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.
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.
Human itineraries are often initiated by some general intentions and will be optimized after considering all kinds of constraints and available information. This paper proposes a category-based itinerary recommendatio...
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Human itineraries are often initiated by some general intentions and will be optimized after considering all kinds of constraints and available information. This paper proposes a category-based itinerary recommendation framework to help the user transfer from intentions to itinerary planning, which join physical trajectories and information of location based social networks. The main contributions are: (1) Build the category based activity scheduling model;(2) Design and implement the category tree based POI (point or interest) query strategy and algorithm;(3) Propose the Voronoi graph based GPS trajectory analysis method to build traffic information networks;(4) Combine social networks with traffic information networks to implement category based recommendation by ant colony algorithm. The study conducts experiments on datasets from FourSquare and GeoLife project. A test on satisfaction of recommended items is also performed. Results show that the satisfaction reaches 80% in average.
Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer ...
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Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer localization algorithm named as Minimum-circlecovering based localization (MCCL). Comparing with the existing solutions that rely on the wireless propagation parameters, MCCL only depends on the location information of sensor nodes at the border of the jammed region. MCCL uses the plane geometry knowledge, especially the minimum circle covering technique, to form an approximate jammed region, and hence the center of the jammed region is treated as the estimated position of the jammer. Simulation results showed that MCCL is able to achieve higher accuracy than other existing solutions in terms of jammer's transmission range and sensitivity to nodes' density.
Power management has become one of the first-order considerations in high performance computing field. Many recent studies focus on optimizing the performance of a computer system within a given power budget. However,...
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ISBN:
(纸本)9780769545769
Power management has become one of the first-order considerations in high performance computing field. Many recent studies focus on optimizing the performance of a computer system within a given power budget. However, most existing solutions adopt fixed period control mechanism and are transparent to the running applications. Although the application-transparent control mechanism has relatively good portability, it exhibits low efficiency in accelerator-based heterogeneous parallel systems. In typical accelerator-based parallel systems, different processing units have largely different processing speeds and power consumption. Under a given power constraint, how to choose the processor to be slowed down and how to schedule a parallel task onto different processors for the maximum performance are different from those in homogeneous systems and have not been well studied. From the motivating example in this paper, we could find that in order to efficiently harness the heterogeneous parallelprocessing, one should not only perform dynamic voltage/frequency scaling (DVFS) to meet the power budget, but also tune the parallel task scheduling to adapt to the changes. In this paper, we propose a heterogeneity-aware peak power management, which extends existing application-transparent power controller with an application-aware power controller. Firstly, we theoretically analyze the conditions for the maximum performance given a power budget for heterogeneous systems. Based on this result, we provide a power-constrained parallel task partition algorithm, which coordinates parallel task partition and voltage scaling for heterogeneous processing units to achieve the optimal performance given a system power budget. Finally, we evaluate the proposed method on a typical CPU-GPU heterogeneous system, and validate the superiority of application-aware power controller over the existing method.
Nowadays, the performance of large-scale parallel computer system improves continuously, and the system scale becomes extremely large. Performance prediction has become an important approach to guide system design, im...
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ISBN:
(纸本)9783642233234
Nowadays, the performance of large-scale parallel computer system improves continuously, and the system scale becomes extremely large. Performance prediction has become an important approach to guide system design, implementation and optimization. Simulation method is the most widely used performance prediction technology for large-scale parallel computer system. In this paper, after analyzing the extant problems, we proposed a novel execution-driven performance simulation technology based on process-switch. We designed a simulation framework named PS-SIM, and implemented a prototype system based on MPICH2. Finally, we verified the proposed approach by experiments. Experimental results show that the approach has high accuracy and simulation performance.
Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and ...
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Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and related products to customers. However, there are still some difficulties for customers to adopt cloud computing, in which many security issues exist, because data for a customer is stored and processed in cloud, not in a local machine. This paper briefly introduces cloud computing and its key concepts. In particularly, we intend to discuss security requirements and security issues involving data, application and virtualization in cloud computing, as well as current solutions to these issues.
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.
We consider the maximal vector problem on uncertain data, which has been recently posed by the study on processing skyline queries over a probabilistic data stream in the database context. Let D n be a set of n points...
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We consider the maximal vector problem on uncertain data, which has been recently posed by the study on processing skyline queries over a probabilistic data stream in the database context. Let D n be a set of n points in a d-dimensional space and q (0 < q 1) be a probability threshold; each point in D n has a probability to occur. Our problem is concerned with how to estimate the expected size of the probabilistic skyline, which consists of all the points that are not dominated by any other point in D n with a probability not less than q. We prove that the upper bound of the expected size is O(min{n, (- ln q)(ln n) d-1 }) under the assumptions that the value distribution on each dimension is independent and the values of the points along each dimension are distinct. The main idea of our proof is to find a recurrence about the expected size and solve it. Our results reveal the relationship between the probability threshold q and the expected size of the probabilistic skyline, and show that the upper bound is poly-logarithmic when q is not extremely small.
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