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作者： Zhang, Ruoyu Huang, Shiqiu Qi, Zhengwei Guan, Haibing School of Software Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China

The evolution of computer science has exposed us to the growing gravity of security problems and threats. Dynamic taint analysis is a prevalent approach to protect a program from malicious behaviors, but fails to provide any information about the code which is not executed. This paper describes a novel approach to overcome the limitation of traditional dynamic taint analysis by integrating static analysis into the system and presents framework SDCF to detect software vulnerabilities with high code coverage. Our experiments show that SDCF is not only able to provide efficient runtime protection by introducing an overhead of 4.16× based on the taint tracing technique, but is also capable of discovering latent software vulnerabilities which have not been exploited, and achieve code coverage of more than 90%. © 2011 Elsevier Ltd. All rights reserved.

关键词： Data flow analysis

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Frontier-based multi-robot map exploration using particle swarm optimization

Frontier-based multi-robot map exploration using particle sw...

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IEEE Symposium on Swarm Intelligence

作者： Wang, Yiheng Liang, Alei Guan, Haibing School of Software Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai 200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai 200240 China

ISBN: (纸本)9781612840529

Exploring an unknown environment using team of autonomous mobile robots is an important task in many real-world applications. Many existing map exploration algorithms are based on frontier, which is the boundary between unexplored space and known open space. In the context of multiple robots, the main problem of frontier-based algorithm is to choose appropriate target points for the individual robots so that they can efficiently explore the different part of the common area. This paper proposed a novel distributed frontier-based map exploration algorithm using Particle Swarm Optimization model for robot coordination. In this algorithm, the robot keeps moving to the nearby frontier to reduce the size of the unknown region, and is navigated towards frontier far away based on the PSO model after exploring the local area. The exploration is completed when there are no frontier cells on the map. Our algorithm has been implemented and tested both in simulation runs and real world experiment. The result shows that our method has a good scalability and efficiency. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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Rethinking benchmarking for NVM-based file systems 16

Rethinking benchmarking for NVM-based file systems

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7th ACM SIGOPS Asia-Pacific Workshop on systems, APSys 2016

作者： Dong, Mingkai Yu, Qianqian Zhou, Xiaozhou Hong, Yang Chen, Haibo Zang, Binyu Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450342650

Fast, byte-addressable NVM promises near-cache latency and near memory bus throughput for file systems, which is evident by recent development of a number of NVM file systems (NVMFS). However, a key approach to measuring their performance, file system benchmarks, remains unchanged as those for disk-based file systems (DFS). In this paper, we analyze the pitfalls of using DFS benchmarks to evaluate NVMFS and propose several changes to existing DFS benchmarks to better characterize the performance of NVMFS. © 2016 ACM.

关键词： Benchmarking

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NUMA-aware graph-structured analytics 2015

NUMA-aware graph-structured analytics

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20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015

作者： Zhang, Kaiyuan Chen, Rong Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Graph-structured analytics has been widely adopted in a number of big data applications such as social computation, web-search and recommendation systems. Though much prior research focuses on scaling graph-analytics on distributed environments, the strong desire on performance per core, dollar and joule has generated considerable interests of processing large-scale graphs on a single server-class machine, which may have several terabytes of RAM and 80 or more cores. However, prior graph-analytics systems are largely neutral to NUMA characteristics and thus have suboptimal performance. This paper presents a detailed study of NUMA characteristics and their impact on the efficiency of graph-analytics. Our study uncovers two insights: 1) either random or interleaved allocation of graph data will significantly hamper data locality and parallelism;2) sequential inter-node (i.e., remote) memory accesses have much higher bandwidth than both intra- and inter-node random ones. Based on them, this paper describes Polymer, a NUMA-aware graph-analytics system on multicore with two key design decisions. First, Polymer differentially allocates and places topology data, application-defined data and mutable runtime states of a graph system according to their access patterns to minimize remote accesses. Second, for some remaining random accesses, Polymer carefully converts random remote accesses into sequential remote accesses, by using lightweight replication of vertices across NUMA nodes. To improve load balance and vertex convergence, Polymer is further built with a hierarchical barrier to boost parallelism and locality, an edge-oriented balanced partitioning for skewed graphs, and adaptive data structures according to the proportion of active vertices. A detailed evaluation on an 80-core machine shows that Polymer often outperforms the state-of-the-art single-machine graph-analytics systems, including Ligra, X-Stream and Galois, for a set of popular real-world and synthetic grap

关键词： Random access storage

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Powerlyra: Differentiated graph computation and partitioning on skewed graphs 15

Powerlyra: Differentiated graph computation and partitioning...

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10th European Conference on Computer systems, EuroSys 2015

作者： Chen, Rong Shi, Jiaxin Chen, Yanzhe Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450332385

Natural graphs with skewed distribution raise unique challenges to graph computation and partitioning. Existing graph-parallel systems usually use a "one size fits all" design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab), or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX). In this paper, we argue that skewed distribution in natural graphs also calls for differentiated processing on highdegree and low-degree vertices. We then introduce Power-Lyra, a new graph computation engine that embraces the best of both worlds of existing graph-parallel systems, by dynamically applying different computation and partitioning strategies for different vertices. PowerLyra further provides an efficient hybrid graph partitioning algorithm (hybrid-cut) that combines edge-cut and vertex-cut with heuristics. Based on PowerLyra, we design locality-conscious data layout optimization to improve cache locality of graph accesses during communication. PowerLyra is implemented as a separate computation engine of PowerGraph, and can seamlessly support various graph algorithms. A detailed evaluation on two clusters using graph-analytics and MLDM (machine learning and data mining) applications show that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs accordingly, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra. Copyright © 2015 ACM 978-1-4503-3238-5/15/04 $15.00.

关键词： Data mining

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Wavelet neural networks optimized by QPSO for network anomaly detection

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Journal of Computational Information systems 2011年第7期7卷 2452-2460页

作者： Liu, Yuan Ma, Ruhui School of Digital Media Jiangnan University Wuxi 214122 China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer science and Engineering Shanghai Jiaotong University Shanghai 200240 China

In this paper, a novel hybrid algorithm based wavelet neural network (WNN) is proposed for network anomaly detection. This new evolutionary algorithm, which is based on a hybrid of Quantum-Behaved Particle Swarm Optimization (QPSO) and conjugate gradient algorithm (CG), is employed to train WNN. The Quantum-Behaved Particle Swarm Optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. Due to the particles in the multi-dimensional space seeking the best position so quickly, it would result in the dangerous of stagnation, which would make the QPSO impossible to arrive at the global optimum. In order to overcome defects of QPSO, the improved hybrid algorithm was proposed. Experimental result on KDD 99 intrusion detection datasets shows that this WNN using the novel hybrid algorithm has high detection rate while maintaining a low false positive rate. Copyright © 2011 Binary Information Press.

关键词： Intrusion detection

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DICFaaS: Parallel and scalable Data Integrity Computation for Serverless systems 30

DICFaaS: Parallel and Scalable Data Integrity Computation fo...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Xiong, Tianchen Peng, Bo Shanghai Jiao Tong University School of Software Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9798331515966

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to 17 × latency improvement over AWS Lambda and up to 42 × latency improvement over OpenFaaS when the data size is 200M. It also saves up to 6 0% CPU overhead at runtime compared to OpenFaaS. © 2024 IEEE.

关键词： Data Integrity Computation Lossless Hardware Offloading Serverless systems Storage Computation

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VASP: Virtualization assisted security monitor for cross-platform protection 11

VASP: Virtualization assisted security monitor for cross-pla...

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Proceedings of the 2011 ACM Symposium on Applied computing

作者： Zhu, Min Yu, Miao Xia, Mingyuan Li, Bingyu Yu, Peijie Gao, Shang Qi, Zhengwei Liu, Liang Chen, Ying Guan, Haibing School of Software Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China IBM Research China China

ISBN: (纸本)9781450301138

Numerous operating systems have been designed to manage and control system resources with large and complicated features, so they need high security protection. However, previous security applications can not provide adequate protection due to the untrusted execution environment. Furthermore, these security strategies cannot support a universal cross-platform system security requirements. This paper presents VASP, a hypervisor based monitor which allows a trusted execution environment to monitor various malicious behaviors in the operating system. This is achieved by taking advantage of ×86 hardware virtualization and self-transparency technology, and providing a unified security protection to unmodified operating systems such as Linux and Windows. Our design is targeted at establishing a security monitor which resides completely outside of the target OS environment with a negligible overhead. According to the security analysis and performance experiment result, our approach can effectively protect applications and the kernel at a modest overhead of only 0.9% average in Windows XP and 2.6% average in Linux. © 2011 ACM.

关键词： Linux

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Manhattan hashing for large-scale image retrieval 12

Manhattan hashing for large-scale image retrieval

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35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012

作者： Kong, Weihao Li, Wu-Jun Guo, Minyi Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781450316583

Hashing is used to learn binary-code representation for data with expectation of preserving the neighborhood structure in the original feature space. Due to its fast query speed and reduced storage cost, hashing has been widely used for efficient nearest neighbor search in a large variety of applications like text and image retrieval. Most existing hashing methods adopt Hamming distance to measure the similarity (neighborhood) between points in the hashcode space. However, one problem with Hamming distance is that it may destroy the neighborhood structure in the original feature space, which violates the essential goal of hashing. In this paper, Manhattan hashing (MH), which is based on Manhattan distance, is proposed to solve the problem of Hamming distance based hashing. The basic idea of MH is to encode each projected dimension with multiple bits of natural binary code (NBC), based on which the Manhattan distance between points in the hashcode space is calculated for nearest neighbor search. MH can effectively preserve the neighborhood structure in the data to achieve the goal of hashing. To the best of our knowledge, this is the first work to adopt Manhattan distance with NBC for hashing. Experiments on several large-scale image data sets containing up to one million points show that our MH method can significantly outperform other state-of-the-art methods. © 2012 ACM.

关键词： Hamming distance

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Energy efficient allocation of virtual machines in cloud computing environments based on demand forecast

Energy efficient allocation of virtual machines in cloud com...

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7th International Conference on Advances in Grid and Pervasive computing, GPC 2012

作者： Cao, Jian Wu, Yihua Li, Minglu Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783642307669

In cloud computing environments, demands from different users are often handled on virtual machines (VMs) which are deployed over plenty of hosts. Huge amount of electrical power is consumed by these hosts and auxiliary infrastructures that support them. However, demands are usually time-variant and of some seasonal pattern. It is possible to reduce power consumption by forecasting varying demands periodically and allocating VMs accordingly. In this paper, we propose a power-saving approach based on demand forecast for allocation of VMs. First of all, we forecast demands of next period with Holt-Winters' exponential smoothing method. Second, a modified knapsack algorithm is used to find the appropriate allocation between VMs and hosts. Third, a self-optimizing module updates the values of parameters in Holt-Winters' model and determines the reasonable forecast frequency. We carried out a set of experiments whose results indicate that our approach can reduce the frequency of switching on/off hosts. In comparison with other approaches, this method leads to considerable power saving for cloud computing environments. © 2012 Springer-Verlag.

关键词： Electric power utilization

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