Edge computing is one of the latest innovations in the field of data processing and data analysis. It is the next step in the evolution of the internet.
Edge computing is a term for distributing computation and data storage closer to the network’s edge, or “the edge.” By moving data and computation closer to where it is needed, edge computing can reduce latency and improve performance.
In many cases, sending data back and forth to a central location can take too long. For example, if you are trying to stream a video from a remote location, you may have a better experience if the video is stored and processed closer to you rather than sending it back to a server farm.
Edge computing makes processing and storing data instantaneous by bringing computing systems close to the device, application, or component that collects or generates data.
“Technology is going to disrupt the future of work,
Perhaps sooner than we thought”
– Brian Cornell
Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). This technology allows users to access their data and applications from anywhere in the world via the Internet.
Internet-connected devices often outsource their computational needs to more powerful resources in the cloud, using broadband speeds to stream data and back-and-forth. A loosely defined cloud is a collection of servers that host data and software, accessed and used by devices that can theoretically be anywhere on the internet. In cloud computing, host resources can be shared among subscribers from a centralized source (Napoli, 2022) .
All new technology aims to improve or replace old systems with better functions/features and smarter brains. Edge computing is no different and offers many potential benefits to its users.
The exponential growth of the number of IoT devices requires a change in how we gather and analyze data. Just think of how many smart home devices you own, and then try to imagine how many there are in healthcare, transportation, or industrial solutions. The volume of data these devices continually send to servers is massive and, in most cases, exceeds network bandwidth. A traditional centralized cloud architecture, however robust or performant, cannot keep up with the real-time needs of these devices (Roh, 2020) .
As the world becomes increasingly connected, the demand for low-latency, high-performance applications will only increase. Edge computing is well-positioned to meet this demand, and we will likely see continued growth. Here are some of the benefits that it offers.
- Reduced Latency
One key advantage of edge computing is that it can help to reduce latency or the time it takes for data to be processed. This is because less data needs to be sent to a remote location for processing, which can greatly reduce processing time.
The vast data streaming to the cloud may create a traffic jam. Cloud-based platforms and analytics can sort, clean, structure data, and perform analysis. But edge computing takes some of that load off the cloud platform, reducing latency. By analyzing locally, the return time from the cloud to the company using the information is reduced, leaving the cloud-based platform for more critical tasks such as analytics (Ray, 2022) .
By processing data closer to the point of collection, edge computing can help reduce latency and improve real-time responses. If an organization is collecting data from multiple sources, it can use edge computing to process the data before it is sent to the cloud.
“A distributed, decentralized network is more of a process than a thing.
In the logic of the net there is a shift from nouns to verbs”
2. Increased Efficiency
It is also a more efficient way of processing data, as it does not need to be sent back and forth between a central location and the devices generating it. Edge computing can help reduce bandwidth requirements and improve network efficiency by processing data before it is sent to the cloud.
Data processing times are vastly reduced since minimal data needs to be sent to a central location. One example of edge computing is when an organization uses sensors to collect data about the environment around them. The data from these sensors can be processed at the network’s edge, near the sensors, instead of being sent to a central location. This can be done using various methods, including fog computing, which uses edge devices to process data before it is sent to the cloud, or purpose-built edge computing devices.
- Improved Security
Edge computing can also be more secure than cloud computing, as data is not stored in a central location where it could be hacked. By processing data locally, edge computing can help reduce the risk of data breaches and improve security.
Edge devices are often less susceptible to attacks than centralized servers, as they typically have fewer open ports and are less accessible. It can help to protect data in the event of a network outage, as data can be processed and stored locally until a connection is re-established.
Edge computing can help save on cloud computing costs by reducing the amount of data that needs to be sent to the cloud. By moving data processing and storage closer to the point of collection, organizations can avoid the need to send data back and forth between centralized data centers. This can help to save on bandwidth and other associated costs.
In addition, edge computing can cut down on the need for expensive data center infrastructure and the power required to run it. Another cost-related advantage of edge computing is that it can help to improve operational efficiency. By processing data at the edge, organizations can avoid the need for time-consuming data transfers between different locations.
“The key benefit of edge computing is the ability of devices to compute, process
And analyze data with the same level of quality as data analyzed in the cloud,
But without latency”
-Dr. Shafiq Rab
Edge and cloud computing work together to add tremendous value to numerous industry verticals. By working together, edge and cloud computing can instantaneously help complete resource-intensive tasks such as large-scale artificial intelligence (AI) and machine learning (ML) operations. With 5G and other technological advancements gaining popularity in 2022, edge and cloud computing are expected to see numerous new opportunities across industries (Ashtari, 2022) .
As with all things in this world, edge computing has challenges and drawbacks. Despite the many advantages of edge computing, several challenges need to be considered, including:
One of the main challenges of edge computing is fragmentation. With so many different devices and networks, deploying and managing edge computing solutions can be difficult. Difficulties can arise in managing and updating data and applications that are distributed across many locations.
Because so many different types of devices are present, it can be difficult to manage them all effectively. This fragmentation can lead to data collection, analysis, and decision-making problems.
One way to overcome fragmentation is to use a gateway that can connect all of the devices. This can help ensure that data is collected properly and decisions are made based on accurate information.
Another way to reduce fragmentation is to use standard protocols for communication between devices. This can help ensure that all devices are compatible and that data can be transferred between them easily. Using standard protocols can also make it easier to manage the devices, as they will all be using the same language.
Security is a tough problem that needs to be considered, but this security risk is at the local level. As data is stored and processed at the network’s edge, it is important to ensure that it is properly secured to prevent it from being intercepted or tampered with.
Since nodes can talk to each other without contacting the cloud storage, and due to the distributed nature of edge computing, it is increasingly difficult to verify the authenticity of the data. Edge computing systems must be designed with security in mind from the outset.
Finally, Data collected at the network’s edge is often unencrypted and therefore vulnerable to interception.
Privacy is a huge issue with all decentralization technology. Since edge computing stores and processes data at the edge of the network, it is important to ensure that it is properly protected to prevent it from being accessed by unauthorized individuals.
Then there is the issue with the devices themselves; edge devices are most often controlled and operated by third-party service providers, meaning that user data is stored and processed on servers outside the user’s control.
Many edge devices are resource-constrained, meaning they may not have the processing power or storage capacity to protect user data properly. As a result, it is important for users to carefully consider the privacy implications of using edge computing services.
“One single vulnerability is all an attacker needs”
Another challenge that needs to be considered is scalability. Edge computing solutions need to scale up or down to meet the demands of a changing environment. In addition, since edge computing relies on individual devices, storage and networking capabilities cannot be upgraded as easily as in cloud computing.
Another scalability issue is the power consumption of these devices. They need to be able to run for long periods without requiring a lot of power. This can be a challenge for devices used in remote areas with no reliable power source.
Finally, the cost of these devices can be a barrier to entry for many organizations. They are not always affordable for small businesses or individual users.
It is possible to overcome these challenges with proper planning and security systems, but that is sometimes more trouble than it is worth. Perhaps we might have a much easier time implementing systems like this in the future.
The cloud has been a popular topic of discussion for years now, but with the recent rise in edge computing, the debate has shifted. So, which is better? Cloud computing or edge computing?
We noticed that both platforms are different and can’t replace each other. It is true that many organizations have accepted that edge technology due to its overcoming minor issues of cloud computing (Arora, 2022) .
There are pros and cons to both approaches. Cloud computing offers the benefit of flexibility and scalability, as well as reduced upfront costs. However, it can suffer latency issues as data travels back and forth between the cloud and the user.
On the other hand, edge computing can offer lower latency as data is processed closer to the user. However, it may be more expensive and less scalable than cloud computing. Edge computing significantly benefits organizations that need fast, efficient and secure data processing. However, edge computing is not right for every organization.
“You can’t defeat the speed of light. Next gen experiences will
Require us to completely re-think the current internet. Data proximity
And data velocity will be embedded in the urban core”
So, which is the better option? It depends on the specific needs of the user. For some, the cloud will be the better option, while edge computing will be a better fit for others. Organizations should carefully consider their needs and requirements before deciding whether edge computing is the right solution.
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: Robert Napoli, 25th April 2022, Edge Computing: What Is It And Why Does It Matter?
: Lucas Roh, 5th March 2020, Cloud Computing Vs. Edge Computing: Friends Or Foe?
: Mira Ray, 26th January 2022, Edge Computing: The Advantages And Disadvantages
: Hossein Ashtari, 7th February 2022, Edge Computing Vs. Cloud Computing: 10 Key Comparisons
: Shivam Arora, 9th August 2022, Edge Computing Vs. Cloud Computing: Key Differences To Know