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网站建设流量是怎么回事,吉林省城乡建设部网站,软件开发案例展示,建站平台外贸在fifth.cc的基础上#xff0c;源代码只设定了NewReno的策略#xff0c;在此基础上#xff0c;我对此进行改进#xff0c;添加了Vegas和Cubic算法的对比分析#xff0c;直接在终端打印输出三种算法的优劣#include ns3/applications-module.h #include n…在fifth.cc的基础上源代码只设定了NewReno的策略在此基础上我对此进行改进添加了Vegas和Cubic算法的对比分析直接在终端打印输出三种算法的优劣#include ns3/applications-module.h #include ns3/core-module.h #include ns3/internet-module.h #include ns3/network-module.h #include ns3/point-to-point-module.h #include fstream #include iostream #include vector #include map #include iomanip #include algorithm using namespace ns3; // 存储每种算法的统计信息 struct AlgorithmStats { std::string name; std::vectordouble timePoints; std::vectoruint32_t cwndValues; double startTime; double endTime; uint32_t maxCwnd; uint32_t minCwnd; double avgCwnd; double finalCwnd; double growthRate; int dataPoints; AlgorithmStats() : name(), startTime(0), endTime(0), maxCwnd(0), minCwnd(UINT32_MAX), avgCwnd(0), finalCwnd(0), growthRate(0), dataPoints(0) {} AlgorithmStats(const std::string n) : name(n), startTime(0), endTime(0), maxCwnd(0), minCwnd(UINT32_MAX), avgCwnd(0), finalCwnd(0), growthRate(0), dataPoints(0) {} void AddDataPoint(double time, uint32_t cwnd) { timePoints.push_back(time); cwndValues.push_back(cwnd); if (dataPoints 0) { startTime time; } endTime time; maxCwnd std::max(maxCwnd, cwnd); minCwnd std::min(minCwnd, cwnd); finalCwnd cwnd; dataPoints; double sum 0; for (uint32_t val : cwndValues) { sum val; } avgCwnd sum / cwndValues.size(); if (cwndValues.size() 10) { double recentGrowth 0; for (size_t i cwndValues.size() - 10; i cwndValues.size() - 1; i) { double timeDiff timePoints[i1] - timePoints[i]; if (timeDiff 0) { recentGrowth (cwndValues[i1] - cwndValues[i]) / timeDiff; } } growthRate recentGrowth / 9.0; } } }; // 全局存储算法统计数据 std::mapstd::string, AlgorithmStats algorithmStats; // 为每个算法创建独立的回调函数 static void CwndChangeCubic(uint32_t oldCwnd, uint32_t newCwnd) { double currentTime Simulator::Now().GetSeconds(); algorithmStats[Cubic].AddDataPoint(currentTime, newCwnd); static double lastPrintTime 0; if (currentTime - lastPrintTime 2.0) { std::cout std::fixed std::setprecision(2) Cubic currentTime s: CWND newCwnd bytes ( std::setprecision(1) newCwnd/1024.0 KB) std::endl; lastPrintTime currentTime; } } static void CwndChangeNewReno(uint32_t oldCwnd, uint32_t newCwnd) { double currentTime Simulator::Now().GetSeconds(); algorithmStats[NewReno].AddDataPoint(currentTime, newCwnd); static double lastPrintTime 0; if (currentTime - lastPrintTime 2.0) { std::cout std::fixed std::setprecision(2) NewReno currentTime s: CWND newCwnd bytes ( std::setprecision(1) newCwnd/1024.0 KB) std::endl; lastPrintTime currentTime; } } static void CwndChangeVegas(uint32_t oldCwnd, uint32_t newCwnd) { double currentTime Simulator::Now().GetSeconds(); algorithmStats[Vegas].AddDataPoint(currentTime, newCwnd); static double lastPrintTime 0; if (currentTime - lastPrintTime 2.0) { std::cout std::fixed std::setprecision(2) Vegas currentTime s: CWND newCwnd bytes ( std::setprecision(1) newCwnd/1024.0 KB) std::endl; lastPrintTime currentTime; } } // 自定义应用类 class TcpCwndApp : public Application { public: TcpCwndApp(); virtual ~TcpCwndApp(); void Setup(PtrSocket socket, Address address, uint32_t packetSize, DataRate dataRate); private: virtual void StartApplication(void); virtual void StopApplication(void); void SendPacket(void); PtrSocket m_socket; Address m_peer; uint32_t m_packetSize; DataRate m_dataRate; EventId m_sendEvent; bool m_running; }; TcpCwndApp::TcpCwndApp() : m_socket(0), m_running(false) { } TcpCwndApp::~TcpCwndApp() { m_socket 0; } void TcpCwndApp::Setup(PtrSocket socket, Address address, uint32_t packetSize, DataRate dataRate) { m_socket socket; m_peer address; m_packetSize packetSize; m_dataRate dataRate; } void TcpCwndApp::StartApplication(void) { m_running true; m_socket-Bind(); m_socket-Connect(m_peer); SendPacket(); } void TcpCwndApp::StopApplication(void) { m_running false; if (m_sendEvent.IsRunning()) { Simulator::Cancel(m_sendEvent); } if (m_socket) { m_socket-Close(); } } void TcpCwndApp::SendPacket(void) { PtrPacket packet CreatePacket(m_packetSize); m_socket-Send(packet); if (m_running) { Time tNext(Seconds(m_packetSize * 8 / static_castdouble(m_dataRate.GetBitRate()))); m_sendEvent Simulator::Schedule(tNext, TcpCwndApp::SendPacket, this); } } // 运行单个算法的仿真 void RunAlgorithmSimulation(std::string algorithmName, std::string tcpType, Callbackvoid, uint32_t, uint32_t cwndCallback) { std::cout \n std::string(60, ) std::endl; std::cout Starting algorithmName Simulation std::endl; std::cout std::string(60, ) std::endl; // 初始化统计信息 algorithmStats[algorithmName] AlgorithmStats(algorithmName); // 设置TCP算法类型 Config::SetDefault(ns3::TcpL4Protocol::SocketType, StringValue(tcpType)); // 创建节点 NodeContainer nodes; nodes.Create(2); // 创建链路 PointToPointHelper pointToPoint; pointToPoint.SetDeviceAttribute(DataRate, StringValue(5Mbps)); pointToPoint.SetChannelAttribute(Delay, StringValue(2ms)); NetDeviceContainer devices; devices pointToPoint.Install(nodes); // 安装协议栈 InternetStackHelper stack; stack.Install(nodes); // 分配IP地址 Ipv4AddressHelper address; address.SetBase(10.1.1.0, 255.255.255.252); Ipv4InterfaceContainer interfaces address.Assign(devices); // 创建socket并连接回调 uint16_t sinkPort 8080; Address sinkAddress(InetSocketAddress(interfaces.GetAddress(1), sinkPort)); PtrSocket tcpSocket Socket::CreateSocket(nodes.Get(0), TcpSocketFactory::GetTypeId()); tcpSocket-TraceConnectWithoutContext(CongestionWindow, cwndCallback); // 创建自定义发送应用 PtrTcpCwndApp app CreateObjectTcpCwndApp(); app-Setup(tcpSocket, sinkAddress, 1024, DataRate(1Mbps)); nodes.Get(0)-AddApplication(app); app-SetStartTime(Seconds(1.0)); app-SetStopTime(Seconds(20.0)); // 创建接收端 PacketSinkHelper sink(ns3::TcpSocketFactory, InetSocketAddress(Ipv4Address::GetAny(), sinkPort)); ApplicationContainer sinkApps sink.Install(nodes.Get(1)); sinkApps.Start(Seconds(0.0)); sinkApps.Stop(Seconds(20.0)); // 运行仿真 Simulator::Stop(Seconds(20)); Simulator::Run(); Simulator::Destroy(); // 显示该算法的简要统计 AlgorithmStats stats algorithmStats[algorithmName]; std::cout \n algorithmName Simulation Complete: std::endl; std::cout std::string(40, -) std::endl; std::cout std::fixed std::setprecision(2); std::cout Data Points: std::setw(8) stats.dataPoints std::endl; std::cout Time Range: std::setw(8) stats.startTime - stats.endTime s std::endl; std::cout Duration: std::setw(8) (stats.endTime - stats.startTime) s std::endl; std::cout Max CWND: std::setw(8) stats.maxCwnd bytes ( std::setprecision(1) stats.maxCwnd/1024.0 KB) std::endl; std::cout Min CWND: std::setw(8) stats.minCwnd bytes ( std::setprecision(1) stats.minCwnd/1024.0 KB) std::endl; std::cout Average CWND: std::setw(8) std::setprecision(0) stats.avgCwnd bytes ( std::setprecision(1) stats.avgCwnd/1024.0 KB) std::endl; std::cout Final CWND: std::setw(8) std::setprecision(0) stats.finalCwnd bytes ( std::setprecision(1) stats.finalCwnd/1024.0 KB) std::endl; std::cout Growth Rate: std::setw(8) std::setprecision(0) stats.growthRate bytes/s std::endl; } // 打印详细的对比表格 void PrintComparisonTable() { std::cout \n std::string(80, ) std::endl; std::cout TCP CONGESTION CONTROL ALGORITHMS COMPARISON std::endl; std::cout std::string(80, ) std::endl; std::cout Network Conditions: 5Mbps bandwidth, 2ms delay std::endl; std::cout Application: 1Mbps data rate, 1024 byte packets std::endl; std::cout Simulation Time: 20 seconds per algorithm std::endl; std::cout std::string(80, ) std::endl; // 表头 std::cout std::left std::setw(12) Algorithm std::setw(12) Data Pts std::setw(12) Duration(s) std::setw(15) Max CWND(KB) std::setw(15) Avg CWND(KB) std::setw(15) Final CWND(KB) std::setw(15) Growth(b/s) std::endl; std::cout std::string(80, -) std::endl; // 数据行 for (const auto pair : algorithmStats) { const AlgorithmStats stats pair.second; std::cout std::left std::setw(12) stats.name std::right std::setw(10) stats.dataPoints std::setw(12) std::fixed std::setprecision(1) (stats.endTime - stats.startTime) std::setw(15) std::setprecision(1) stats.maxCwnd/1024.0 std::setw(15) std::setprecision(1) stats.avgCwnd/1024.0 std::setw(15) std::setprecision(1) stats.finalCwnd/1024.0 std::setw(15) std::setprecision(0) stats.growthRate std::endl; } std::cout std::string(80, ) std::endl; } // 打印性能排名 void PrintPerformanceRankings() { std::cout \n std::string(60, ) std::endl; std::cout PERFORMANCE RANKINGS std::endl; std::cout std::string(60, ) std::endl; // 按最大CWND排名 std::vectorstd::pairstd::string, double maxCwndRanking; for (const auto pair : algorithmStats) { maxCwndRanking.push_back({pair.first, pair.second.maxCwnd}); } std::sort(maxCwndRanking.begin(), maxCwndRanking.end(), [](const auto a, const auto b) { return a.second b.second; }); std::cout \n1. Maximum CWND (Aggressiveness): std::endl; std::cout Rank | Algorithm | Max CWND (KB) std::endl; std::cout ------------------------------ std::endl; for (size_t i 0; i maxCwndRanking.size(); i) { std::cout std::setw(4) i1 | std::setw(9) maxCwndRanking[i].first | std::fixed std::setprecision(1) std::setw(10) maxCwndRanking[i].second/1024.0 KB std::endl; } // 按平均CWND排名 std::vectorstd::pairstd::string, double avgCwndRanking; for (const auto pair : algorithmStats) { avgCwndRanking.push_back({pair.first, pair.second.avgCwnd}); } std::sort(avgCwndRanking.begin(), avgCwndRanking.end(), [](const auto a, const auto b) { return a.second b.second; }); std::cout \n2. Average CWND (Stable Throughput): std::endl; std::cout Rank | Algorithm | Avg CWND (KB) std::endl; std::cout ------------------------------ std::endl; for (size_t i 0; i avgCwndRanking.size(); i) { std::cout std::setw(4) i1 | std::setw(9) avgCwndRanking[i].first | std::fixed std::setprecision(1) std::setw(10) avgCwndRanking[i].second/1024.0 KB std::endl; } // 按增长率排名 std::vectorstd::pairstd::string, double growthRanking; for (const auto pair : algorithmStats) { growthRanking.push_back({pair.first, pair.second.growthRate}); } std::sort(growthRanking.begin(), growthRanking.end(), [](const auto a, const auto b) { return a.second b.second; }); std::cout \n3. Growth Rate (Responsiveness): std::endl; std::cout Rank | Algorithm | Growth Rate (bytes/s) std::endl; std::cout -------------------------------------- std::endl; for (size_t i 0; i growthRanking.size(); i) { std::cout std::setw(4) i1 | std::setw(9) growthRanking[i].first | std::fixed std::setprecision(0) std::setw(15) growthRanking[i].second bytes/s std::endl; } // 综合评分 std::cout \n4. Overall Performance Score: std::endl; std::cout (Score 0.4*MaxCWND 0.4*AvgCWND 0.2*GrowthRate) std::endl; std::cout Algorithm | Score | Normalized std::endl; std::cout ---------------------------- std::endl; std::vectorstd::pairstd::string, double overallScores; double maxScore 0; for (const auto pair : algorithmStats) { const AlgorithmStats stats pair.second; double score 0.4 * (stats.maxCwnd / 1024.0) 0.4 * (stats.avgCwnd / 1024.0) 0.2 * (stats.growthRate / 1000.0); overallScores.push_back({pair.first, score}); maxScore std::max(maxScore, score); } std::sort(overallScores.begin(), overallScores.end(), [](const auto a, const auto b) { return a.second b.second; }); for (const auto score : overallScores) { std::cout std::setw(9) score.first | std::fixed std::setprecision(2) std::setw(5) score.second | std::setprecision(1) std::setw(7) (score.second / maxScore * 100) % std::endl; } } int main(int argc, char *argv[]) { std::cout \n std::string(70, *) std::endl; std::cout TCP CONGESTION CONTROL ALGORITHM COMPARISON TOOL std::endl; std::cout std::string(70, *) std::endl; // 运行每种算法的仿真 - 使用Cubic、NewReno、Vegas std::cout \nRunning Cubic simulation... std::endl; RunAlgorithmSimulation(Cubic, ns3::TcpCubic, MakeCallback(CwndChangeCubic)); std::cout \nRunning NewReno simulation... std::endl; RunAlgorithmSimulation(NewReno, ns3::TcpNewReno, MakeCallback(CwndChangeNewReno)); std::cout \nRunning Vegas simulation... std::endl; RunAlgorithmSimulation(Vegas, ns3::TcpVegas, MakeCallback(CwndChangeVegas)); // 打印对比结果 PrintComparisonTable(); PrintPerformanceRankings(); // 最终总结 std::cout \n std::string(70, ) std::endl; std::cout FINAL SUMMARY std::endl; std::cout std::string(70, ) std::endl; // 找出最佳算法 std::string bestMaxCwnd, bestAvgCwnd, bestGrowth; double maxMaxCwnd 0, maxAvgCwnd 0, maxGrowth 0; for (const auto pair : algorithmStats) { const AlgorithmStats stats pair.second; if (stats.maxCwnd maxMaxCwnd) { maxMaxCwnd stats.maxCwnd; bestMaxCwnd stats.name; } if (stats.avgCwnd maxAvgCwnd) { maxAvgCwnd stats.avgCwnd; bestAvgCwnd stats.name; } if (stats.growthRate maxGrowth) { maxGrowth stats.growthRate; bestGrowth stats.name; } } std::cout \nBest Algorithm for Each Metric: std::endl; std::cout -------------------------------- std::endl; std::cout Maximum CWND (Aggressiveness): bestMaxCwnd ( std::fixed std::setprecision(1) maxMaxCwnd/1024.0 KB) std::endl; std::cout Average CWND (Stability): bestAvgCwnd ( std::fixed std::setprecision(1) maxAvgCwnd/1024.0 KB) std::endl; std::cout Growth Rate (Responsiveness): bestGrowth ( std::fixed std::setprecision(0) maxGrowth bytes/s) std::endl; // 算法特性分析 std::cout \nAlgorithm Characteristics: std::endl; std::cout --------------------------- std::endl; std::cout Cubic: Linux默认算法高带宽延迟积网络表现好 std::endl; std::cout NewReno: 标准TCP算法稳定性好 std::endl; std::cout Vegas: 基于延迟的算法避免拥塞但可能不公平 std::endl; std::cout \n std::string(70, *) std::endl; std::cout COMPARISON COMPLETE std::endl; std::cout std::string(70, *) std::endl; return 0; }上述代码是直接的可执行文件运行之后结果为