However, the Cloud model—which relies on processing and storage across massive data centers in remote locations—is beginning to be challenged by the natural evolution of technology. Why is this happening? With the accelerated expansion of the Internet of Things (IoT) framework and the need to optimize data management, there is a growing demand to bring computing power and analytical capabilities closer to IoT devices. This shift aims to eliminate the latency associated with remote data centers while gaining operational availability and real-time resolution capabilities. In short, there is a clear imperative to transition toward a distributed model where a portion of processing and storage occurs "at the edge."
Recent research revealed that 81% of organizations host at least one application or portion of their IT infrastructure in the Cloud. More than half (55%) of companies currently leverage multiple public clouds, with 21% reporting the use of three or more.
Another industry analysis confirms that Cloud computing is firmly established as the standard for enterprise IT; however, it also projected that leading Cloud service providers would establish an ATM-like distributed presence to deliver a subset of their services for low-latency application requirements. In fact, a report from the same consulting firm in late 2018 indicated that only 10% of enterprise-generated data was created and processed outside a traditional centralized data center or Cloud environment; it predicted that this figure would reach 75%.
Before evaluating and comparing the benefits delivered by each architecture—the centralized Cloud model versus distributed edge computing—it is useful to review the core characteristics of each, as defined in this article.
Models Under the Microscope
As noted, a Cloud computing strategy relies on centralized data centers to store, process, compute, and analyze vast datasets. This model delivers a wide array of web-based services, including networking, software, databases, servers, and storage. Because these data centers are typically located in remote regions, a latency gap exists between data collection and processing. While this delay is often imperceptible (measured in milliseconds), it can create bottlenecks in time-critical applications. Furthermore, transferring raw data from its point of origin to a central server for processing—and subsequently back to the end user—consumes significant bandwidth, incurs higher operational costs, and can slow response times.
Conversely, the edge computing model shifts computing power, networking, and a portion of storage directly to the data source (for example, IoT edge devices). This dramatically reduces data transit distance and latency, enables faster response times, and decreases the volume of data requiring transmission.
Advantages and Disadvantages
Based on these operational models, the advantages and disadvantages of Cloud and edge computing can be summarized as follows:
- Cloud computing, in its Infrastructure as a Service (IaaS) model, offers high availability, elasticity, scalability, mobility, workload resilience, migration flexibility, broad network access, disaster recovery, and pay-as-you-go pricing.
- Edge computing directly leverages smart devices, smartphones, and network gateways to deploy computing power close to the user. By reducing data volumes and network traffic, it delivers lower latency and reduces transmission costs. Additionally, it offers faster response times and optimizes real-time analytics.
The overarching conclusion is that both architectures can coexist and, in fact, complement each other effectively, as they fulfill distinct operational roles. Edge computing is ideal for applications where every millisecond counts, whereas Cloud computing architecture excels in non-time-sensitive enterprise applications.
In this article, you will find a more detailed analysis.
Are you considering designing a hybrid cloud and edge computing framework for your enterprise?