Do you remember the first ever huge and bulky sets of computers? As devices grew smaller over the years, their computing and processing powers have grown exponentially. While data warehouses and server farms were once considered to be the ultimate choice for computing speed, the focus has quickly shifted to the concept of cloud or “offsite storage”. Companies like Netflix, Spotify and other SaaS companies have even built their entire business models on the concept of cloud computing. However, cloud computing comes with a number of drawbacks. The biggest problem of cloud computing is latency because of the distance between users and the data centers that host the cloud services. This has led to the development of a new technology called edge computing moves computing closer to end users. 

Want a Job at AWS? Find Out What It Takes

Cloud Architect Master's ProgramExplore Program
Want a Job at AWS? Find Out What It Takes

What Is Edge Computing?

Edge computing is a distributed IT architecture which moves computing resources from clouds and data centers as close as possible to the originating source. The main goal of edge computing is to reduce latency requirements while processing data and saving network costs.

cloud_edge

Fig: The “edge” is where the device communicates with the Internet (source)

The edge can be the router, ISP, routing switches, integrated access devices (IADs), multiplexers, etc. The most significant thing about this network edge is that it should be geographically close to the device.

How Does Edge Computing Work

In today's ever-evolving landscape of data management, the game-changing concept of edge computing has emerged. Traditional data handling methods faced significant limitations in accommodating the exponential growth in data volume and the proliferation of internet-connected devices. In response to these challenges, edge computing introduced an innovative approach. This article delves into the transformation from conventional data processing to the fundamental principles of edge computing. We'll explore its remarkable significance and the profound impact it has on the way data is managed and processed.

The subsequent sections provide an in-depth look at the traditional data handling process, the challenges faced by traditional data centers, and the core concept of edge computing.


Traditional Data Handling Process:

  • In a traditional setup, data is generated on a user's computer or client application.
  • The data is then transmitted to a remote server via channels like the internet, intranet, or LAN.
  • At the server, the data is stored and processed, following the classic client-server computing model.


Challenges with Traditional Data Centers:

  • The rapid growth in data volume and the increasing number of internet-connected devices have strained traditional data center infrastructures.
  • A study by Gartner predicts that 75 percent of enterprise-generated data will be created outside centralized data centers by 2025.
  • This massive amount of data burdens the internet, leading to congestion and disruptions.

Edge Computing Concept:

  • Edge computing offers a solution to these challenges by reversing the data flow.
  • Instead of bringing data to centralized data centers, the concept involves bringing data centers closer to where data is generated.
  • Storage and computing resources from data centers are deployed as close as possible, ideally at the same location, to the source of the data.

Comparison of Computing Models:

Early computing

Applications run only on one isolated computer

Personal computing

Applications run locally either on the user’s device on in a data center

Cloud computing

Applications run in data centers and processed via the cloud

Edge computing

Applications run close to the user; either on the user’s device or on the network edge

Want a Job at AWS? Find Out What It Takes

Cloud Architect Master's ProgramExplore Program
Want a Job at AWS? Find Out What It Takes

Benefits of Edge Computing

Edge computing has emerged as one of the most effective solutions to network problems associated with moving huge volumes of data generated in today’s world. Here are some of the most important benefits of edge computing:

1. Eliminates Latency

Latency refers to the time required to transfer data between two points on a network. Large physical distances between these two points coupled with network congestion can cause delays. As edge computing brings the points closer to each other, latency issues are virtually nonexistent.

2. Saves Bandwidth

Bandwidth refers to the rate at which data is transferred on a network. As all networks have a limited bandwidth, the volume of data that can be transferred and the number of devices that can process this is limited as well. By deploying the data servers at the points where data is generated, edge computing allows many devices to operate over a much smaller and more efficient bandwidth.

3. Reduces Congestion

Although the Internet has evolved over the years, the volume of data being produced everyday across billions of devices can cause high levels of congestion. In edge computing, there is a local storage and local servers can perform essential edge analytics in the event of a network outage.

Drawbacks of Edge Computing

Although edge computing offers a number of benefits, it is still a fairly new technology and far from being foolproof. Here are some of the most significant drawbacks of edge computing:

1. Implementation Costs

The costs of implementing an edge infrastructure in an organization can be both complex and expensive. It requires a clear scope and purpose before deployment as well as additional equipment and resources to function.

2. Incomplete Data

Edge computing can only process partial sets of information which should be clearly defined during implementation. Due to this, companies may end up losing valuable data and information.

3. Security

Since edge computing is a distributed system, ensuring adequate security can be challenging. There are risks involved in processing data outside the edge of the network. The addition of new IoT devices can also increase the opportunity for the attackers to infiltrate the device.

Examples and Use Cases

1) Smart Home Devices:

Implementation: Edge computing is effectively utilized in smart home devices.

Scenario: In smart homes, numerous IoT devices gather data throughout the house.

Data Handling: Initially, data is sent to a remote server for storage and processing.

Challenges: This centralized architecture can lead to issues during network outages.

Edge Computing Benefits: By deploying edge computing, data storage and processing are brought closer to the smart home.

Advantages: Reduces backhaul costs and latency, ensuring continuous operation even when the network is down.

2) Cloud Gaming Industry:

Application: Edge computing is employed in the cloud gaming sector.

Objective: Cloud gaming companies aim to position their servers as close as possible to gamers.

Purpose: This approach minimizes lags and enhances the overall gaming experience, providing gamers with an immersive gameplay environment.

FAQs

1) How does edge computing work?

Edge computing works by processing data right where it's needed, close to the devices or people using it. This means data is analyzed and decisions are made on the spot, like on a user's device or an IoT gadget.


2) What is an example of edge computing?

Example of edge computing can be found in worker safety and security, where data from on-site cameras, safety devices, and sensors is processed to prevent unauthorized site access and monitor employee compliance with safety policies.


3) What is edge computing vs cloud computing?

Edge computing processes data that is time-sensitive, whereas cloud computing handles data that lacks time constraints.


4) Where is edge computing used?

Edge computing is used in various applications, such as IoT devices, autonomous vehicles, industrial automation, and even in ensuring worker safety at construction sites. It's all about processing data closer to where it's needed for quicker and more efficient decision-making.

Here’s What You Can Do Next

The adoption of edge computing has brought about data analytics to a whole new level. More and more companies are relying on this technology for data-driven operations that require lightning-fast results. If you interested to learn more about edge computing, Simplilearn’s Post Graduate Program in Cloud Computing designed in collaboration with Caltech CTME, will help you master key architectural principles and develop the skills needed to become a cloud expert. Get started with this course today to accelerate your career in cloud computing.

Our Cloud Computing Courses Duration and Fees

Cloud Computing Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Post Graduate Program in Cloud Computing

Cohort Starts: 20 Mar, 2024

8 Months$ 4,500
AWS Cloud Architect11 Months$ 1,299
Cloud Architect11 Months$ 2,000
Microsoft Azure Cloud Architect11 Months$ 1,499
Azure DevOps Solutions Expert6 Months$ 1,649