An incentive scheduling mechanism for peer-to-peer video streaming

P2P video streaming networks are found as a scalable solution and an alternative for traditional client–server based video streaming over the Internet. One of the significant issues affecting the success of any P2P streaming network is cooperation between peers. Practical observations have proved the prevalence of free riders in P2P networks that degrade their performance. To solve this problem, using incentive mechanisms, which encourage peers to contribute more in the network, is necessary. In this paper, we designed and proposed a distributed and scalable incentive mechanism for mesh based P2P video streaming networks. In the proposed approach the contribution of the peers is measured and maintained in a distributed fashion. Furthermore, we proposed an incentive sending side scheduler in which peers are served based on their contribution in the network. Our simulation evaluations show the efficiency of the proposed approach in improving the overall perceived video quality by the non-free rider nodes and consequently in the whole network.     

File size models for shared content over the BitTorrent Peer-to-Peer network

Peer-to-Peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. In this work we perform a measurement study of the content shared over the popular BitTorrent P2P file sharing network. We mathematically model the file size distributions of shared files after categorizing them into Audio, Video, Archive and CD image classes. For each of these categories we look into the most popular shared file formats and investigate their file size statistics. This provides an important milestone to building a realistic simulation framework for P2P systems, and for future analytical modeling of P2P networks.     

Simulating peer-to-peer cloud resource scheduling

Resource scheduling in large-scale distributed systems, such as grids and clouds, is difficult due to the size, dynamism, and volatility of resources. These resources are eclectic and autonomous, and may exhibit different usage policies, levels of participation, capabilities, local load, and reliability. Moreover, applications are likely to exhibit various patterns and levels, and distributed resources may organize into various different overlay topologies for information and query dissemination. Researchers have proposed a wide variety of approaches and policies for mapping offered load onto resources and for solving the various component parts of the scheduling problem. However, production clouds and grids may be underutilized, and may not exhibit the load to effectively characterize all of the scheduling system inputs. The composition of large-scale systems is also changing, potentially to include more individual and peer-to-peer resources. These factors will influence the effectiveness of proposed scheduling solutions. Therefore, a simulation environment is necessary to study different approaches under different scenarios, especially those that are expected, but that are not currently characteristic of existing systems. This article describes a general-purpose peer-to-peer simulation environment that allows a wide variety of parameters, protocols, strategies and policies to be varied and studied. To provide a proof of concept, utilization of the simulation environment is presented in a large-scale distributed system problem that includes a core model and related mechanisms. In particular, this article presents a definition and possible peer-to-peer solutions for the large-scale scheduling problem. Moreover, this article describes a general simulation model, some policies that can be varied, an implementation, and some sample results.     

Organisation and management of shared documents in super-peer networks based semantic hierarchical cluster trees

A super-peer semantic network defines the semantic groups that client peers and their super peers use to connect with each other according to the semantic similarity of shared documents in peers. This will result in a query that can be evaluated locally thus reducing query process time and the amount of communication required to obtain related data. In this study, new algorithms are proposed to arrange shared documents in peers into hierarchical cluster tree structures, and links will be set up to connect semantically related super peers. During query evaluation, only related peers will be searched thus the complexity of computing is significantly reduced. Consideration is also given to automatically deciding the threshold for clustering and the construction and other essential operations for hierarchical cluster trees. The experiment results illustrate the features of the approach developed in this study.     

CloudRank-D: benchmarking and ranking cloud computing systems for data processing applications

With the explosive growth of information, more and more organizations are deploying private cloud systems or renting public cloud systems to process big data. However, there is no existing benchmark suite for evaluating cloud performance on the whole system level. To the best of our knowledge, this paper proposes the first benchmark suite CloudRank-D to benchmark and rank cloud computing systems that are shared for running big data applications. We analyze the limitations of previous metrics, e.g., floating point operations, for evaluating a cloud computing system, and propose two simple metrics: data processed per second and data processed per Joule as two complementary metrics for evaluating cloud computing systems. We detail the design of CloudRank-D that considers representative applications, diversity of data characteristics, and dynamic behaviors of both applications and system software platforms. Through experiments, we demonstrate the advantages of our proposed metrics. In several case studies, we evaluate two small-scale deployments of cloud computing systems using CloudRank-D.     

一种基于区域分割的多尺度遥感图像融合方法

光谱保持和高分辨率保留是图像融合的重要问题,提出了一种区域分割和小波变换相结合的多尺度遥感图像融合方法。首先对经过配准的待融合图像进行小波变换,然后对变换后的低频系数进行基于区域标准差的分割,将低频系数分为目标信息和背景信息,接着对目标信息采取基于绝对值的融合,对背景信息采用基于灰度误差的融合。对小波变换后的高频系数采用基于清晰度的融合规则,最后进行小波逆变换得到融合图像。将该方法和几种常用融合方法进行对比分析,结果表明:该方法在有效地保持多光谱影像光谱信息的同时,可以有效地提高融合影像的空间细节信息,有利于后续进行信息提取和图像分类。     

穷举法在程序设计中的应用

穷举法是程序设计中经常用到的一种算法,用来解决一些用常规的数学方法无法解决的问题.文章通过两个典型的例子对穷举法的思路和有关注意事项进行了分析,供编程学习者参考.     

光学遥感舰船目标识别方法

提出一种基于粗糙集理论和分层判别回归技术的光学遥感舰船目标识别方法。该方法首先提出新的光学遥感舰船识别特征———面积比编码,并与四类特征组合作为备选特征;然后基于粗糙集理论按同可区分度来计算各备选特征的重要性权值,自动选择出对正确识别贡献较大的特征组合;最后根据分层判别回归原理生成分类判决树来识别光学遥感舰船目标。实验结果表明,本文方法在识别精度和速度方面优于最近邻和支持向量机方法,且通用可行。     

An ET-Based Two-Phase Method for the Calibration and Application of Distributed Hydrological Models

Water Resources Management - Parameter calibration is a core process for the application of distributed hydrological models, which depends strongly on river runoff records. However, the sparse...     

"源—网—厂—河"一体化智慧排水系统的构建设计

为提升城市排水系统运行的效率和质量,开发设计了基于GIS的"源—网—厂—河"一体化智慧排水系统,其层次结构包括基础层、感知层、网络层、应用层.应用层是整个智慧排水系统的核心组成,主要包含六大模块——排水许可信息化管理、拓扑分析、健康评估、终端监测、模型计算及动态巡检.应用该系统构建排水系统模型计算,集成终端在线监测校核分析,分析诊断排水系统健康问题和拓扑问题,定位病害区域,自动制定最优巡检方案,分配派单于外业,外业巡检结果反馈回智慧排水系统,最终实现城市排水一体化智慧管理.