When cloud computing was first introduced, it completely transformed how computing resources were consumed. All the troubles and limitations of on-premises systems were suddenly overcome, and businesses got the chance of improving the availability, scalability, and flexibility of systems and resources.
But just as the rate of cloud adoption snowballed, organizations came to realize the latency and performance issues brought about by the cloud – especially in 5G and IoT use cases, where data has to be collected, processed, and shared in near-real-time. This led to the emergence of edge computing, which pushed processing capabilities to the extreme edge of computing networks – leading to substantial improvements in latency and bandwidth.
Why cloud computing alone is no longer enough
Cloud computing brings data from different sources to a centralized virtual server to be analyzed for different purposes. Cloud has revolutionized how data is handled and how it powers business decision-making. However, it usually brings with it the problem of latency, which can cost dearly to some organizations. In situations where data has to be processed in real-time, say for instance in autonomous vehicles or patient monitoring, such latency can prove to be very risky.
Concurrently, the centralized nature of cloud computing makes it difficult for organizations to process data quickly. Since all the data that is captured has to be sent to a centralized server for processing, near real-time analysis simply isn’t possible. The constant movement of data back and forth from devices and the central server makes it difficult for organizations to respond to changes faster and more effectively.
Although the cloud allows for large-scale data analysis, the need for data to be collected and analyzed in real-time is pressing. This is exactly why organizations need to move away from simple cloud computing and merge it with the capabilities of edge computing to create new and improved ways to maximize operational efficiency, safety, and performance.
Why the marriage of cloud and edge makes sense?
As a distributed computing model where data analysis takes place at or near the physical location where data is being collected, edge computing allows for real-time processing of data – instead of on a centralized cloud server or in the cloud – enabling decisions to be taken almost immediately. By cutting down the time to process data collected from devices, it ensures the “always-on” availability of the system in question, creating immediate actionable intelligence that can be leveraged for optimum business results.
Let’s look at why it’s time to marry cloud and edge computing:
- Promotes an agile business ecosystem: With businesses constantly under the pressure to respond to a fluctuating market, customer, and regulatory requirements with increased agility, the marriage of cloud and edge computing paves the way for an agile business ecosystem. Since data can be processed in real-time, decisions can be taken quickly, allowing organizations to respond to changes immediately – while bringing the business up-to-speed with the latest trends and innovations.
- Accelerates digital transformation: Cloud and edge computing, when used together, is also a great way to accelerate digital transformation. Since real-time data analysis is a core pillar of modern transformation efforts, by making this substantive shift in how data is gathered, processed, and shared, organizations can create greater business value while also supercharging potential business outcomes via real-time visibility, improved responsiveness, and intelligent decision-making.
- Provides a mechanism for small-scale and large-scale analysis: Although edge computing makes it easy for data to be analyzed where it is generated, not all workloads can be (or need to be) analyzed at the edge. For bigger workloads, the cloud serves as the best foundation for large-scale analytics, while also providing a centralized location for edge data to be stored and managed. Together, they allow for real-time collection and analysis of critical data, as well as secure and robust storage of workloads for long-term analysis and performance management.
The delivery of computing capabilities at the logical extreme of a network via edge computing is a great way to improve the performance, operating cost, and reliability of modern applications and services. Edge bridges the distance between devices and the computing resources that power them, mitigates the latency and bandwidth constraints of today’s networks, and ushers in a new and quick way of data analysis. On the other hand, Cloud provides organizations with a robust mechanism for big data analysis and long-term storage and management of business data.
Organizations should not look at cloud and edge as an either/or proposition. Combining the two technologies in a single continuum can help overcome the latency problems plaguing businesses while enabling immediate and efficient data processing close to the source for real-time decision-making. Constant merging of the data-analysis potential of edge computing with the storage and processing capacity of the cloud can help you keep your business running efficiently and drive efforts towards improving performance and fostering innovation.