Edge computing is at the forefront of innovations driven by the digitization of industries and complimented by the series on investments. The deployment of 5G, acceptance of IoT, and automation are knocking the industry growth. How far will edge computing go? Our study shall provide few insights to answer them.
Edge computing is a distributed IT infrastructure, which brings enterprise applications closer to the data generating source and creates profuse curiosity, leading to discussions amongst industries, researchers, and academia. Edge computing shares the computational burden of cloud computing and supercomputers; it, however, does not compete with them. Keeping in view the recent trends, more energy-efficient, robust, and small embedded machines will be the future of computations.
The article provides an insight into the industry trends and the role of technologies such as IoT, 5G, and AI across the edge computing ecosystem. The end-use industries are moving towards digitization, resulting in enormous data generation, increasing the need for edge computing. With IoT, autonomous vehicles, and smart cities developing substantially, edge-enhanced applications are requisite. However, the industry participants are yet to conclude the impact of edge computing on businesses.
The article also highlights the recent developments in the edge computing industry. Essentially, investments are a pivotal strategic move adopted by most companies operating in the edge computing industry. Additionally, edge computing offers tremendous opportunities driven by the funds and investments from the industry giants to the new start-ups with disrupting technologies.
Edge computing has become a necessity, as the amount of machine data generated is incidental to exceed the network capacity. Advancements in technology have increased and the adoption of IoT devices are expected to expand further. Vast usage of these devices has led to voluminous data generation, which is difficult to process. This data is usually stored on the cloud, and due to the increased transmission latency, IoT users face performance issues.
The digitization of organizations has increased the need for digital business infrastructure. These organizations revealed that a decentralized approach is required to accommodate the requirements of digital businesses, and edge computing has the potential to do so.
The global edge computing industry stood at USD 4.7 Billion in 2020 and is expected to reach USD 42.8 Billion by 2027 with a growing CAGR of 36.7% during the forecast period. Edge computing is transitioning as the industry confronts opportunities and challenges due to the COVID-119 pandemic. The key industry leaders are increasingly deploying data centers in response to a massive shift to work-from-home that created new avenues for decentralized computing.
Currently, the world has different prospects for edge computing as to whether it will be profitable or not, and if yes, when? The answer is uncertain, leading the investors and end-users to focus on short-term costs over long-term scenarios. Besides, the market players in the cloud industry are eyeing ambitious on edge computing and expanding their footprint and product portfolio to gain early adopters. These companies have huge deployment capabilities with capital availability, and their existing relationships with the industry stakeholders are easing the process further.
The edge computing market is concentrated in North America (primarily the U.S.) and Europe and the trend would prevail for a few years. Europe is expected to hold a market share of 29.5% by 2027. The primary reason for the growth in Europe is its need for computing resources at the data source origin. Edge computing is increasingly used for fast, reliable, secure, and scalable products and services.
In addition, the market for IoT solutions across Europe is growing, and the growth is attributed to the sizeable adoption from European countries, such as the U.K., Germany, Spain, Italy, France, and the Netherlands. Moreover, the Eastern European countries and the Nordics closely follow the European countries that have established edge computing industries.
Edge computing, cloud computing, and fog computing are closely associated concepts. These concepts may overlap each other but are not the same and should not be used interchangeably. Edge and fog computing, however, are widely used interchangeably, even with the former being focused on things and the latter being focused on infrastructure. These three technologies are different layers of IIoT. The table and the figure below will help you understand the concept of edge, cloud, and fog computing.
Table 1: Comparison of Edge, Cloud, and Fog Computing
Sr. No. |
Parameters |
Edge Computing |
Cloud Computing |
Fog Computing |
1. |
Location of Data Processing |
Occurs directly on the device connected to sensors or a gateway device in the proximity of the sensor |
Central cloud server (usually located far from information source) |
Shifts edge computing tasks to processors that are directly connected to LAN or LAN hardware |
2. |
Processing, Power, and Storage |
Lower processing power as the processing is performed on edge devices |
Superior and advanced processing technological capabilities |
Limited processing power as the processing is performed on edge devices |
3. |
Purpose |
Suitable for the quick analysis required for real-time response |
Suitable for a long term, in-depth analysis of data |
Suitable for the quick analysis required for real-time response |
4. |
Connectivity |
Constant internet access is not required |
Constant internet access is required |
Constant internet access is not required |
5. |
Security |
More secured compared to cloud |
Less secured compared to edge and fog |
More secured compared to cloud |
Source: Straits Research Analysis, Secondary Sources, Government Publications - Download Request Sample
Figure 1: Share of Edge (2020): Industry 4.0
Source: Straits Research Analysis, Secondary Sources, Government Publications, Download Request Sample
Surplus investments are made by service, technology, and infrastructure providers, data centers, and Real Estate and Investment Trusts (REITs) to innovate and build the edge computing industry. Nearly three-quarters of the organizations would invest in Artificial Intelligence (AI) in three years to create new models at the edge in combination with automation, edge device interconnectivity, and intelligent workflows and generate a positive ROI. A few recent investments made by the industry participants are:
Table 2: Investment/ Funding in Edge Computing by Companies
Sr. No. |
Company |
Year |
Investment/ Funding (USD) |
1. |
Macrometa |
2021 |
20 Mn (Funding) |
2. |
Netradyne |
2020 |
150 Mn (Funding) |
3. |
DataBank |
2020 |
30 Mn (Investment) |
4. |
Vapor IO |
2020 |
90 Mn (Funding) |
5. |
FogHorn |
2020 |
20 Mn (Funding) |
6. |
Litmus Automation |
2019 |
7 Mn (Funding) |
Source: Straits Research Analysis, Secondary Sources, Government Publications, Download Request Sample
Various industries are exploring edge computing across multiple use cases, and it is expected to turn edge computing into a core element for decentralized architecture over a couple of years. The manufacturing industry is amidst a transformational period due to rising data-centric and interconnected factories and IoT. Manufacturers will need to integrate edge computing for Industry 4.0 to attain its full potential.
Industry 4.0 mandates connecting machines for the company’s manufacturing processes to respond to changing factory floor conditions swiftly and intelligently. Connecting assets increase agility and automation. These are, however, more dangerous. A well-connected organization has a large number of attack surfaces and is more vulnerable to cyber-attacks. These organizations require edge computing to minimize the risk while implementing their Industry 4.0 strategy. In addition, edge computing provides many benefits such as manageable data analytics, ultra-low latency, reduced storage costs, and improved interoperability.
Figure 2: Share of Edge (2020): Industry 4.0
Source: Straits Research Analysis, Secondary Sources, Government Publications
A Reference Architecture (RA) proposes structure, products, and services to deliver a solution and incorporates industry-accepted practices to suggest specific optimal technologies. Edge computing RA designs are based on ISO/IEC/IEEE 42010:2011 standards and are FAR-Edge RA, Edge Computing Consortium RA 2.0, and Industrial Internet Consortium RA. These architectures provide a blueprint for industrial automation based on edge computing. The table below shows the characteristics of each architecture.
Table 2: Main Features of Edge Computing Reference Architectures
Sr. No. |
Characteristics |
FAR-Edge |
ECC |
IIC |
1. |
Connectivity and Communication |
✔ |
✔ |
✔ |
2. |
Device Management |
✔ |
✔ |
✔ |
3. |
Data Collection, Analysis, and Performance |
✔ |
✔ |
✔ |
4. |
Scalability |
✔ |
✔ |
✔ |
5. |
Standards |
|
✔ |
✔ |
6. |
Security |
✔ |
✔ |
✔ |
7. |
Data Encryption |
|
|
|
8. |
Blockchain |
✔ |
|
|
Source: Straits Research Analysis, Secondary Sources, Government Publications, Download Request Sample
Advanced technology innovations of smart bots and connected vehicles have generated enormous data, which is still unexplored. These technologies, along with edge computing, are creating tremendous growth opportunities across multiple industries. It is predicted that over 50% of the enterprise data will be processed on edge by 2027 as opposed to 10% of data processed as of 2020.
5G: The deployment of 5G is expected to assist edge computing phenomenally, as predicted by tech enthusiasts. Mobile edge computing (MEC) is the main ingredient for 5G networks. The increasing reliance on mobile devices for data computation and storage—personal or business-related, required offloading to ensure better performance with extended battery life.
These objectives are achievable by bringing the cloud closer to the locus of data generation and the users. The mobile operators are working on mobile edge computing (MEC) to integrate computing, storage, and networking with the base station. Open RAN architecture is the first use of the 5G mobile network, which evolved the massive radio network towards an open, virtualized, intelligent, and interoperable RAN.
Internet-of-Things (IoT) and Industrial Internet-of-Things (IIoT): IoT is accelerating the need for decentralized data processing in response to the rising investments by organizations in IoT to differentiate services, create new business models, and enhance digital capabilities.
IIoT is the heart of Industry 4.0, where smart sensors, electronics, RFIDs, and software are infused within industrial machinery to collect and monitor real-time performance and is fueled by advancements in edge computing technology. Among the twelve breakthrough technologies of Industry 4.0, IIoT is the most prominent one.
The USPs of the Industrial IoT, such as enhancement of IT security, automating maintenance procedures, and driving automation, are primarily offered by edge computing. Advancements in IoT and IIoT over the years are expected to benefit from edge computing technology.
OTT and Online Video: Over-the-top (OTT) delivery and online video are dependent on low latency infrastructure. OTT streaming has changed the ways of consumers’ content consumption, which highly depends on seamless experience. Further, the COVID-19 pandemic has escalated the demand for video conferencing as the work-from-home business models developed.
Between late 2019–2020, video conferencing solution providers witnessed tremendous growth of monthly active users. Zoom, a video teleconferencing software program, experienced a massive hike in its participants from 10 million in late 2019 to 300 million in late 2020. Low-latency video streaming and responsive applications are required to ensure a seamless experience, elevating edge computing in importance.
Edge computing is far from infallible, despite its potential to provide significant benefits across multiple industrial use cases. Data lifecycles, connectivity, limited capability, and security are some challenges encountered while adopting edge computing. The prevailing edge hardware is less competent than other data processing technologies, making it more vital. Edge has to be positioned strategically for specific functions, and identifying those functions is a big challenge.
The availability of different protocols, new versus legacy hardware, among others, enable edge computing to act as a shield. However, the proliferation of incompatible edge computing applications and platforms in the market threatens the growth of the edge computing industry. Another factor to be considered is the critical and arduous management of edge systems due to their large size and highly distributed nature.
In addition, there are no existing industry standards to define ethical, social, and legal aspects of using edge intelligence. Existing benchmarking practices and tools are also not yet mature and require significant research, which challenges the growth of the edge computing ecosystem.
Integration of edge computing across the industrial sector requires edge AI chips, which will provide enormous growth potential for the semiconductor industry. 1.5 billion edge AI chips are expected to be sold by 2024, representing 20% annual unit sales growth, which is more than double the overall growth of the semiconductor industry.
In addition, the automotive sector has observed massive transformations with the advancement of technologies, such as advanced driver-assist systems (ADAS), connected cars, intuitive infotainments, and predictive and adaptive vehicle maintenance. The connected vehicles are evolving significantly with V2V and V2X communications, generating a large volume of data. Automobile makers are focused on leveraging edge computing to address these ever-evolving challenges.
Figure 3: Industries Projecting Increased Operational Responsiveness from Edge Computing (between 2019–2024)
Source: Straits Research Analysis, Secondary Sources, Government Publications, Download Request Sample
Figure 4: Edge Computing Footprint by Industries, 2027
Source: Straits Research Analysis, Secondary Sources, Government Publications, Download Request Sample
Essentially, edge computing was noticed due to the rise in IoT technology, which is still budding, is expected to significantly impact the future development of edge computing- micro modular data centers (MMDs). MMDC is a mobile system that can be deployed closer to the source of data.
In addition to the development of compact devices, the creation of software, allowing companies to access remotely and monitor edge devices, is also crucial. A few companies, such as Cloudera, Red Hat, and Nutanix, are among the few that have developed their own edge technology to resolve challenges that are expected to be imposed by the edge computing ecosystem. The utilization of edge computing in software development is considerable, so speaking with some top-tier developers, such, for instance, top software companies in Dallas, would be a wonderful idea.
Moreover, the edge computing industry is forecasted to have the largest global footprints across the Asia-Pacific, followed by other regions, majorly Europe and North America. Across Europe, Germany is expected to be the biggest market for edge computing by 2027. Besides, the U.S. will hold its dominant position among the other countries throughout the forecast period. The edge devices and the edge infrastructure will grow at a high rate, supporting the commercial and technical demands from the end-users.
Further, the 5G rollout and hyperscalers will push hybrid models such as edge cloud for telecom companies and service partners, taking advantage of mobile edge computing. The digitization across the end-use industries will mature by 2027, along with the large-scale penetration of connected devices. The data-driven environment of industries, such as healthcare, transportation and logistics, retail, energy, and the government, will move them towards the edge to gain operational efficiencies— a key parameter for their business growth. Edge computing has a bright future.