**2.1 Why cloud computing?**

Cloud computing offers many benefits that make it an attractive option for individuals and businesses alike. According to the research paper [5], there are some reasons why cloud computing is a popular choice:

**Scalability:** Cloud computing enables users to scale up or down their computing resources easily, depending on their needs. This means that users can adjust their computing resources as their requirements change, which can result in significant cost savings.

**Cost-effectiveness:** Cloud computing eliminates the need for organizations to invest in expensive hardware and software infrastructure. Instead, users pay for the computing resources they use on a pay-as-you-go basis, which can result in significant cost savings.

**Accessibility:** Cloud computing enables users to access their data and applications from anywhere with Internet connection. This means that users can work remotely, collaborate with others in real time, and access their data on any device.

**Reliability:** Cloud computing providers typically offer high levels of reliability and uptime, which ensures that users can access their data and applications when they need them.

*Perspective Chapter: A View – Cloud-Edge Computing Technology DOI: http://dx.doi.org/10.5772/intechopen.111454*

**Security:** Cloud computing providers typically employ robust security measures to protect their users' data and applications from cyber threats. These measures often exceed what most organizations can implement on their own.

**Agility:** Cloud computing enables organizations to quickly deploy new applications and services, which can result in faster time-to-market and increased competitive advantage.

Cloud computing offers numerous benefits that make it an attractive option for individuals and organizations. By enabling scalability, cost-effectiveness, accessibility, reliability, security, and agility, cloud computing has become an essential component of modern computing infrastructure.

#### **2.2 Types of cloud**

There are three main types of cloud computing: public cloud, private cloud, and hybrid cloud. Here is a brief overview of each type.

**Public Cloud:** A public cloud is a cloud computing environment that is hosted by a third-party cloud service provider and made available to the public. These providers offer computing resources such as servers, storage, and applications to multiple customers who share these resources. Public clouds are accessible to anyone with Internet connection and are typically priced on a pay-as-you-go basis.

**Private Cloud:** A private cloud is a cloud computing environment that is dedicated to a single organization. These clouds are hosted either on-premises or by a third-party service provider and are typically used by large organizations that require a high degree of control over their computing resources. Private clouds offer greater control and security than public clouds but require significant upfront investment.

**Hybrid Cloud:** A hybrid cloud is a combination of public and private clouds that work together to provide a single computing environment. This approach allows organizations to leverage the benefits of both public and private clouds while addressing their specific needs for security, control, and scalability. In a hybrid cloud, organizations can use a public cloud for non-sensitive data and applications while using a private cloud for sensitive data and applications.

In addition to these three main types, there are also other types of clouds, such as community clouds and multi-clouds. Community clouds are clouds that are shared by multiple organizations with similar requirements, such as government agencies or healthcare organizations. Multi-clouds are environments that incorporate multiple cloud providers, enabling organizations to leverage the strengths of different providers for different applications or workloads.

*Benefits of cloud computing:*


#### *Drawbacks of cloud computing:*


#### **2.3 Edge computing**

Edge computing is a distributed computing paradigm that involves processing and analyzing data at or near the source of the data, rather than sending it to a centralized data center or cloud for processing. The term "edge" refers to the edge of a network, where data is generated, collected, and analyzed in real-time.

The main goal of edge computing is to reduce the latency and bandwidth requirements associated with transmitting data to a centralized location for processing. By processing data at the edge of the network, organizations can improve their response times, reduce network congestion, and improve the overall performance of their applications.

Edge computing involves deploying edge devices, such as sensors, gateways, and other types of computing devices, at the edge of the network. These devices are responsible for collecting data, processing it in real time, and sending only the relevant data to the cloud for further analysis or storage.

Edge computing is particularly useful in scenarios where low latency and high reliability are critical, such as in industrial automation, autonomous vehicles, and healthcare. It also enables organizations to process and analyze data in environments where network connectivity is limited or unreliable, such as in remote locations or on mobile devices. It is an important development in the field of distributed computing, as it enables organizations to improve the performance, reliability, and scalability of their applications by processing data at the edge of the network.

#### **2.4 Working of edge computing**

EDGE computing involves processing and analyzing data at or near the source of the data, rather than sending it to a centralized data center or cloud for processing. The following are the basic steps involved in the working of edge computing.

**Data collection:** Edge devices, such as sensors, gateways, and other types of computing devices, are deployed at the edge of the network to collect data from various sources. These devices may also preprocess the data to reduce the amount of data that needs to be transmitted to the cloud for further analysis or storage.

**Data processing:** Edge devices process the collected data in real time using local computing resources. This enables organizations to perform analysis and make decisions quickly, without the delays associated with transmitting data to a centralized location for processing.

**Data storage:** Edge devices may also store data locally for quick access and offline processing. This helps to reduce the latency associated with transmitting data to the cloud for storage.

**Data transmission:** Edge devices transmit only relevant data to the cloud for further analysis or storage. This reduces the amount of data that needs to be transmitted over the network, reducing network congestion and improving overall performance.

**Cloud-based analysis:** The cloud receives the relevant data from edge devices and performs further analysis or storage. Cloud-based analytics can provide insights into patterns, trends, and anomalies that can help organizations make better decisions and optimize their operations.

IT enables organizations to process data at the edge of the network, reducing the latency and bandwidth requirements associated with transmitting data to a centralized location for processing. By processing data at the edge, organizations can improve their response times, reduce network congestion, and improve the overall performance of their applications.

#### **2.5 Types of edge computing**

There are four main types of edge computing, each with their own specific characteristics and use cases. These are as follows:

**Local edge:** Local edge computing is the simplest form of edge computing, and involves processing data at the edge of the network on a device or gateway that is directly connected to the data source. Local edge computing is used to minimize latency and ensure high availability for applications that require real-time processing.

**Regional edge:** Regional edge computing involves processing data at a regional data center or a group of data centers that are geographically close to the edge devices. Regional edge computing is used to support applications that require higher computational power or storage capacity than what is available on the local edge.

**Distributed edge:** Distributed edge computing involves processing data at multiple edge locations simultaneously. This approach is used to ensure high availability and redundancy for applications that require real-time processing and are critical to business operations.

**Cloud edge:** Cloud edge computing involves processing data at the edge of the network using a combination of cloud and edge resources. This approach is used to support applications that require scalability, high availability, and high computational power. Cloud edge computing can also enable edge devices to offload data processing and analysis to the cloud when needed.

#### *Benefits of edge computing:*

Edge computing offers several benefits to organizations, including the following:

**Reduced latency:** Edge computing reduces the time it takes for data to travel from the source to the processing location, resulting in lower latency and faster processing times. This is especially important for applications that require real-time processing and response times.

**Improved reliability:** Edge computing can improve the reliability of applications by reducing the impact of network outages or disruptions. By processing data at the edge of the network, applications can continue to function even if the network connection is lost.

Lower bandwidth requirements: Edge computing reduces the amount of data that needs to be transmitted to a centralized location for processing, resulting in lower bandwidth requirements and reduced network congestion.

**Enhanced security:** Edge computing can enhance security by processing sensitive data locally, reducing the risk of data breaches and unauthorized access. Additionally, edge devices can be configured to encrypt data at rest and in transit, further enhancing security.

Improved scalability: Edge computing [6] can improve the scalability of applications by distributing processing and storage resources across multiple edge devices. This enables organizations to handle increasing volumes of data and user requests without impacting performance.

**Cost savings:** Edge computing can result in cost savings by reducing the need for expensive data center infrastructure and network bandwidth. Additionally, edge computing can reduce the cost of data transfer and storage by processing and storing data locally.

#### *Drawbacks of edge computing:*

Edge computing offers several benefits, and it also has some drawbacks that organizations should be aware of. These include the following:

**Limited processing power:** Edge devices typically have limited processing power compared to cloud servers or data centers. This means that complex applications or processing tasks may not be able to be handled at the edge, and may need to be offloaded to the cloud.

**Limited storage capacity:** Edge devices also typically have limited storage capacity, which can be a constraint for applications that require large amounts of storage.

**Increased complexity:** Edge computing can increase the complexity of IT infrastructure, as it involves managing and coordinating a large number of edge devices and data sources. This can be challenging for organizations that do not have the necessary expertise or resources.

**Security risks:** Edge devices are often deployed in remote or unsecured locations, making them more vulnerable to cyber-attacks. Additionally, managing security across many edge devices can be challenging, and can require additional resources and expertise.

**Integration challenges:** Integrating edge computing into existing IT infrastructure can be challenging, especially if legacy systems are involved. This can result in additional costs and complexity.

**Maintenance and upgrades:** Edge devices require regular maintenance and upgrades to ensure that they are functioning properly and are up to date with security patches and software updates. This can be challenging in remote or hard-to-reach locations.

#### **3. Examples and use cases**

Edge computing is being used in a variety of industries and use cases. Some examples include the following:

**Manufacturing:** In manufacturing, edge computing is used to optimize production processes, monitor equipment performance, and reduce downtime. For example, edge devices can be used to collect sensor data from production lines in real time, analyze the data at the edge, and provide insights into operators and engineers to improve production efficiency.

*Perspective Chapter: A View – Cloud-Edge Computing Technology DOI: http://dx.doi.org/10.5772/intechopen.111454*

**Healthcare:** In healthcare, edge computing is used to monitor patients in real time, analyze data from medical devices, and provide alerts to healthcare providers when necessary. For example, edge devices can be used to monitor vital signs of patients in real time and provide early warning alerts when a patient's condition is deteriorating.

**Smart cities:** In smart cities, edge computing is used to manage traffic, monitor air quality, and provide real-time public safety alerts. For example, in Ref. [7] edge devices can be used to collect data from traffic cameras, analyze the data at the edge, and provide real-time traffic updates to commuters.

**Retail:** In retail, edge computing is used to personalize customer experiences, optimize inventory management, and improve supply chain efficiency. For example, edge devices can be used to collect data from in-store sensors, analyze the data at the edge, and provide personalized recommendations to customers based on their shopping behavior.

**Oil and gas:** In the oil and gas industry, edge computing is used to monitor and optimize production processes, reduce downtime, and improve worker safety. For example, edge devices can be used to collect data from oil rigs in real time, analyze the data at the edge, and provide insights into operators and engineers to improve production efficiency and reduce downtime.

#### **4. Cloud-Edge technology**

Cloud-edge technology is the integration of cloud computing resources with edge computing devices. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. Cloud computing, on the other hand, is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale.

By combining the two technologies, organizations can take advantage of the scalability and flexibility of the cloud while also [2] enjoying the low latency and high performance of edge computing. This allows for real-time data processing and decision making, as well as the ability to offload data and processing to the cloud for storage, management, and advanced processing.

One of the main use cases of cloud-edge technology is in the Internet of Things (IoT) and Industry 4.0. IoT devices generate [8] large amounts of data that need to be processed and analyzed in real time. By using edge computing, this data can be processed locally, reducing the amount of data that needs to be sent to the cloud for processing. This can also reduce the cost of transmitting large amounts of data over long distances. In addition, edge devices can also make decisions based on the data they collect, without the need for a constant connection to the cloud.

Another use case of cloud-edge technology is in the field of autonomous vehicles. Autonomous vehicles generate huge amount of data from sensors that need to be processed in real time to make decisions. By using edge computing, the data can be processed locally, reducing the amount of data that needs to be sent to the cloud for processing. This can also reduce the cost of transmitting large amounts of data over long distances. Cloud-edge technology also plays a vital role in 5G networks, which helps to reduce the latency and increase the data rate for the end-users.

In conclusion, cloud-edge technology is an important trend in computing that allows organizations to take advantage of the scalability and flexibility of the cloud while also enjoying the low latency and high performance of edge computing. It has many use cases, including IoT, Industry 4.0, autonomous vehicles, and 5G networks.
