A Beginner’s Guide to Edge Computing
Edge computing is a distributed framework that puts computer processing closer to the data source — like sensors, IoT (internet of things) devices, or servers. By keeping the processor right next to the action, edge computing can increase performance, efficiency, and reliability.
But it's a big shift from other common frameworks, like cloud computing. So, we've compiled a guide to understanding edge computing, its benefits, its limitations, and advice for how to adopt it successfully.
What Are the Benefits of Edge Computing?
Putting the processor close to the action reduces the amount of time and energy required to send the same information to a central processing system. That makes edge computing perfect for projects that require real-time data processing, like IoT devices. It's also a great choice for projects where reliability is a top priority, like in healthcare or manufacturing. Edge computing benefits include improved performance, reliability, efficiency, and scalability.
Edge computing reduces latency, so sensors and IoT devices can function faster. The fractions of a second that it takes to send information from a device to an offsite processing center can add up quickly. Just cutting that time down by keeping the processor close to the source of the data noticeably improves performance.
Consider the dozens of semi-autonomous vehicles now on the road — more and more consumer and commercial cars and trucks are including autonomous features like advanced speed control, obstacle sensors, and navigation. Reducing latency by even a fraction of a second can help prevent accidents, keeping more people safe.
When the processor is right next to the source, the device can keep working even if an internet connection is lost. This is particularly important for critical systems where downtime can become an emergency. In a hospital, an edge computing framework for medical devices can allow life-saving equipment to continue operating even if the network is spotty.
Edge networks can improve reliability by extending the lifespan of your hardware too. Edge computing reduces the amount of data sent over long distances, reducing device wear and tear. In this way, edge computing can reduce the need for costly repairs or replacements, saving significant money over time.
With edge computing, you can also save money on bandwidth costs and reduce the need for expensive central processing equipment. Edge computing can be more energy efficient than other options (like cloud computing).
Say an enterprise is looking to reduce energy costs in its retail spaces, and it decides to improve the energy efficiency in its heating and cooling system. The enterprise might install a smart thermostat that adjusts heat and air conditioning use in real time.
In this case, the enterprise could choose thermostats that send all data to a central processing center, but if it has hundreds of buildings to manage, that will require a lot of bandwidth. The system is constantly sending and receiving all that information.
An edge-computing approach would put the processors close to the thermostat. Because the data doesn't travel as far from the thermostat to the processor and back, the enterprise saves significant bandwidth.
Edge computing is also modular by nature — its decentralized architecture makes it infinitely scalable.
Since processing and storage can be distributed across many endpoints, scaling happens incrementally by adding endpoints as needed without updating one large processing or storage center.
This allows edge computing systems to be more flexible and responsive to an organization's changing needs. For a centralized processor, the ability to double the number of data sources might require a significant upgrade, but with edge computing, it's simply a matter of adding more of the same devices.
Edge Computing vs. Cloud Computing
Edge computing and cloud computing are opposites. Where edge computing puts the processor closer to the action, cloud computing moves processing offsite to a centralized data center.
Edge computing is actually older than cloud computing. Before widespread internet access, edge computing was the norm. But given the cloud computing popularity surge of the past couple of decades, edge computing got somewhat left behind.
There was a lot of excitement about how much could be done via cloud computing, but now there's a push to find the most efficient strategy. This has reignited an interest in edge computing.
Cloud computing has its own unique benefits and drawbacks. We've compiled a list to help you decide which approach is right for each project.
Cloud Computing Pros
- Processing power: Cloud computing offers high processing power and can handle large amounts of data, making it perfect for projects that require significant computing resources.
- Scalability: Cloud computing can be easily scaled up or down as needed, making it a good choice for projects with changing demands and growing teams.
- Security: Cloud computing providers typically offer robust security measures, including encryption and firewalls, to protect data.
Cloud Computing Cons
- Latency issues: Since cloud computing involves processing data at a centralized location, it can be slower than edge computing for real-time processing.
- Reliance on internet connection: Cloud computing relies on a stable internet connection, which can be a problem in areas with poor connectivity.
- Data privacy concerns: Since data is often stored on servers owned by third-party providers, there may be concerns about data privacy and security, even if the network is secure.
When To Use Cloud Computing
Cloud computing is best suited for projects that require significant computing resources, such as those involving big data analysis or machine learning. It is also well-suited for projects with changing demands, as it can be easily scaled up or down as needed.
Edge Computing vs. Multi-Access Edge Computing (MEC)
Previously known as mobile edge computing, multi-access edge computing (MEC) is a type of edge computing that involves cloud services running at the very edge of a mobile network, usually 5G. This setup reduces latency and improves the quality of service for mobile network users.
- Real-time data processing: MEC allows for such fast processing that it is perfect for applications that require real-time processing of large amounts of data, like augmented reality.
- Advanced features: MEC can handle advanced features previously impossible for mobile networks, like object tracking and facial recognition.
- Reduces network congestion: Since data doesn't have to travel to be processed, it doesn't clog up network traffic.
- Flexible and scalable: Like other forms of distributed computing, MEC can be expanded or reduced at any point with minimal changes required.
- Requires good network coverage: Because MEC functions on mobile networks, it's not as reliable in areas with spotty coverage.
- Complicated setup: MEC can be more complex than other architecture, which may require specialized expertise to implement and manage.
When To Use MEC
MEC is designed specifically for mobile applications and is ideal for situations where extremely low latency is needed and mobile coverage is abundant. Thanks to MEC, apps are able to bring users more immersive experiences; for instance, storage and manufacturing facilities can deploy more advanced robots thanks to the ability to employ edge computing frameworks over 5G networks.
Edge Computing vs. Fog Computing
Compared to edge computing, fog computing uses a more distributed architecture with a greater emphasis on local processing and control. Edge computing is seen as ideal for low-latency and high-bandwidth applications, while fog computing is often used for both high- and low-latency applications.
Fog Computing Pros
- Reduced latency: Fog computing processes data closer to the source using fog nodes, reducing the amount of time it takes for information to be processed and acted upon. This can be particularly beneficial for projects that require real-time data processing, such as those involving IoT devices or autonomous vehicles.
- Increased reliability: With fog computing, devices continue to function even if an internet connection is lost. This can be particularly important for critical systems where downtime can be catastrophic, such as those in healthcare or transportation.
- Enhanced efficiency: Fog computing saves bandwidth costs and reduces the need for expensive central processing equipment. Additionally, fog computing can be more energy efficient than cloud computing.
- Scalability: Fog computing is more scalable than other frameworks since processing and storage are decentralized. This allows fog computing systems to be more responsive to changing data demands.
Fog Computing Cons
- Limited processing power: Fog devices often have limited processing power, which can make them vulnerable to attack.
- Security risks: With fog computing, data is processed and stored locally, which can create new security risks. Fog devices may not have the same level of security as centralized systems, and they may be vulnerable to physical tampering or theft. Data is also vulnerable to hacking or theft because it is stored closer to the device.
When To Use Fog Computing
Fog computing is best suited for projects that require real-time data processing and low-latency applications, such as those involving IoT devices or autonomous vehicles. It is also well-suited for projects that require high scalability and reliability, such as those in healthcare or transportation.
From the perspective of a project manager, fog computing is a good choice when dealing with large amounts of data that need to be processed quickly and reliably, as well as when dealing with critical systems where downtime can be catastrophic. However, it is important to carefully evaluate the potential security risks associated with fog computing and ensure that appropriate security measures are in place to protect sensitive data.
How Edge Computing Works
Edge computing involves processing and storing data closer to where it comes from. But unlike cloud computing, where data is stored in a central server, edge computing processes, analyzes, and stores data at the edge of the network.
First, data is produced by an endpoint device, like a sensor or IoT device. Then, instead of sending the data to a central processing location, the data is processed by a local processor, which is located close to the data source.
Once the data is processed, it's either stored locally or sent to a central (or cloud) location for further processing.
An important aspect of edge computing is the communication protocol. A communication protocol is a set of rules that define how data is transmitted and received between devices. There are many different kinds of communication protocols used in edge computing.
Edge computing frameworks use a variety of communication protocols, each with strengths and weaknesses.
- Wi-Fi — Strengths: It's widely available, fast, and reliable. Weaknesses: Wi-Fi has a limited range, and connectivity issues are frequent.
- RFID — Strengths: Can be used for tracking or identification without a line of sight. Weaknesses: Functions only in a limited range and doesn't come with much security.
- 5G — Strengths: A new technology that's very fast with low latency, ideal for large amounts of data. Weaknesses: Because it's new, the infrastructure is expensive, so it's not yet available everywhere.
- NFC — Strengths: Can make mobile payments. Weaknesses: Like RFID, it has a short range, and it can't handle a lot of data.
- Zigbee — Strengths: Doesn't need a lot of power and can support many devices. Weaknesses: It's limited to short-range functions only and, like Wi-Fi, often experiences connection issues.
- Z-Wave — Strengths: More secure than most options and requires little power to operate. Weaknesses: Can't handle many devices at once and is limited in range.
Edge Computing Security
Edge computing comes with obvious security benefits (like reducing the distance data has to travel over potentially insecure networks). But while edge computing comes with some security benefits, it also raises some security concerns.
Edge devices are often small and have limited processing power, which can make them vulnerable to attack. They may not have the same level of security as centralized systems, and they may be more susceptible to malware or other forms of attack.
How to secure your devices: Protect devices with strong passwords and regular software updates. Use encryption to protect sensitive data and consider implementing two-factor authentication to add an extra layer of security.
Edge computing involves a distributed network of devices that are often connected wirelessly. This can create new security risks, as the network may be vulnerable to attacks, such as man-in-the-middle attacks or denial-of-service attacks.
How to secure your network: Use firewalls and intrusion-detection systems to protect your network from unauthorized access. Implement strong access controls, like multi-factor authentication, to limit who can access your network and sensitive data.
Edge computing often involves the collection and processing of sensitive data, such as personal health information or financial information. This data is vulnerable to theft or misuse, and it is important to take appropriate security measures to protect it.
How to secure data privacy: Use secure communication protocols like SSL/TLS to protect data in transit. Implement end-to-end encryption to protect data as it travels between devices. Regularly monitor your network for signs of suspicious activity. Implement security information and event management (SIEM) tools to help detect and respond to security threats.
Expand Your Knowledge Beyond Edge Computing
If you are interested in enhancing your knowledge of other project management and development topics, check out the other topics we've covered in our Stream tutorials. These tutorials are created by experts in the field and provide in-depth explanations on various topics related to the subject matter. Additionally, they also offer practical examples and suggestions to help you build the best products on the market.
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