Python 代码实现高性能异构分布式并行集群管理系统
设备管理模块
用于连接、管理集群中的异构节点，处理各节点的注册、健康检查等。
import threading
class Node:
def __init__(self, name, is_active=True):
self.name = name
self.is_active = is_active
def connect(self):
print(f"Node {self.name} connected.")
def check_health(self):
return self.is_active
class ClusterManager:
def __init__(self):
self.nodes = []
self.lock = threading.Lock()
def add_node(self, node):
with self.lock:
self.nodes.append(node)
def connect_all_nodes(self):
threads = []
for node in self.nodes:
t = threading.Thread(target=node.connect)
threads.append(t)
t.start()
for t in threads:
t.join()
def check_all_nodes(self):
return all([node.check_health() for node in self.nodes])
# Example usage
manager = ClusterManager()
node1 = Node('Node1')
node2 = Node('Node2')
manager.add_node(node1)
manager.add_node(node2)
manager.connect_all_nodes()
print("All nodes healthy:", manager.check_all_nodes())
任务调度模块
负责将并行任务调度到适当的节点上，考虑异构计算资源的特性。
import random
class Task:
def __init__(self, task_id, complexity):
self.task_id = task_id
self.complexity = complexity
class TaskScheduler:
def __init__(self, nodes):
self.nodes = nodes
def schedule_task(self, task):
# Simple scheduling: assign task to a random active node
available_nodes = [node for node in self.nodes if node.is_active]
assigned_node = random.choice(available_nodes)
print(f"Task {task.task_id} scheduled to {assigned_node.name}")
# Example usage
scheduler = TaskScheduler(manager.nodes)
task = Task(1, "high")
scheduler.schedule_task(task)
通信模块
负责节点间的通信，支持消息传递和数据同步。
import threading
class Communication:
def send_message(self, node, message):
print(f"Sending message to {node.name}: {message}")
def broadcast_message(self, nodes, message):
threads = []
for node in nodes:
t = threading.Thread(target=self.send_message, args=(node, message))
threads.append(t)
t.start()
for t in threads:
t.join()
# Example usage
comm = Communication()
comm.broadcast_message(manager.nodes, "Start Task")
数据处理模块
负责在分布式环境中处理数据任务。
class DataProcessor:
def process_data(self, data_chunk):
print(f"Processing data: {data_chunk}")
def distribute_data(self, nodes, data):
# Splitting data into chunks and distributing
chunk_size = len(data) // len(nodes)
for i, node in enumerate(nodes):
data_chunk = data[i*chunk_size:(i+1)*chunk_size]
self.process_data(data_chunk)
# Example usage
data = [i for i in range(100)]
processor = DataProcessor()
processor.distribute_data(manager.nodes, data)
监控和负载均衡模块
实时监控集群节点的状态，动态调整任务分配，保证资源利用率最大化。
class LoadBalancer:
def __init__(self, nodes):
self.nodes = nodes
def get_least_loaded_node(self):
# Dummy implementation: return first active node
active_nodes = [node for node in self.nodes if node.is_active]
return active_nodes[0] if active_nodes else None
class Monitoring:
def monitor_nodes(self, nodes):
for node in nodes:
status = "active" if node.check_health() else "inactive"
print(f"Node {node.name} status: {status}")
# Example usage
monitoring = Monitoring()
load_balancer = LoadBalancer(manager.nodes)
monitoring.monitor_nodes(manager.nodes)
node = load_balancer.get_least_loaded_node()
print(f"Assigning task to least loaded node: {node.name}")
存储模块
负责处理分布式系统中的数据存储，支持分布式文件系统或数据库。
import os
class DistributedStorage:
def __init__(self, storage_dir):
self.storage_dir = storage_dir
if not os.path.exists(storage_dir):
os.makedirs(storage_dir)
def store_data(self, node, data):
file_path = os.path.join(self.storage_dir, f"{node.name}_data.txt")
with open(file_path, 'w') as f:
f.write(data)
print(f"Data stored in {file_path}")
def retrieve_data(self, node):
file_path = os.path.join(self.storage_dir, f"{node.name}_data.txt")
if os.path.exists(file_path):
with open(file_path, 'r') as f:
data = f.read()
print(f"Data retrieved from {file_path}: {data}")
else:
print(f"No data found for {node.name}")
# Example usage
storage = DistributedStorage('distributed_storage')
storage.store_data(node1, "Node1 data")
storage.retrieve_data(node1)
代码总结
这些模块分别处理集群管理中的不同功能：设备管理、任务调度、通信、数据处理、监控和负载均衡，以及数据存储。通过异步和多线程技术，系统能够在异构分布式集群中实现高效的任务调度和管理。
C++ 代码实现高性能异构分布式并行集群管理系统
设备管理模块
用于连接、管理集群中的异构节点，处理各节点的注册、健康检查等。
#include <iostream>
#include <vector>
#include <thread>
#include <mutex>
class Node {
public:
std::string name;
bool is_active;
Node(const std::string &name) : name(name), is_active(true) {}
void connect() {
std::cout << "Node " << name << " connected." << std::endl;
}
bool check_health() const {
return is_active;
}
};
class ClusterManager {
private:
std::vector<Node> nodes;
std::mutex lock;
public:
void add_node(const Node &node) {
std::lock_guard<std::mutex> guard(lock);
nodes.push_back(node);
}
void connect_all_nodes() {
std::vector<std::thread> threads;
for (auto &node : nodes) {
threads.emplace_back(&Node::connect, &node);
}
for (auto &t : threads) {
t.join();
}
}
bool check_all_nodes() const {
for (const auto &node : nodes) {
if (!node.check_health()) {
return false;
}
}
return true;
}
const std::vector<Node>& get_nodes() const {
return nodes;
}
};
// Example usage
int main() {
ClusterManager manager;
Node node1("Node1");
Node node2("Node2");
manager.add_node(node1);
manager.add_node(node2);
manager.connect_all_nodes();
std::cout << "All nodes healthy: " << (manager.check_all_nodes() ? "Yes" : "No") << std::endl;
return 0;
}
任务调度模块
负责将并行任务调度到适当的节点上，考虑异构计算资源的特性。
#include <iostream>
#include <vector>
#include <random>
class Task {
public:
int task_id;
std::string complexity;
Task(int id, const std::string &complexity) : task_id(id), complexity(complexity) {}
};
class TaskScheduler {
private:
std::vector<Node> nodes;
public:
explicit TaskScheduler(const std::vector<Node> &nodes) : nodes(nodes) {}
void schedule_task(const Task &task) {
// Simple scheduling: assign task to a random active node
std::vector<Node> active_nodes;
for (const auto &node : nodes) {
if (node.is_active) {
active_nodes.push_back(node);
}
}
if (!active_nodes.empty()) {
int idx = rand() % active_nodes.size();
std::cout << "Task " << task.task_id << " scheduled to " << active_nodes[idx].name << std::endl;
} else {
std::cout << "No active nodes available." << std::endl;
}
}
};
// Example usage
int main() {
ClusterManager manager;
Node node1("Node1");
Node node2("Node2");
manager.add_node(node1);
manager.add_node(node2);
manager.connect_all_nodes();
Task task1(1, "high");
TaskScheduler scheduler(manager.get_nodes());
scheduler.schedule_task(task1);
return 0;
}
通信模块
负责节点间的通信，支持消息传递和数据同步。
#include <iostream>
#include <vector>
#include <thread>
class Communication {
public:
void send_message(const Node &node, const std::string &message) {
std::cout << "Sending message to " << node.name << ": " << message << std::endl;
}
void broadcast_message(const std::vector<Node> &nodes, const std::string &message) {
std::vector<std::thread> threads;
for (const auto &node : nodes) {
threads.emplace_back(&Communication::send_message, this, node, message);
}
for (auto &t : threads) {
t.join();
}
}
};
// Example usage
int main() {
ClusterManager manager;
Node node1("Node1");
Node node2("Node2");
manager.add_node(node1);
manager.add_node(node2);
manager.connect_all_nodes();
Communication comm;
comm.broadcast_message(manager.get_nodes(), "Start Task");
return 0;
}
数据处理模块
负责在分布式环境中处理数据任务。
#include <iostream>
#include <vector>
class DataProcessor {
public:
void process_data(const std::string &data_chunk) {
std::cout << "Processing data: " << data_chunk << std::endl;
}
void distribute_data(const std::vector<Node> &nodes, const std::vector<std::string> &data) {
size_t chunk_size = data.size() / nodes.size();
for (size_t i = 0; i < nodes.size(); ++i) {
std::string data_chunk = data[i * chunk_size];
process_data(data_chunk);
}
}
};
// Example usage
int main() {
ClusterManager manager;
Node node1("Node1");
Node node2("Node2");
manager.add_node(node1);
manager.add_node(node2);
manager.connect_all_nodes();
std::vector<std::string> data = {"chunk1", "chunk2"};
DataProcessor processor;
processor.distribute_data(manager.get_nodes(), data);
return 0;
}
监控和负载均衡模块
实时监控集群节点的状态，动态调整任务分配，保证资源利用率最大化。
#include <iostream>
#include <vector>
class LoadBalancer {
private:
std::vector<Node> nodes;
public:
explicit LoadBalancer(const std::vector<Node> &nodes) : nodes(nodes) {}
Node get_least_loaded_node() {
// Dummy implementation: return first active node
for (const auto &node : nodes) {
if (node.is_active) {
return node;
}
}
return Node("No available nodes");
}
};
class Monitoring {
public:
void monitor_nodes(const std::vector<Node> &nodes) {
for (const auto &node : nodes) {
std::string status = node.check_health() ? "active" : "inactive";
std::cout << "Node " << node.name << " status: " << status << std::endl;
}
}
};
// Example usage
int main() {
ClusterManager manager;
Node node1("Node1");
Node node2("Node2");
manager.add_node(node1);
manager.add_node(node2);
manager.connect_all_nodes();
Monitoring monitoring;
LoadBalancer load_balancer(manager.get_nodes());
monitoring.monitor_nodes(manager.get_nodes());
Node least_loaded_node = load_balancer.get_least_loaded_node();
std::cout << "Assigning task to least loaded node: " << least_loaded_node.name << std::endl;
return 0;
}
存储模块
负责处理分布式系统中的数据存储，支持分布式文件系统或数据库。
#include <iostream>
#include <fstream>
#include <string>
class DistributedStorage {
private:
std::string storage_dir;
public:
explicit DistributedStorage(const std::string &dir) : storage_dir(dir) {}
void store_data(const Node &node, const std::string &data) {
std::ofstream file(storage_dir + "/" + node.name + "_data.txt");
if (file.is_open()) {
file << data;
file.close();
std::cout << "Data stored for " << node.name << std::endl;
}
}
void retrieve_data(const Node &node) {
std::ifstream file(storage_dir + "/" + node.name + "_data.txt");
if (file.is_open()) {
std::string data;
file >> data;
std::cout << "Data retrieved from " << node.name << ": " << data << std::endl;
file.close();
} else {
std::cout << "No data found for " << node.name << std::endl;
}
}
};
// Example usage
int main() {
ClusterManager manager;
Node node1("Node1");
Node node2("Node2");
manager.add_node(node1);
manager.add_node(node2);
manager.connect_all_nodes();
DistributedStorage storage("distributed_storage");
storage.store_data(node1, "Node1 data");
storage.retrieve_data(node1);
return 0;
}
总结
这些C++模块分别实现了集群设备管理、任务调度、通信、数据处理、监控和负载均衡以及数据存储功能。这些模块协同工作，使得异构分布式并行集群管理系统具备高效的管理与处理能力。
