Centralized servers were established as the be all and end all of computing until it started to change as this form of computing is costly, hard to scale and resource intensive. Cloud computing emerged as the most flexible model of computing that could cater to a wider range of networking requirements. But computing needs have grown to the peripherals of the network with devices called as edge devices.
Edge devices are those devices that are on the edge of a network, either close to the end user or just simply at the peripherals of an entire network in an unmanned state. Such devices don’t perform as best as those placed closer to the central servers. Because of the lack of adequate storage, network capacity, and other supporting infrastructure, cloud computing was also a no-go for these edge devices.
This is mainly because the devices are usually sharing resources among multiple distributed nodes. These nodes have other users and content consumers placed close to each other, leading to a ‘noisy neighbour’ effect.
In large industrial IoT environments with many thousands of distributed nodes, the ‘noisy neighbour’ effect is extremely challenging and problematic. Industrial devices are also deployed far and wide in a way that makes it impossible for them to depend on a single cloud server. Even for retail stores and other business, the latency issues of edge devices play into making them ineffective for data analytics and fast communication.
With the advent of smart cities, fast trains, AR/VR, etc., the demand for fast connections and unlimited data consumption increased. Considering also a stark increase in content consumption from streaming services, intermittent and unreliable infrastructure leads to destructive experiences for digitized consumer services.
Since edge devices require high bandwidth, low latency and high compute power, it makes them difficult to be accommodated on central cloud servers.
Alternative computing methods that suit modern network consumption requirements is therefore a necessity in today’s age. They should provide the capability to perform computations and process information at high speeds for devices on edge networks. However, it is difficult to accomplish due to a few limitations. Let’s look at some of the limitations and how they can be resolved through the use of alternative computing.
Edge Device Computing Limitations
For any cloud computing system, the data gets stored in the cloud or the local server. But the cost of cloud storage can increase significantly considering the large amount of data generated by IoT devices. Besides, the data has to be stored in the cloud using the available bandwidth.
Processing data from the cloud means that the data exchange happens with a response time that’s way too long since the cloud data centres are located too far away. According to Gartner forecasts, the total number of connected things will reach 25 billion by the end of 2021, producing immense volume of data. Later researches predict an even higher number which can reach up to 46 billion devices. The quantity of data is too large and requires a lot of storage and demands extra bandwidth that is usually unavailable. If there are numerous devices that are trying to reach the cloud storage at the same time, it can limit the bandwidth shared among these devices. In such cases, the data needs to be processed at the edge for shorter response time, more efficient processing and smaller network pressure.
Besides the problem of storage and bandwidth, edge devices create many practical difficulties during implementation:
- Real-time data availability: The very purpose of edge devices is mostly real-time data retrieval for the main systems. But with the enormously slowed down processing speeds and increased latency, this never happens.
- Data loss: Intermittent connectivity leads to loss of data during transmission, rendering edge devices unreliable.
- Cost: Edge device set-ups themselves cost a lot, but without real-time data availability this cannot translate to adequate ROI or profits in the long term.
- Naming: The lack of standardized naming conventions for edge devices causes a lot of confusion for IoT practitioners. Different network and communication protocols may be required for different types of devices. So, learning all of them can be a struggle for them. DNS and URIs are not sufficient for various edge networks as they may be for central systems. The naming mechanism that they choose must conform with the computing needs, the nature of mobility, dynamicity of network topology, privacy and security protocols, etc. Moreover, they need to consider the scale of each communication across things.
- Connection scheduling: Scheduling tasks for each edge node can be challenging. The scheduling needs to be made in order that the data processing and information handling does not affect the device’s performance in the end.
- Maintenance: Maintenance is costly and time consuming for edge devices because they are mostly located in remote areas or non-serviceable regions. They may even be fitted onto vehicles that traverse large geographical areas.
- Privacy: Privacy is a major concern for edge devices as most of the devices deal with sensitive information such as health information from wearables or personal information from workers or even location information. This information is privy to hacking in a cloud environment. Data ownerships need to be established early on when consumer data is being manipulated for required device functions and its broad usage.
To overcome these challenges with edge device computing, computations needed to be performed nearer to the edge devices without compromising on the various advantages that cloud could provide, giving rise to new edge computing technologies.
Edge Cloud Combines Cloud Capabilities with Edge Computing
When edge device computations are performed by combining the strengths of cloud technology and local storage and processing capacity of the devices through the use of gateways, it is known as cloud edge computing or more popularly, edge cloud. Such a confluence of technology ends the conflict that existed with edge computing vs cloud computing.
Edge computing architectures are becoming more and more capable of processing data on the edge through edge processing. The devices or their connected systems can utilize available storage and processing power to make computations and analytics.
However, this could mean huge losses in valuable data. That’s because all the data generated by an IoT device cannot be stored in the local storage and needs to be deleted from time to time. Since data is seen as the new fuel, this can be untapped potential.
Edge cloud can substitute as an additional resource in such a scenario to save valuable data. It is the new way to combine the cloud storage capacities with the data gathering potential of edge computing. Some data storage and a chunk of the processing could be accomplished in the cloud, while the most necessary processes are run at the edge. This mitigates the problem of unavailability of real-time data while also taking care of the broader data needs. Needless to say, the data thus collected on the cloud can be used for improved data analytics, data research and also drive innovation.
Edge connected applications are more responsive and robust. Network connectivity plays a huge role in accomplishing this through sliced networks and bandwidth management. The edge devices should be able to function isolated from the rest of the network. Such measures can also improve the edge device’s security and privacy.
Use Cases for Cloud Edge Computing
Edge computing use cases are expanding and it is being leveraged more and more as time goes. Yet, edge cloud has found its unique place in some business fields and has also become most notable in applicability in their edge locations. However, these are only a few examples in an expanding ecosystem.
The biggest challenges for the supply chain market have been the visibility of assets. As assets are transported across geographies, most of the time the asset managers lack visibility into their location.
Previously, supply chain businesses relied on man power (internal and external) as well other expensive methods to track and monitor assets from various locations. A dependable and continuous method of asset management was lacking. This led to loss of assets and damaged assets during transport, resulting in high losses to the businesses in supply chain.
Edge devices and edge cloud computing solve these problems by almost completely eliminating the need for any other external resources. Managing assets wherever they are is easy and cloud edge computing provided a means to receive information periodically from the devices, if and when needed. This system got rid of middle men and also served additional analytical insights for the business.
IoT and connected devices have played an important role in helping autonomous industries like manufacturing or production companies to power plants or mining operations. By nature, those are located in the outskirts of cities, making most communication technologies unreachable.
Asset management and spending are regulated through the IoT edge devices and there is a huge investment into making cloud edge computing work in industrial sites. Edge computing is an indispensable element in all autonomous industries such as manufacturing. The manufacturing industry is thus far leading among all the industries in IoT spending.
Devices that scan the premises, track asset production pipelines, detect abnormalities and keep track of assets throughout the delivery process are employed in the sites. This extends to logistics and transportation of goods also from site to site and to other locations.
Edge computing has disrupted the AR/VR markets to bring a uniform and quality experience to all its users. When AR/VR gaming started climbing in the gaming market, two things stood out as deterring factors- the cost and the discomfort of using the devices. The headsets were very costly and upwards $400, and it was mostly due to the processing happening in the device itself. The other issue was created by the heaviness of the headsets as the storage and processing was embedded into the device.
Edge cloud was the solution, for which network services and device manufacturers needed to join hands in making the devices less heavy on bandwidth consumption and processing needs. Image rendering would be allocated to the cloud and the storage was sorted out likewise. This translated to halving the processing capacity of the device as well as making them less battery intensive. The network services needed to slice their bandwidths to allow a seamless experience.
As edge cloud is being more and more adopted into the industry, the adoption rates for AR/VR could go up significantly.
Intelligent transport systems use smart technology like traffic management, navigation, automatic number plate recognition, incident recognition, parking guidance and information systems etc. These are IoT systems that are hoisted at the network peripherals making use of edge technology and edge cloud computing.
Transportation becomes especially challenging for a centralized server to handle as the vehicles such as fast trains are constantly on the move. This results in intermittent network connections, unreliable data exchange and more. With edge computing, the local data can be stored and processed locally, discarding unimportant data. All important and required data are stored in the cloud. This allows for data analytics without disrupting the normal tasks of an edge device.
Perhaps the most popular usage of edge devices is in healthcare systems, wearable devices and mobile apps, etc. Managing healthcare from the home and exchanging valuable health data with health practitioners have changed the way data influenced the medical industry. Storing EHR information in patient held health monitoring devices and sharing them through the cloud benefits patients as doctors have insight into patient health history, treatments, and further medical research.
Furthermore, it lightens the burden on doctors and other health practitioners in handling patient data and information. The data is neatly stored in the cloud systems. There is also a demand for edge cloud devices in real-time data exchange for critically ill patients and information availability during emergencies.
Financial institutions and retail stores
Mobile banking and easy money handling applications can rely on cloud edge to process important financial data. Such distributed systems can rely on cloud computing to make customers’ life easy.
Inside financial institutions, the edge devices could monitor and survey everything from people, surroundings, security measures, as well as all money transfers and transactions. The reliability and efficiency of edge computing for such important and critical needs have skyrocketed the demand for safe edge computing.
Retail stores are embracing competitive and digitally transformative technologies to keep them afloat in the digital age. Online shopping has bloomed and taken over much of the sales shares of the retail market. It is imperative that the stores provide a fresh experience to the users, whether for payments or for choosing the products they want, making the shopping experience easy and fulfilling.
Innominds’ cloud engineering services provide companies with a flexible and adaptive cloud computing experience. With years of experience in the IoT and connected devices field of services, we know the right tools to employ for your specific needs.
With numerous use case deliveries in edge cloud, across supply chain, medTech, retail, modern systems and more, Innominds does it all. Read the report by 451 Research where Innominds gets recognized as the leading innovators in smart assets for industries, whether greenfield or brownfield.
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