Edge means literal geographic distribution. In the beginning, there was One Big Computer. Then, in the Unix era, we learned how to connect to that computer using dumb terminals. Next, we had personal computers.
Right now, we’re firmly in the cloud computing era. Many of us still own personal computers, but we mostly use them to access centralized services like Dropbox, Gmail, Office 365, and Slack. Additionally, devices like Amazon Echo, Google Chromecast, and the Apple TV are powered by content and intelligence that’s in the cloud.
Now with the growth of the Internet of Things (IoT), billions of devices are generating huge amounts of data. But to store or analyze all that data in real-time is almost impossible. This is where edge computing becomes relevant.
What is Edge computing?
· Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth.
· Edge computing significantly extends this approach through virtualization technology that makes it easier to deploy and run a wider range of applications on the edge servers.
Why Does Edge Computing matter?
Bandwidth: Bandwidth is the amount of data that a network can carry over time, usually expressed in bits per second. All networks have limited bandwidth, and the limits are more severe for wireless communication. This means that there is a finite limit to the amount of data or the number of devices that can communicate data across the network. Although it’s possible to increase network bandwidth to accommodate more devices and data, the cost can be significant, there are still (higher) finite limits and it doesn’t solve other problems.
Latency: Latency is the time needed to send data between two points on a network. Although communication ideally takes place at the speed of light, large physical distances coupled with network congestion or outages can delay data movement across the network. This delays any analytics and decision-making processes and reduces the ability of a system to respond in real-time.
Congestion: The internet is basically a global “network of networks.” Although it has evolved to offer good general-purpose data exchanges for most everyday computing tasks — such as file exchanges or basic streaming the volume of data involved with tens of billions of devices can overwhelm the internet, causing high levels of congestion and forcing time-consuming data retransmissions.
How the Edge Computing Works?
· Edge computing works by pushing data, applications, and computing power away from the centralized network to its extremes, enabling fragments of information to lie scattered across distributed networks of the server.
· The Processing will be done at the location near to the user or at the device which will be having the processing and the storing capacity.
Edge Computing Architecture
Edge computing is a part of the overall architecture as it was necessary to provide key services at the edge. The following illustrates the implementation with further extensions having since been made.
1. Device Edge: The actual devices running on-premises at the edge such as cameras, sensors, and other physical devices that gather data or interact with edge data. Simple edge devices gather or transmit data or both. The more complex edge devices have the processing power to do additional activities. Examples of such applications include specialized video analytics, deep learning AI models, and simple real-time processing applications. IBM’s approach (in its IBM Edge Computing solutions) is to deploy and manage containerized applications on these edge devices.
2. Local Edge: The systems running on-premises or at the edge of the network. The edge network layer and edge cluster/servers can be separate physical or virtual servers existing in various physical locations or they can be combined in a hyperconverged system. There are two primary sublayers to this architecture layer. Both the components of the systems that are required to manage these applications in these architecture layers as well as the applications on the device edge will reside here.
· Application layer: Applications that cannot run at the device edge because the footprint is too large for the device will run here. Example applications include complex video analytics and IoT processing.
· Network layer: Physical network devices will generally not be deployed due to the complexity of managing them. The entire network layer is mostly virtualized or containerized. Examples include routers, switches, or any other network components that are required to run the local edge.
3. Cloud: This architecture layer is generically referred to as the cloud but it can run on-premise or in the public cloud. This architecture layer is the source for workloads, which are applications that need to handle the processing that is not possible at the other edge nodes and the management layers. Workloads include application and network workloads that are to be deployed to the different edge nodes by using the appropriate orchestration layers.
Examples for Cloud edge: AWS CloudFront, Azure CDN
Edge Computing Use Cases
· Micro data centers: These are used wherever access to a full-scale database is limited, but there is a great need for computing power. They can be installed in remote facilities, in factories to power up IoT devices, or in any location where putting up a traditional data center would be impossible and moving computations into the cloud would be costly.
The CORD project — a reference NFV implementation that was constructed using commodity hardware and open-source software — is a good example of such a solution. CORD is a general-purpose service delivery platform that can be configured to provide services for the residential, enterprise, or mobile customers, or indeed any combination of the three.0
· SDN/NFV: Less common take on edge computing SDN and NFC techniques can be employed in the data center to optimize base performance. Cloud computing tends to be seen as infinite computing power that sits a mere arm’s length away. And from the client’s point of view, it is indeed that. But the provider or operator sees the other side and must provide computing power by utilizing hardware to the greatest extent possible. Empowering the cloud with SDN and NFV at the hardware level leads to improved asset management and a reduced need for computing power for operations.
Microdata centers are even more reliant on SDN and NFV technologies than traditional data centers, as they need to be space-effective. Given this, virtualization is one of the best ways (and clearly the most convenient one) to run all the necessary devices (router, load-balancer, firewall, etc.) inside the data center.
Leading hardware providers thus deliver SDN-enabled edge devices like routers that can be reprogrammed while their internal operations are redesigned with SDN and NFV techniques. The final goal is to extend the cloud beyond the data center and stop thinking about the network of devices as a sum of separate machines, but rather much more. We need seamless and secure connectivity to the cloud, so we can easily migrate and manage workloads. Edge can be treated as an extension of the Cloud.
Edge computing Examples
· Manufacturing: An industrial manufacturer deployed edge computing to monitor manufacturing, enabling real-time analytics and machine learning at the edge to find production errors and improve product manufacturing quality. Edge computing supported the addition of environmental sensors throughout the manufacturing plant, providing insight into how each product component is assembled and stored and how long the components remain in stock. The manufacturer can now make faster and more accurate business decisions regarding the factory facility and manufacturing operations.
· Farming: Consider a business that grows crops indoors without sunlight, soil, or pesticides. The process reduces growth times by more than 60%. Using sensors enables the business to track water use, nutrient density and determine optimal harvest. Data is collected and analyzed to find the effects of environmental factors and continually improve the crop growing algorithms and ensure that crops are harvested in peak condition.
· Network optimization: Edge computing can help optimize network performance by measuring performance for users across the internet and then employing analytics to determine the most reliable, low-latency network path for each user’s traffic. In effect, edge computing is used to “steer” traffic across the network for optimal time-sensitive traffic performance.
· Workplace safety: Edge computing can combine and analyze data from on-site cameras, employee safety devices, and various other sensors to help businesses oversee workplace conditions or ensure that employees follow established safety protocols — especially when the workplace is remote or unusually dangerous, such as construction sites or oil rigs.
· Improved healthcare: The healthcare industry has dramatically expanded the amount of patient data collected from devices, sensors, and other medical equipment. That enormous data volume requires edge computing to apply automation and machine learning to access the data, ignore “normal” data and identify problem data so that clinicians can take immediate action to help patients avoid health incidents in real-time.
· Transportation: Autonomous vehicles require and produce anywhere from 5 TB to 20 TB per day, gathering information about location, speed, vehicle condition, road conditions, traffic conditions, and other vehicles. And the data must be aggregated and analyzed in real-time, while the vehicle is in motion. This requires significant onboard computing — each autonomous vehicle becomes an “edge.” In addition, the data can help authorities and businesses manage vehicle fleets based on actual conditions on the ground.
· Retail: Retail businesses can also produce enormous data volumes from surveillance, stock tracking, sales data, and other real-time business details. Edge computing can help analyze this diverse data and identify business opportunities, such as an effective endcap or campaign, predict sales and optimize vendor ordering, and so on. Since retail businesses can vary dramatically in local environments, edge computing can be an effective solution for local processing at each store.
Benefits of Edge Computing
· Autonomy: Edge computing is useful where connectivity is unreliable or bandwidth is restricted because of the site’s environmental characteristics. Examples include oil rigs, ships at sea, remote farms, or other remote locations, such as a rainforest or desert. Edge computing does the compute work on-site sometimes on the edge device itself such as water quality sensors on water purifiers in remote villages and can save data to transmit to a central point only when connectivity is available. By processing data locally, the amount of data to be sent can be vastly reduced, requiring far less bandwidth or connectivity time than might otherwise be necessary.
· Data sovereignty: Moving huge amounts of data isn’t just a technical problem. Data’s journey across national and regional boundaries can pose additional problems for data security, privacy, and other legal issues. Edge computing can be used to keep data close to its source and within the bounds of prevailing data sovereignty laws, such as the European Union’s GDPR, which defines how data should be stored, processed, and exposed. This can allow raw data to be processed locally, obscuring or securing any sensitive data before sending anything to the cloud or primary data center, which can be in other jurisdictions.
· Edge security: Finally, edge computing offers an additional opportunity to implement and ensure data security. Although cloud providers have IoT services and specialize in complex analysis, enterprises remain concerned about the safety and security of data once it leaves the edge and travels back to the cloud or data center. By implementing computing at the edge, any data traversing the network back to the cloud or data center can be secured through encryption, and the edge deployment itself can be hardened against hackers and other malicious activities even when security on IoT devices remains limited.
Challenges of edge computing
· Limited capability: Edge computing deployment serves a specific purpose at a pre-determined scale using limited resources and few services.
· Connectivity: Edge computing overcomes typical network limitations, but even the most forgiving edge deployment will require some minimum level of connectivity.
· Security: IoT services from major cloud providers include secure communications, but this isn’t automatic when building an edge site from scratch.
· Data lifecycles: The perennial problem with today’s data glut is that so much of that data is unnecessary. A business must decide which data to keep and what to discard once analyses are performed. And the data that is retained must be protected in accordance with business and regulatory policies.
Edge computing has revolutionized the way innovative technology works, bringing in added speed to operations. This is the technology that IoT demands. As requirements and demands out of technology increase, the trend of using cloud computing along with edge computing will get pushed further. Combining cloud and edge computing for IoT implementation spells your betterment.