Difference between Edge Computing vs Cloud Computing?

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.

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 Risks and Benefits?

Benefits of Edge Computing

  • Low Bandwidth: As in edge computing, a decision is made on edge, require low bandwidth.
  • High Security: Data does not transfer on the cloud; that's why chances of a steal of information are significantly less and increase security.
  • Quick decision making: The decision took place at the edge within few milliseconds.
  • Better Application Experience: Application like Siri, Google Maps, Alexa give quick responses to the user; that's why users get a better experience.

Risks 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.

Edge computing is a distributed computing paradigm that makes necessary computation and data storage closer to the devices where it is collected.


Benefits of Cloud Computing

  • Productivity Anywhere: As data is centralized, it will be productive anywhere irrespective of the user's location.
  • Low cost of ownership: The cost of cloud computing is very less, and users can take ownership according to requirements.
  • Remote Working: Users can work remotely through virtual machines.
  • More Powerful: As a large amount of data, is generated it helps make better decisions for technologies like smart traffic lights.
  • Easily Upgraded: User who is working on the cloud can upgrade their versions.

Risks 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.

Read more about Edge AI Implementation


Use cases where Edge Computing or Cloud Computing is best suited for

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.


Click to explore about Drivers of Edge Computing and Edge AI


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.


Summary

  • 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.
  • Cloud computing will prevail as an essential part of the IoT ecosystem for intricate and historical data processing. The amount and practicality of AI-enabled IoT solutions continue to increase.
  • Nevertheless, edge computing is a better and more agile way for real-time decision-making, offering computing and analytics capabilities to end devices.

Read more about What's the Difference Between Cloud, Edge, and Fog Computing?

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