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Introduction to Cloud and Edge Computing

Cloud computing plays a significant role in making the best possible choices for IoT devices. A cloud-based framework helps developers create, deploy and manage their applications easily, such as acting as an application data platform, developing an application to scale, supporting millions of user interactions, and more. It can store large quantities of information and conduct analytics, generating powerful visualizations.

the processing as an appropriated worldview. It brings information about data and registers power nearer to the gadget or information source where it's generally required. Click to explore about, Difference Between Cloud, Edge, and Fog Computing

Then there is edge computing, meaning that outside of a centralized data center, and perform software, utilities, and computational data analysis closer to end-user. The Internet of Things is associated closely with Edge computing. It is a step back from the trendy computing cloud paradigm, where all the exciting bits occur in data centers. Rather than using local resources to collect and send data to the cloud, decisions are taken place on local servers.

What are the advantages of Cloud Computing?

The advantages of Cloud Computing in modern era are listed below:

Employee Collaboration & Productivity

Using cloud computing, employees can collaborate and communicate with each other in real-time irrespective of the user's location. As data is centralized, it will be productive no matter where use is located.
Easily Upgraded: Users working on the cloud can upgrade their versions easily and keep track of them.

Unlimited Storage Capacity

Cloud computing allows users to store, access, and retrieve data through easy and scalable remote storage systems. Without acquiring and providing local storage devices, networks can instantly scale up (or down) for storage capacity. Rather than paying a prescribed amount, most plans allow you to pay for just the storage capacity they need.

Reduced Server Hardware Costs

Reduces the cost of maintaining and managing hardware as users are using remote servers and paying for just using that is much less than hardware and its maintenance cost.

Fast/Central Provisioning of Services

Setting up an IT infrastructure in-house can take weeks or even months. On the other hand, Cloud systems can be set up in a day, if not a few hours. Cloud service providers have pre-configured systems that may be swiftly customized to the company's needs. With short provisioning time, cloud service providers can deliver mission-critical technologies.

Mobility and Flexibility

Employees can access company data from their cellphones and other mobile devices using cloud computing. So, staff who are away from the office for extended periods due to travel or long commutes can utilize the mobile feature to stay in touch with clients and coworkers. Flexibility with cloud computing allows the IT department to have more time to focus on other business goals, such as customer happiness. The team won't have to worry about managing the infrastructure or spending time on upgrades and patches.

Edge Computing allows data generated by IoT to be processed near its source rather than sending the data to a great distance to data centers or cloud. Click to explore about, Artificial Intelligence in Edge Computing

What are the advantages of Edge Computing?

The advantages of Edge Computing are described below:

Reduced Latency

As the analysis and processing happen on the device or edge servers hence it significantly reduces the latency as a result giving fast response time.

LAN Speed Bandwidth

The resilience of the network improves when edge devices store and process data locally. Microdata centers are prefabricated and can be operated in a variety of environments. Hence it continues to function normally when it loses cloud connection for a short time due to intermittent connectivity.

Furthermore, each network has a limit on the amount of data it can send at any one time. While you can increase your bandwidth as needed, business expansion frequently means pushing your broadband infrastructure to its limitations.

Reduced WAN Costs

Edge computing categorizes data to store locally or sent to the cloud. Thus it reduces the need and cost for WAN bandwidth. It doesn’t eliminate the need for the cloud, instead optimizes the flow of data that reduces the operating costs.

Local computing for real-time data

Immediate processing and analysis closer to the edge of applications provide near-time or real-time analysis. For instance, in the case of autonomous vehicles, immediate analytics and processing are critical for safety and efficiency.

Enhanced Compliance

Breach of data is costly, especially in the healthcare and finance industries. Edge computing simplifies compliance by allowing data to be stored locally rather than in the cloud. When sensitive data travels, compliance responsibilities increase exponentially, especially due to regulations like GDPR.

Security and Privacy

In cloud computing, as it is not always required to send sensitive data to the cloud for processing, hence the overall security of your network improves. Business and operational procedures become even more vulnerable if all of your data is stored in the cloud. Because there is less data that can be intercepted, it minimizes the risk of man-in-the-middle security breaches.

A distributed computing paradigm in which processing and computation are performed mainly on classified device nodes Click to explore about, The Impact of Edge Computing on IoT

What are the challenges of Edge Computing and Cloud Computing?

The below are the highlighted Risks and Benefits of Edge Computing and Cloud Computing:

Challenges of Edge Computing

  • Control & Reliability: As edge computing is a decentralized system, some are less reliable and need the user's proper attention.
  • Security & Compliance: In this, data processing is done at the outside edge of the network, so there might be identity theft chances.
  • Compatibility: Some IoT devices have generated a large amount of data every second, challenging to handle on edge.
  • Contracts & Lock-In: We need to take or sign some essential contracts and Lock-inns for doing this.

Challenges of Cloud Computing

  • High Risk: As data is stored centralized when information is transferred from edge to cloud, chances of attacks are more.
  • Potential Loss: Due to the increase in cloud infrastructure data, the chances of threats also increases.
  • Longer Outage Time: As data is transferred from edge to cloud, it takes a longer time than edge computing.
  •  Look at security: Using a cloud computing system, we cannot trust security, and companies need to compromise data confidentiality.
Edge Computing helps to provide server resources, data analysis, and artificial intelligence to data collection sources and cyber-physical sources. Click to explore about, Edge Computing and its Impact on IoT

Cloud Computing vs Edge Computing

Cloud Computing

Edge Computing

Cloud computing is centralized servers stored in faraway, large-scale data centers.

On the other hand, edge computing is highly distributed and global computing infrastructure that is closer to the devices and users.

It processes data on central cloud server that is often located far away from the data sources

Process data on site quickly and analyzes data in real-time. It does not focus on storing data.

It is suited for applications that are not time-sensitive.

It is ideal for low latency where every millisecond counts

Cloud computing gives improved and innovative processing capabilities and storing capacity.

As it process in the device so it has lower processing power and storage capacity

It is good for in-depth and long-term analysis.

It is better for fast and real-time analysis.

It needs internet connectivity.

It can work without internet connectivity.

An expensive and intensive operational activity for the company.

Automated scalability with zero-touch provisioning

The cost of connectivity, data movement, bandwidth, and latency is quite high.

Fewer latency and bandwidth requirements increased performance, and lower operational expenses.

Use Cases of Edge Computing and Cloud Computing

The below mentioned are the Use Cases of Edge Computing and Cloud Computing

Edge Computing

  • Autonomous Vehicles: Self-driving cars can gather vast volumes of information and make real-time choices on or near the road for passengers' and others' safety. In-vehicle response times, latency problems could trigger millisecond delays — a scenario that could have profound consequences.
  • Smart Thermostats: They produce very little data from these devices. Besides, some of the information they gather, such as the times of day people come home and change the heat, may affect privacy. It is feasible to keep the data at the edge and help reduce safety issues.
  • Traffic lights: Three characteristics of a traffic light make it a strong candidate for edge computing: the need to respond to real-time changes, relatively low data output, and occasional internet connection losses.

Cloud Computing

  • Conventional Applications: It's challenging to think of a traditional application needing edge infrastructure efficiency or responsiveness. It could save some milliseconds, it takes an app to load or respond to requests, but the cost is rarely worth the change.
  • Video Camera Systems: Videos produce loads of details. It's not feasible to process and store the data at the edge because it would require a broad and specialized infrastructure. Storing the data in a centralized cloud facility would be much cheaper and easier.
  • Smart Lighting Systems: Systems that allow you to monitor lighting over the internet in a home or office don't produce many details. Yet light bulbs tend to have minimal processing power - including smart ones. There are also no ultra-low latency criteria for lighting systems — it is probably not a big deal if it takes a second or two for your lights to turn on. We can build edge infrastructure for managing these types of systems, but sometimes it's not worth the cost.

An application that helps to understand the huge volume of data generated connected IoT devices. Click to explore about, IoT Analytics Platform for Real-Time Data Ingestion

Future of Edge computing and Cloud computing for IoT

Smart homes, cars, equipment, and everything else create an enormous amount of data. The IoT sector is growing at a great pace day by day, and most probably, we are heading into a future where every device is connected. Demand for computer power for devices is also increasing; cloud computing offers decentralized storage solutions for faster and cheaper deployments and makes it easy. For doing this, Developers only need to connect their systems to IoT cloud platform existing infrastructure to benefit from third-party computing power.

Smart Analytics

Internet of things generates a huge amount of data. Developers and organizations understand their customers' needs better through it. Cloud services offer a protected environment where it is possible to analyze, monitor, and store certain information. Many services, including machine learning algorithms that model insights from data and allow automation, are already equipped with AI capabilities.

Better Security

A breach of security in IoT networks may compromise entire companies and industries, impacting millions of connected devices and individuals using them. Because of their remote location and security policies, cloud storage is harder to target. In the future, before they even appear, devices can use previously collected data to detect vulnerabilities.

Inter-device Interactions

The cloud facilitates system and application connectivity, transmitting data between data centers and local nodes easily. For offline communication and micro-operations, fog and edge computing can be beneficial, reducing operating costs and increasing speed.

Java vs Kotlin
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Edge computing is becoming an evolving approach with the development of IoT to the difficult and complex challenges of handling millions of sensors/devices and the corresponding resources they need. Edge computing would migrate data computing and storage to the "edge" of the network, near the end-users, relative to the cloud computing model. Edge computing reduces traffic flows to decrease the IoT requirements for bandwidth. Also, edge computing will decrease the communication latency between edge/cloudlet servers and end-users, resulting in shorter reaction times relative to conventional cloud services for real-time IoT applications.

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