Author: Zen and the Art of Computer Programming
1 Introduction
Cloud Computing is a new service model that brings high efficiency, flexibility and low cost. It provides data center infrastructure, application software and services to users through the Internet. Cloud computing has the following characteristics:
1. Access on demand: Cloud computing allows users to access their data and computing resources from anywhere they need at any time, which can meet a variety of business needs.
2. Resource elastic scaling: The cloud computing platform can automatically adjust the size of computing resources according to business needs and pay as needed. This means users don’t have to pay a large initial fee upfront, but only pay for the resources they actually use.
3. High reliability: Cloud computing platform operators ensure the availability of cloud computing services and prevent data loss and service interruptions caused by force majeure or system failures.
4. Flexibility: The cloud computing platform allows users to deploy their own applications in a variety of ways, allowing users to use the same services in different scenarios. For example, virtual machines deployed using cloud platforms can run almost anywhere; databases built using cloud platforms can be deployed in multiple regions; using cloud storage services provided by cloud platforms can quickly store and migrate massive data.
5. Low cost: Cloud computing platforms increase service prices and reduce costs by reducing hardware costs, reducing IT investment, and reducing service provider expenses.
There are many types of cloud computing platforms, including public clouds, private clouds, community clouds, hosting services, etc. The most famous of them are public clouds, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Compared with other types of computing platforms, cloud computing platforms have more advantages, such as on-demand access, flexible deployment of applications, and cost reduction. Therefore, the development of cloud computing platforms is inseparable from large-scale user participation and continuous innovation by participants.
2. Core concepts and terminology
2.1 Cloud computing model
Cloud computing models can be divided into three types: IaaS, PaaS, and SaaS.
1.IaaS (Infrastructure as a Service): Infrastructure as a service, which provides leasing services for underlying resources such as computing, network, and storage. Developers can build cloud-based application systems without paying attention to the maintenance and management of underlying servers.
2. PaaS (Platform as a Service): Platform as a service provides developers with a complete platform. They do not need to purchase or maintain servers themselves. They only need to call the API according to the interface provided by the platform to implement functions. For example, developers can choose the provided message queue service for real-time information delivery without having to worry about the installation and configuration of middleware and clusters.
3. SaaS (Software as a Service): Software as a service, providing commercial software services, such as office suites, CRM software, etc. Users only need to log in to the website to register an account and enter a password to use the software.
2.2 Cloud computing services
Cloud computing services refer to various computing, storage, network, security, analysis, big data processing and other services provided by cloud computing platforms. These services can be subscribed on demand and support a variety of programming languages, operating systems and tools. For example, optional services include:
1. Computing services: including virtual machines, container services, function computing, etc., helping customers quickly deploy, expand, and manage applications.
2. Network services: including load balancers, CDN services, streaming media services, etc., providing safe and reliable network connection capabilities.
3. Storage services: including object storage, block storage, file storage, etc., providing customers with reliable and efficient storage capabilities.
4. Security services: including encrypted transmission, identity authentication, authorization, compliance management, etc., providing secure infrastructure.
5. Analysis services: including data warehouse, data lake, data analysis services, etc., providing massive, high-speed, real-time big data processing capabilities.
6. Big data processing services: including cluster planning, query optimization, data import and export, ETL tools, etc., to help customers quickly process large amounts of data.
3. Core algorithm principles and operating steps
The main problem that cloud computing solves is how to effectively utilize Internet resources and use computing, storage, network, security, analysis, big data processing and other services to build a cloud computing platform that can provide high availability. One of the most important tasks of a cloud computing platform is to provide computing resources to users. In order to achieve this goal, the cloud computing platform will use server clusters, which are physically distributed in different locations and connected through the network. Each server is called a node, and nodes communicate through the network to form a resource pool of the cloud computing platform. Users can submit tasks that need to be calculated to the platform by submitting task requests. The platform allocates node resources to execute the tasks and finally returns the results.
As shown in the figure below, when a user submits a job request, the platform must decide which nodes to perform the task. This process needs to consider many factors, such as node resource utilization, request queue length, job priority, execution time, etc. . Finally, the platform will schedule the task to the appropriate node for execution, and then return the results to the user after completing the task.
3.1 Distributed computing
Distributed computing is a parallel computing method that uses multiple computers to work together, assign tasks to different computers, and then summarize the results. The key to distributed computing is how to decompose tasks into different nodes while making it easier for users to control each node. Currently, distributed computing frameworks mainly include Apache Hadoop, Spark, etc.
3.2 Distributed storage
Distributed storage refers to distributing data to different data centers to achieve data storage and processing. Distributed storage is usually divided into distributed file systems, distributed databases, distributed message queues, etc.
3.3 Distributed message queue
Distributed message queue (Distributed message queue) is a distributed system based on message passing mechanism, used to achieve asynchronous communication across multiple processes and computers.
3.4 Distributed multi-Master protocol
Distributed multi-master protocol is a distributed system based on distributed coordination and synchronization protocol, which is used to solve multi-master control problems. In a distributed system, multiple nodes may think they are the master node at the same time, but in fact only one node has real control. The design goal of the multi-Master protocol is to improve system performance while maintaining availability, consistency, and fault tolerance. Currently, research on distributed multi-Master protocols is relatively popular, and related research includes Paxos, Zookeeper, Raft, etc.
3.5 Load balancing
Load balancing is a technology that dynamically redistributes workloads in order to balance the load on each server. Load balancing can be divided into soft load balancing, hard load balancing, etc.
3.6 Data Center Network
Data center network refers to the connection method within the data center and between data centers. Data center networks usually consist of dedicated switches and routing equipment, and can adopt two architectures - star structure and ring structure.
4.Code examples
4.1 Python code examples
import boto3
def create_ec2():
client = boto3.client('ec2')
# Create an EC2 instance
response = client.run_instances(
ImageId='ami-0ff8a91507f77f867',
MinCount=1,
MaxCount=1,
InstanceType='t2.micro'
)
return response['Instances'][0]['InstanceId']
if __name__ == '__main__':
ec2_id = create_ec2()
print("EC2 created with ID:", ec2_id)4.2 Java code examples
package com.example;
import software.amazon.awssdk.services.ec2.Ec2Client;
import software.amazon.awssdk.services.ec2.model.*;
public class App {
public static void main(String[] args) throws Exception {
Ec2Client ec2 = Ec2Client.builder().build();
RunInstancesRequest request = RunInstancesRequest.builder()
.imageId("ami-0ff8a91507f77f867") // Amazon Linux AMI
.minCount(1)
.maxCount(1)
.instanceType(InstanceType.T2_MICRO)
.build();
RunInstancesResponse response = ec2.runInstances(request);
System.out.println("Instance created with ID: " + response.reservation().instances().get(0).instanceId());
}
}4.3 C++ code examples
#include <aws/core/Aws.h>
#include <aws/ec2/EC2Client.h>
#include <iostream>
int main(int argc, char** argv)
{
Aws::SDKOptions options;
Aws::InitAPI(options);
const std::string region = "us-east-1";
auto ec2 = Aws::MakeShared<Aws::EC2::EC2Client>(
"", Aws::Region::Create(region));
RunInstancesRequest run_request;
run_request.SetImageId("ami-0ff8a91507f77f867");
run_request.SetMinCount(1);
run_request.SetMaxCount(1);
run_request.SetInstanceType("t2.micro");
auto outcome = ec2->RunInstances(run_request);
if (!outcome.IsSuccess())
{
std::cerr << "Error running instances: "
<< outcome.GetError().GetMessage() << "\n";
return 1;
}
const auto& reservation = outcome.GetResult().GetReservation();
const auto& instance = reservation.GetInstances()[0];
std::cout << "Instance created with ID: "
<< instance.GetInstanceId() << "\n";
Aws::ShutdownAPI(options);
return 0;
}5. Future development trends
Although cloud computing has gradually become the standard for enterprise applications, due to the lack of unified standards, each manufacturer itself often provides different solutions and directions for the development of cloud computing. With the development of cloud computing, more and more manufacturers have joined the cloud computing camp, and it is precisely because of this diversification that cloud computing has become broader. In addition to the technical field, cloud computing also involves multiple impacts in legal, economic, policy and other aspects. With the popularity of cloud computing, you may face a series of issues such as data privacy protection, computing cost optimization, regulatory compliance, etc. All these changes require the joint efforts and advancement of cloud computing platform vendors.