Edge Computing vs Cloud Computing

Introduction to Edge Computing and Cloud Computing

Cloud computing is significant in making the best possible choices for IoT devices. The 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 centralised data centre, it performs software, utilities, and computational data analysis closer to the end-user. The Internet of Things is associated closely with it. It is a step back from the trendy computing cloud paradigm, where all the exciting bits occur in data centres. Rather than using local resources to collect and send data to the cloud, decisions are taken place on local servers.

Edge Computing vs. Cloud Computing

edge-computing-vs-cloud-computing

Cloud and edge computing are different, that cannot replace each other. Edge computing is used to process time-sensitive data, and cloud computing is used to process non-time-triggered data.

Cloud Computing	Edge Computing
Cloud computing is centralised servers stored in faraway, large-scale data centres.	On the other hand, it is a highly distributed and global computing infrastructure closer to the devices and users.
It processes data on a central cloud server far from the data sources.	Process data on site quickly and analyze 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 storage capacity.	As it processes in the device, it has lower processing power and storage capacity.
It is suitable 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 connectivity, data movement, bandwidth, and latency cost are relatively high.	Fewer latency and bandwidth requirements increased performance and lower operational expenses.

Advantages of Cloud Computing

The advantages of cloud Computing in the 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 centralised, it will be productive no matter where the 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 easily 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

It reduces the cost of maintaining and managing hardware as users use 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 customised 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 cell phones 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 utilise 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 centres or cloud. Click to explore about, Artificial Intelligence in Edge Computing

What are the advantages of Edge Computing?

The advantages 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 network's resilience improves when edging devices store and process data locally. Microdata centres 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 optimises the flow of data that reduces 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, mainly due to regulations like GDPR.

Security and Privacy

In cloud computing, your network's overall security improves because it is not always required to send sensitive data to the cloud for processing. Business and operational procedures become even more vulnerable if your data is stored in the cloud. Because less data 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?

Below are the highlighted Risks and Benefits:

Challenges of Edge Computing

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

Challenges of Cloud Computing

High Risk: Data is stored centralised when information is transferred from edge to cloud, so the chances of attacks are higher.
Potential Loss: Due to the increase in cloud infrastructure data, threats' chances also increase.
Longer Outage Time: As data is transferred from edge to cloud, it takes longer than edge computing.
Look at security: Using a cloud computing system, we cannot trust security, and companies need to compromise data confidentiality.

Use Cases of Edge Computing and Cloud Computing

Below mentioned are the Use Cases of Edge computing and Cloud Computing.

Edge Computing

Autonomous Vehicles: Self-driving cars can gather vast information and make real-time choices on or near the road for passengers' and others' safety. In-vehicle response times and latency problems could trigger millisecond delays — a scenario with 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 centralised cloud would be much cheaper and more accessible.
Intelligent 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 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 enormous data. The IoT sector is growing incredibly, and we are probably heading into a future where every device is connected. Demand for computer power for devices is also increasing; cloud computing offers decentralised storage solutions for faster and cheaper deployments and makes it easy. To do this, Developers only need to connect their systems to the IoT cloud platform existing infrastructure to benefit from third-party computing power.

Smart Analytics

The Internet of Things generates a massive amount of data. Developers and organizations understand their customers' needs better through it. Cloud services offer a protected environment 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 security breach 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 centres and local nodes efficiently. For offline communication and micro-operations, fog, and edge computing can be beneficial, reducing operating costs and increasing speed.

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Conclusion

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 related resources they need. It would migrate data computing and storage to the "edge" of the network, near the end-users, relative to the cloud computing model.It 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 than conventional cloud services for real-time IoT applications.