Performance prediction for the high performance computer system is of great importance for designing, implementing, and optimizing system. As a widely used technique for predicting performance, simulation method attra...
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Performance prediction for the high performance computer system is of great importance for designing, implementing, and optimizing system. As a widely used technique for predicting performance, simulation method attracts more and more attention from the research community. Based on analyzing the problems in the current performance simulation techniques, we present a key idea of the performance simulator for SMP system based on event-driven. We propose the framework of SMP-SIM and implement it based on MPICH2. The simulation results show that, our simulation technique has the advantages of high accuracy and simulation performance.
Buffer overflow is one of the most dangerous and common vulnerabilities in CPS software. Despite static and dynamic analysis, manual analysis is still heavily used which is useful but costly. Human computation harness...
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Buffer overflow is one of the most dangerous and common vulnerabilities in CPS software. Despite static and dynamic analysis, manual analysis is still heavily used which is useful but costly. Human computation harness humans' time and energy in a way of playing games to solve computational problems. In this paper we propose a human computation method to detect buffer overflows that does not ask a person whether there is a potential vulnerability, but rather a random person's idea. We implement this method as a game called Bodhi in which each player is shown a piece of code snippet and asked to choose whether their partner would think there is a buffer overflow vulnerability at a given position in the code. The purpose of the game is to make use of the rich distributed human resource to increase effectiveness of manual detection for buffer overflows. The game has been proven to be efficient and enjoyable in practice.
The malicious code detection based on behaviors has proved effective. But there are high false positives and high false negatives when using this method. Because the behaviors are always out-of-order and redundant. To...
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The malicious code detection based on behaviors has proved effective. But there are high false positives and high false negatives when using this method. Because the behaviors are always out-of-order and redundant. To solve these problems, this paper proposes a detection method based on statistical analysis. Firstly, this method uses association rules to sort out the behaviors, and then we can get the integrated and accurate behavior sequences. Secondly, by using the association algorithm we can pick up the signatures of behavior sequences. In addition, this method can detect the signatures to judge the threat based on statistical analysis. Experimental results indicate that it can reduce both the false positives and the false negatives effectively.
In this paper, we apply tree-structured conditional random field (TCRF) to all-words word sense disambiguation (WSD), where the graphical structure of TCRF is the dependency syntax tree produced by Minipar. The extrem...
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Malicious code detection based on behaviors is the development direction of anti-virus techniques. However, the current detection methods based on this theory expose several problems such as the unclearness of behavio...
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Malicious code detection based on behaviors is the development direction of anti-virus techniques. However, the current detection methods based on this theory expose several problems such as the unclearness of behavior sequence analysis and the high false negatives. For this situation, this paper proposes a malicious code detection method based on least-squares estimation. In this method, it correlates program behaviors with time and subject-object, and then constitutes an accurate and complete behavior sequence. It can provide a preprocessing method for the subsequent detection. In order to improve the accuracy and intelligence of malicious code detection, we introduce the concept of expert subjective degree. By modeling malicious samples based on least-squares estimation we can train the Expert Subjective Degree Vector (ESDV) and simulate experts to judge the threat values of malicious codes. Experiments show that this method is more accurate than the current ways to detect the malicious codes which execute themselves in sub-period and sub-process ways, so it can be used as an effective complement of the current anti-virus software.
In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource ef...
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In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource efficiently and fairly is an interesting and practical problem. In this paper we proposed a cooperative spectrum sharing strategy (RT-CSS) for the relay-trading mode from the fairness view. The cooperative SUs are gathered in a cooperative sharing group (CSG), and contribution metric (CM) is proposed to measure each CSG member's contribution to CSG as well as benefit from CSG. The adjustment of CM can guarantee the fairness and efficiency of spectrum sharing. The numerical simulation shows that RT-CSS can achieve better performance than the sense-uncooperative mode.
The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks ...
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The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units （GPU）. This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.
Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that...
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Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.
There is an increasing need to build scalable distributed systems over the Internet infrastructure. However the development of distributed scalable applications suffers from lack of a wide accepted virtual computing e...
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There is an increasing need to build scalable distributed systems over the Internet infrastructure. However the development of distributed scalable applications suffers from lack of a wide accepted virtual computing environment. Users have to take great efforts on the management and sharing of the involved resources over Internet, whose characteristics are intrinsic growth, autonomy and diversity. To deal with this challenge, Internet-based Virtual Computing Environment （iVCE） is proposed and developed to serve as a platform for distributed scalable applications over the open infrastructure, whose kernel mechanisms are on-demand aggregation and autonomic collaboration of resources. In this paper, we present a programming language for iVCE named Owlet. Owlet conforms with the conceptual model of iVCE, and exposes the iVCE to application developers. As an interaction language based on peer-to-peer content-based publish/subscribe scheme, Owlet abstracts the Internet as an environment for the roles to interact, and uses roles to build a relatively stable view of resources for the on-demand resource aggregation. It provides language constructs to use 1） distributed event driven rules to describe interaction protocols among different roles, 2） conversations to correlate events and rules into a common context, and 3） resource pooling to do fault tolerance and load balancing among networked nodes. We have implemented an Owlet compiler and its runtime environment according to the architecture of iVCE, and built several Owlet applications, including a peer-to-peer file sharing application. Experimental results show that, with iVCE, the separation of resource aggregation logic and business logic significantly eases the process of building scalable distributed applications.
Nowadays, more and more scientific applications are moving to cloud computing. The optimal deployment of scientific applications is critical for providing good services to users. Scientific applications are usually to...
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Nowadays, more and more scientific applications are moving to cloud computing. The optimal deployment of scientific applications is critical for providing good services to users. Scientific applications are usually topology-aware applications. Therefore, considering the topology of a scientific application during the development will benefit the performance of the application. However, it is challenging to automatically discover and make use of the communication pattern of a scientific application while deploying the application on cloud. To attack this challenge, in this paper, we propose a framework to discover the communication topology of a scientific application by pre-execution and multi-scale graph clustering, based on which the deployment can be optimized. Comprehensive experiments are conducted by employing a well-known MPI benchmark and comparing the performance of our method with those of other methods. The experimental results show the effectiveness of our topology-aware deployment method.
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