Because of the growing trend of businesses implementing real-time applications, there’s an increase in the demand for edge computing technologies that can deliver better responsiveness to end users while keeping costs down.
Edge computing refers to the practice of storing and processing data locally, rather than in centralized cloud data centers. It could change the way we use technology forever! Don’t believe me? Keep reading to learn more about edge computing and why it should be on your radar moving forward!
What Is Edge Computing?
In a nutshell, edge computing refers to processing data at or near where it originates from. In an age of digital transformation driven by big data, edge computing enables fast processing time while protecting sensitive information.
For example, a utility company may use edge computing to monitor a customer’s electricity usage in real-time. They may also use that same information to determine when a technician should visit that house for maintenance or repair work.
Today’s organizations are increasingly adopting hybrid cloud strategies due to skyrocketing demand for compute power across both cloud platforms and on-premises infrastructure. That increased demand requires advanced infrastructure management technologies designed to ensure optimal application performance no matter how many moving parts get involved in your solution strategy.
How Does Edge Computing Work?
Edge computing works by taking your data (e.g., sensor readings, images) and processing it where it actually takes place.
For example, instead of transferring an image file to a central server (in order to process it), image recognition could run on IoT devices themselves. This would send only valuable insights back to a centralized hub for storage or further action. These capabilities can exist alongside traditional cloud-based functions. For example, edge nodes may make sense in fields like agriculture, while centralized storage makes more sense in some other scenarios.
There’s one big question: How will my business use edge computing? The answer will depend on how you define your digital transformation efforts over time; whether that means reducing costs, improving efficiency through local processing, or something else entirely.
Edge Computing Acts On Data At The Source
The new complexity and scale of data that’s created by connected devices has surpassed infrastructure and network capabilities. New edge computing architectures allow businesses to process, store, and analyze data on site. That decreases latency from days to seconds or even milliseconds.
In fact, research firm MarketsandMarkets expects global spending on edge computing solutions to increase from $1.43 billion in 2017 to $24.84 billion in 2022 at a Compound Annual Growth Rate (CAGR) of 35% during that period.
These numbers are incredibly exciting for both businesses and end users. There’s clearly an enormous market opportunity here with disruptive potential. But how can we best position ourselves to profit from it?
Why Edge Computing?
Regardless of what you think of edge computing, it’s clearly on its way to making a big impact on how IT functions. And while there may be some resistance to adopting these capabilities right away, we expect them to become an essential part of data infrastructure.
The following are some basic capabilities that will play key roles in edge computing’s growth:
1) real-time analytics:
Real-time analytics help businesses analyze data as it arrives, rather than having to wait until all the data from a specific day or month gets collected. This can provide more timely insights into sales, traffic, inventory levels and other key metrics.
2) distributed computing:
Distributed computing helps businesses distribute their existing data centers out to meet current needs. Besides reducing overall cost by distributing components across multiple facilities, distributed computing also allows businesses to reduce latency issues that may otherwise slow down application processing speeds.
3) cloud-based capabilities:
Edge computing overlaps many key concepts in cloud computing. However, it’s critical for organizations to remember that edge computing can also exist without a connection to a traditional data center or other high-level cloud services provider. This allows companies to create hybrid clouds. They do this by using their own private networks combined with offsite processing power offered by third parties, like Amazon Web Services (AWS).
4) analytics-driven applications:
Analytics applications make use of large amounts of data collected at multiple points throughout an organization. For example, AT&T has created its XAPP platform around analytics-driven apps. Users interact directly with these apps as they go about their daily business. They essentially create virtual employees to help them perform specific tasks. And while such technology is still in its early stages, it’s expected to become a major part of how businesses operate.
5) software-defined networking (SDN):
SDN makes networks more flexible by breaking down barriers between hardware and software components on a central network. This allows companies to better customize their networks for different needs while reducing costs.
6) edge intelligence:
Once you have edge computing in place, it’s important to take advantage of its intelligence features to maximize business opportunities.
For example, embedding local intelligence capabilities into a car can help you create a virtual driver that can aid real drivers with key insights as they drive. Examples include identifying dangerous road conditions or offering directions that might be unavailable from other forms of navigation systems.
7) edge-based storage:
As more devices become part of cloud computing efforts, there will be more need for onsite storage solutions that can process data on location rather than sending everything through a centralized server system. Some experts suggest that 80% of all future IoT traffic will rely on edge computing systems for local processing and storage capabilities.
8) edge-to-cloud synchronization:
Synchronization between edge computing solutions and traditional cloud services allows businesses to use both locally processed information and central information provided by other parties. This ensures you’re leveraging multiple sources of information without having to sacrifice quality or accuracy.
9) improved management of smart products:
With thousands of smart products set to hit market over time, it will be critical for companies to implement edge solutions capable of collecting large amounts of data while also analyzing any issues that may crop up during day-to-day use.
Organizations will want smart products they produce or sell to work seamlessly with other products on sale through their stores. However, they want to do it without compromising key business goals like security or low latency requirements.
10) location-based services (LBS):
As more businesses make use of IoT technology, you’ll see increased demand for LBS solutions designed to coordinate data collection efforts between multiple distributed systems.
For example, an LBS platform could keep track of where sensors are throughout a city. It can also tell how long each sensor has been inactive before flagging problems in need of attention.
There are many more ways that you can benefit from edge computing. These 10 features have given you a solid overview of how edge computing works and its major benefits. If any of these capabilities sound interesting to your business, be sure to take time before making final decisions to research edge computing even further.
There’s a lot of opportunity out there for those who move quickly enough to take advantage.
Key Capabilities For Edge Computing
The key capabilities for edge computing include:
We can store data close to where it’s generated, rather than moving data from a centralized facility hundreds or thousands of miles away;
We can perform any type of computation in near real time at an edge location
Processed and pushed to users without requiring large amounts of local storage, similar to how radio stations push music in real time versus using prerecorded tracks.
All data must remain secure even when transmitted over longer distances than with other cloud configurations.
This application for edge computing comes from streaming data to people as it’s generated. It is so companies can perform real-time analytics on their operations without having to store all that information in a central location. Instead, only summaries, or aggregates of that data, get stored centrally. Theoretically, organizations could use these summaries for regulatory reporting requirements by using random sampling techniques to ensure they have accurate summaries of their entire population.
Decision support systems:
Edge computing can help organizations improve their decision support capabilities, since they can process data close to where it’s generated rather than having to ship it to a central location for processing.
For example, retailers could monitor supply chain data in real time and use that information to streamline their distribution operations. This could include monitoring environmental conditions like temperature, humidity, or barometric pressure. Also included are process changes, such as equipment failures or manual intervention.
Artificial intelligence (AI):
AI is a good fit for edge computing, given that AI models often require massive amounts of data to train. One example of where edge computing could play a role in AI training involves autonomous vehicles, since they need to have location-specific information about roads, parking spaces and stop signs. In these cases, data from one area gets used for training but not needed by other vehicles in different parts of a city or country.
They also need to provide solutions that cover big data storage and processing, real-time analytics and AI training. In addition, each layer must be secure, so sensitive information doesn’t leak out or become vulnerable if a device becomes compromised. Finally, users must have tools they’re comfortable using so they can interact with their data from anywhere on any device.
In addition, many of these technologies include security features to ensure data remains secure as it’s being transmitted from one location to another. As a result, edge computing has become a key focus for several technology vendors. Vendors that have been active in developing edge computing solutions include Cisco Systems Inc., Hewlett Packard Enterprise Co., IBM Corp., Microsoft Corp. and VMware Inc.
Edge Vs. Cloud Vs. Fog Computing
Edge computing, cloud computing, and fog computing: all three technologies are changing how we live our lives. But which one should you choose for your next project or product? To answer that question, we need to look at how they differ from each other. First, let’s get an overview of each technology: what it does, where it works best, and what its benefits are. Then we can compare them in more detail.
What it does: The term edge computing refers to a decentralized computing environment where computational resources are located close to end users. These systems use local input/output (I/O) devices for processing data generated at or near an endpoint. That improves responsiveness by eliminating latency that occurs with centralized data centers.
This approach also reduces bandwidth costs by minimizing traffic, which must get transmitted over long distances. It additionally reduces storage costs by reducing the need for large storage facilities.
Where it works best: With edge computing, it does processing very close to where data gets generated. This makes sense for use cases like manufacturing, healthcare, and retail, where data comes from a variety of sources and therefore must get processed on-site rather than centrally. (And of course, industries such as transportation may also make use of distributed systems.) Also, networks are becoming faster all the time.
What its benefits are: One of edge computing’s biggest advantages is that it can make systems more responsive to change by moving computation closer to where data gets created.
For example, sensor-based machine learning algorithms can get trained locally to classify real-time data based on changing business or environmental conditions. This approach enables systems to adapt their behavior rather than relying on pre-programmed decision rules. That makes them far less likely to break down when unforeseen conditions occur.
What it does: Cloud computing — also known as cloud services or simply cloud—refers to online, hosted systems that deliver software, storage, databases, analytics tools, machine learning capabilities, and more to users via the internet. Using these systems allows businesses to avoid buying new software licenses or hardware equipment; instead, they can pay for access on a per-use basis.
Where it works best: Because cloud-based systems get accessed over a network, they can get used by anyone with an internet connection. And they have many real-world applications. For example, cloud-based platforms get used for:
- building collaboration tools
- storing enterprise data in online repositories
- performing large-scale simulations of complex operations
- launching websites with minimal IT infrastructure investment
- managing supply chains across global networks of suppliers and customers
What its benefits are: One of cloud computing’s biggest advantages is that it allows enterprises to take advantage of a large, shared pool of computational resources without having to buy their own hardware.
For instance, cloud-based systems can offload computationally intensive tasks from a company’s in-house servers in order to reduce costs. In addition, access to software in a public cloud can allow firms to evaluate tools before deciding whether they want to purchase them.
What it does: Fog computing — also called fog networking or fogging—refers to a network architecture in which computation, storage, and control gets dispersed closer to endpoints. The goal of a fog network is to enable devices to connect seamlessly with each other and with other resources on a cloud-based system.
Where it works best: It can reduce data latency by routing traffic from an endpoint device through multiple nodes that process information before sending data over a long distance. In addition, a fog network offers advantages where there are no wired connections to endpoints. This makes fog networking appropriate for use cases such as:
- industrial monitoring and control systems
- smart buildings
- asset tracking devices used in logistics applications
- wireless sensor networks in agriculture and environmental monitoring applications
- mobile-phone based health monitoring systems
What its benefits are: One of fog computing’s biggest advantages is that it offers a cost-effective way to deploy machine learning algorithms at or near their sources. For example, we can load AI tools onto devices so that they can respond to changing environments without having to travel over a network connection. Another advantage of using fog systems for machine learning applications is that training data doesn’t have to be transmitted over long distances.
Edge Computing Use Cases And Examples
Prime use cases which use edge technology include:
- Augmented Reality (AR) and Virtual Reality (VR)
- Industrial Automation
- Video Surveillance
- Ride Sharing/Fleet Management
One key advantage of using edge computing solutions is their ability to decrease latency issues for time-sensitive applications.
For example, a taxi service provider may want a solution that will reduce end-user wait times by performing more locally on a user’s smartphone. Using an AR platform based on IoT data collected from smartphones within range, users could get real-time directions through their phone displays while still en route to their destinations.
This can create significant value for ride sharing providers who are essentially in competition with one another over getting customers from Point A to Point B in as few steps as possible.
Your Journey To Edge Computing: Things To Consider
Some things to consider before adding edge computing to your company’s digital transformation strategy:
Is it more cost-effective than traditional data centers?
Will you be able to move sensitive data off site with local encryption capabilities?
Will security breaches be less likely with more localized storage capabilities?
Will it improve latency rates for your users?
How will you monitor performance in real-time without overloading your operations center?
We can address the above questions through careful planning. It’s important to monitor what trends are driving today’s digital transformation. Hybrid cloud, edge computing, artificial intelligence (AI), robotics, augmented reality/virtual reality (AR/VR), Internet of Things (IoT); they’re all here to stay.
What Are The Benefits Of Edge Computing?
There are plenty of benefits that come along with edge computing,. The first benefit is one you’re likely familiar with if you’ve used any sort of cloud service, specifically Amazon Web Services (AWS). When handling things like your documents or images, just about everyone needs cloud storage. AWS offers quick, easy access to storage space on their servers. The same goes for Google Drive or Microsoft OneDrive, which both offer similar services to their customers.
These companies allow users to store large amounts of data in a safe place where it can get accessed from anywhere. The main issue with having all your data stored in the cloud is there are limits in how much data you can actually store. This is especially true if you only use these companies for basic file-sharing services. For example, both AWS and Google have a file size limit between 25 MB – 100 MB depending on what type of account you have available. This might not seem like a lot, but once those files add up, there’s really nowhere else to go. That’s where edge computing comes into play.
Edge computing gives companies an alternative way of storing their data without needing access to the internet or even a physical server that houses that data. Cloud servers can be hundreds, thousands, and sometimes even millions of miles away from your actual location.
That makes it difficult to transfer extensive files within a reasonable amount of time. When these files are too big for local storage, businesses need another option where they can quickly grab files from whatever cloud service they’re using and push them out to other locations. That’s where edge computing comes in handy.
Another benefit of edge computing is its ability to process huge amounts of data in real time, as opposed to cloud-based systems, which often rely on pre-processing. When using traditional cloud servers, you still need to make sure your data is ready for distribution once it’s gotten processed locally. This isn’t an issue with edge computing. You can push whatever information you want out whenever you want it, saving a lot of time along the way.
Challenges Of Edge Computing
Don’t be mistaken, though. There are challenges involved in edge computing. The first challenge stems from a lack of infrastructure. Although many businesses have deployed intelligent IoT devices to collect, process and analyze data at local servers, there isn’t a unified way for all of those devices to communicate with one another. That means it’s much more difficult to make sense of those streams of data from various sources at scale compared to what you could do with everything centrally managed.
The second challenge of edge computing for companies is maintaining a secure data flow. If you’re using edge computing as part of a hybrid cloud strategy, your data may be vulnerable because it might pass through two or more clouds before reaching its final destination.
The next challenge of edge computing for business is security. As noted above, not only do you have to worry about keeping your data secure during its journey from point A to point B; you also need to make sure that data never ends up in hands that it’s not supposed to be in once it arrives at its destination.
Finally, enterprise edge computing is challenging because it requires substantial investment. Not only do you need to invest in edge-computing hardware; you’ll also need to invest plenty of time and money educating your business on how best to put it all to use.
Privacy And Security In Edge Computing
As with anything that’s connected to the internet, edge computing presents both privacy and security challenges. Privacy regarding edge computing becomes even more complex because data associated with an application or device might be near or on a user. This poses a concern for cyber-security professionals, since malicious actors may have easier access to sensitive information when it’s not stored in heavily guarded facilities.
The importance of carefully securing data at each point along its journey increases significantly when you add local storage to network models. If hackers can get into systems that are closer to users, they may have better luck stealing sensitive information than they would if an attack originated from overseas. Building privacy and security mechanisms can resolve this in edge computing platforms from their earliest iterations and perform regular testing to ensure these mechanisms remain robust.
Another security challenge for edge computing in business is physical tampering. While it’s obviously easier to tamper with a server farm, someone who wanted to tamper with an individual system or device could theoretically gain access and inflict some damage. It might not be as easy as hacking into a central database, but it’s not impossible either. As a result, a lot of work needs to go into securing edge systems against theft or damage.
Discover The Future Of Edge Computing In Your Industry
Will Edge Become The New Normal?
Technology manufacturers are now equipping their products with edge technology features in order to keep up with industry trends. It won’t be long before we see widespread adoption of cloud services without centralized processing capabilities.
This could mean you’ll soon only need one network infrastructure, so you can grow your business faster than ever before. Edge computing increases resource utilization because traffic doesn’t need to travel across a WAN or through distant data centers. This will help create a more flexible, less expensive and highly scalable approach to deployment that improves operational performance for businesses across all industries.
How Do We Get There?
We can attribute the acceleration of edge computing technology adoption to several factors, including an increase in data collection points. We particularly find this in industries like transportation, healthcare, smart cities, retail and logistics.
After years of research into how networks have changed since IPv4 became commonplace, we’re seeing organizations build new applications on top of existing architectures. This means data collection points are growing increasingly complex, so even if you’re not looking to implement edge computing today, start planning for it. This technology gives you access to instant processing capabilities that can help with monitoring large volumes of traffic, real-time analytics, video streaming and much more. It also saves on bandwidth costs by cutting down on unneeded network transfers.
What Are The Stakeholders Looking For?
Last, but not least, understanding who your edge computing stakeholder groups are will help you take a long-term view of opportunities for investment. Each stakeholder group has its own set of challenges, risks, and rewards. You’ll need to take all of it into account as you look to future proof your edge computing strategy. That said, it’s not always easy to see what’s happening in these different areas. It really pays to get out there and talk with some of them directly.
For example, manufacturing or transportation executives would tell you that their biggest pain points revolve around visibility and getting real-time analytics from their supply chain partners. In contrast, healthcare professionals might be more concerned with tracking data from patient sensors. Meanwhile, insurance companies care about where things happen within their network.
Discovering the future of edge computing in your industry is simple. It’s right here, waiting for you to tap into. The challenge comes from getting all these stakeholders onboard—but if you can, you’ll see some tremendous rewards down the road.
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