW Sydney Australia

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating" version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value" that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency.

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An Online Market Mechanism for Edge Emergency Demand Response via Cloudlet Control

An Online Market Mechanism for Edge Emergency Demand Respons...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Shutong Chen Lei Jiao Lin Wang Fangming Liu Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China University of Oregon USA VU Amsterdam The Netherlands

The computing frontier is moving from centralized mega datacenters towards distributed cloudlets at the network edge. We argue that cloudlets are well-suited for participation in Emergency Demand Response (EDR) programs due to their enormous energy consumption and flexible workload distribution, while existing EDR mechanisms for clouds and colocation datacenters are not suitable for cloudlets. We propose a novel online market mechanism, EdgeEDR, to incentivize cloudlets to participate in EDR, featuring multiple cloudlet-specific designs. At a high level, we observe that cloudlet operators can dynamically switch on/off entire cloudlets to compensate for the energy reduction required by the power grid. We formulate a long-term social cost minimization problem and decompose it into a series of one-round procurement auctions. In each auction instance, we propose to let the cloudlet tenants bid with cost functions of their service quality degradation tolerance, and let the cloudlet operator choose the service quality, allocate the workload, and shut down the cloudlets. Via rigorous analysis, we exhibit that our bidding policy is individually rational and truthful; our workload distribution algorithm has near-optimal performance in each auction; and our overall online algorithm achieves a provable competitive ratio. We further confirm the performance of our mechanism through extensive trace-driven simulations.

关键词： Cloud computing Delays Switches Generators Degradation Procurement Load management

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The roles of urban buildings and vegetation in adjusting seasonal and daily air temperature 4

The roles of urban buildings and vegetation in adjusting sea...

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4th ISPRS Geospatial Week 2019

作者： Lan, Y. Huang, Z. Guo, R. Zhan, Q. Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services Shenzhen University China Guangdong Key Laboratory of Urban Informatics Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Urban Design Wuhan University Wuhan China

Exploring the spatiotemporal patterns of the relationships between urban indicators and urban temperature is essential to improve the mitigation effectiveness when we intend to adjust built environment for moderating urban thermal environment. In this study, RS, GIS technology and statistical methods were involved to investigate the spatiotemporal patterns of the impacts of urban buildings and vegetation on Air Temperature (AT). Building Density (BD) and Normalized Difference Vegetation Index (NDVI) are the indicators for urban buildings and vegetation respectively. The objectives of this study are: 1) to determine an appropriate scale for examining the building-AT relationships and vegetation-AT relationships;2) to explore the seasonal and daily characteristics of these relationships;and 3) to compare the effects of urban buildings and vegetation. The results show that, for both summer and winter, a scale of 200–250 m is optimal for examining building-AT relationships, and 960–1020 m is the desirable scale for studying vegetation-AT relationships. Based on the optimal scales, we find that for both buildings and vegetation, they only significantly impact night-time temperature in both summer and winter. For seasonal comparison, the building-AT relationships and vegetation-AT relationships are relatively stronger in summer than in winter, which are indicated by R-square of the regression results. When comparing the effects of urban building and vegetation, we find that increasing vegetation is more effective than reduce buildings to achieve the same air temperature reduction. Our findings are conducive to generating space-time targeted Urban Heat Island (UHI) mitigation strategies. © Authors 2019.

关键词： Vegetation

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Information Value Evaluation Model for ILM

Information Value Evaluation Model for ILM

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Hai Jin Muzhou Xiong Song Wu Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769532639

The first step towards the information lifecycle management is to differentiate information by values. This paper presents an evaluation model for information value. The idea of the model is from commodity value discipline in economic area. The output of information value evaluation model is determined by information itself which is like commodity price is determined by use-value in economic area. The output of the model is changed around the real information value and controlled by the relationship between information supply and demand. The output of the model is determined by factors from three aspects that are information itself, information supply, and information demand. When applying the model into traditional information management approaches, such as replication, data migration, and backup, those mechanisms control the output of the model through adjusting the ability of information supply.

关键词： Biological system modeling Supply and demand Computational modeling Bandwidth Data models Economics Analytical models

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Real-Time Activity Recognition in Wireless Body Sensor Networks: From Simple Gestures to Complex Activities

Real-Time Activity Recognition in Wireless Body Sensor Netwo...

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IEEE International Conference on Embedded and Real-Time computing systems and Applications (RTCSA)

作者： Liang Wang Tao Gu Hanhua Chen Xianping Tao Jian Lu State Key Laboratory of Novel Software Technology Nanjing University China Department of Mathematics and Computer Science University of Southern Denmark Denmark Services Computing Technology and System Laboratory Huazhong University of Science and Technology China

Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a real time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast, lightweight template matching algorithm to detect gestures at the sensor node level, and then use a discriminative pattern based real-time algorithm to recognize high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average precision of 94.9%, an average recall of 82.5%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network communication cost by 60.2%.

关键词： Real time systems Body sensor networks Acceleration Wireless communication Algorithm design and analysis Gesture recognition

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Carbon-Aware Load Balancing for Geo-distributed Cloud services

Carbon-Aware Load Balancing for Geo-distributed Cloud Servic...

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International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Zhi Zhou Fangming Liu Yong Xu Ruolan Zou Hong Xu John C.S. Lui Hai Jin Key Laboratory of Services Computing Technology and System Huazhong University of Science and Technology China Department of Electrical and Computer Engineering University of Toronto Canada The Chines University of Hong Kong

Recently, data center carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the data centers to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed data centers. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission.

关键词： Carbon dioxide Electricity Servers Optimization Fuels Power demand Real-time systems

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Load Balancing Routing Algorithm for Ad Hoc Networks

Load Balancing Routing Algorithm for Ad Hoc Networks

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Wenbin Jiang Zhaojing Li Chunqiang Zeng Hai Jin Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan China

In ad hoc networks, when the load is heavy, the performance of on-demand routing protocols, such as DSR protocol, will suffer large degradation. For instance, the absence of balanced distribution of network flow among nodes in the whole network is highly prone to producing heavy-load key nodes. This results in fast energy consumption of these key nodes, and finally decreasing the survival time of the network caused by their premature failures. To address this problem, a novel algorithm called cross-layer based load balancing algorithm for DSR (CLB-DSR) is proposed. In CLB-DSR, the cross-layer strategy is adopted to exchange the information between data link layer and network layer. Simulation results show that the improved routing protocol using CLB-DSR can effectively balance network load, improve the network throughput and reduce the end-to-end delay, the rate of data dropped.

关键词： Load management Ad hoc networks Routing protocols Energy consumption Delay effects Storms Mobile computing Sensor systems Clustering algorithms Computers

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GIRAFFE: A scalable distributed coordination service for large-scale systems

GIRAFFE: A scalable distributed coordination service for lar...

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IEEE International Conference on Cluster computing

作者： Xuanhua Shi Haohong Lin Hai Jin Bing Bing Zhou Zuoning Yin Sheng Di Song Wu Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Centre for Distributed and High Performance Computing School of Information Technologies University of Sydney GraphSQL Inc. INRIA and Argonne National Laboratory

ISBN: (纸本)9781479955497

The scale of cloud services keeps increasing over time, significantly introducing huge challenges in system manageability and reliability. Designing coordination services in cloud is the right track to solve the above problems. However, existing coordination services (e.g., Chubby and ZooKeeper) only perform well in read-intensive scenario and small ensemble scales. To this end, we propose Giraffe, a scalable distributed coordination service. There are three important contributions in our design. (1) Giraffe organizes coordination servers using interior-node-disjoint trees for better scalability. (2) Giraffe employs a novel Paxos protocol for strong consistency and fault-tolerance. (3) Giraffe supports hierarchical data organization and in-memory storage for high throughput and low latency. We evaluate Giraffe on a high performance computing test-bed. The experimental results show that Giraffe gains much better write performance than ZooKeeper when server ensemble is large. Giraffe is nearly 300% faster than ZooKeeper on update operations when ensemble size is 50 servers. Experiments also show that Giraffe reacts and recovers more quickly than ZooKeeper against node failures.

关键词： cloud computing fault tolerant computing large-scale systems protocols public domain software reliability Giraffe Paxos protocol ZooKeeper cloud services fault-tolerance hierarchical data organization memory storage scalable distributed coordination service Color Proposals Protocols Scalability Servers Topology Vegetation Consistency Coordination Service Distributed Cloud system Fault Tolerance

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