Cloud Computing vs. Edge Computing: What Is the Difference?

Public cloud computing platforms empower enterprises to enhance their private data centers by leveraging global servers. This enables the extension of infrastructure to any location and facilitates the flexible scaling of computational resources. Hybrid public-private clouds offer unmatched flexibility, value, and security for enterprise computing applications.

However, real-time AI applications distributed worldwide may demand substantial local processing power, particularly in remote locations distant from centralized cloud servers. Certain workloads, driven by low latency or data residency requirements, necessitate on-premises or location-specific deployment.

To address these challenges, many enterprises opt for edge computing, a paradigm focused on processing where data originates. Unlike traditional cloud processing, edge computing conducts operations locally on edge devices, storing data at the source. This approach eliminates dependence on internet connections, allowing devices to function as standalone network nodes.

The Cloud Computing

What Is Cloud Computing?

In essence, cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.

What Are the Benefits of Cloud Computing?

In contrast to conventional on-premises IT setups, and contingent upon the chosen cloud services, cloud computing facilitates the accomplishment of the following objectives:

Lower IT costs: Cloud computing eradicates the upfront costs associated with procuring hardware and software, as well as the establishment and operation of on-premises data centers. This includes server racks, continuous power consumption for both electricity and cooling, and the need for IT professionals to oversee infrastructure management. In addition, enterprises only need to pay for the computing resources used and can well control their operating costs.

Simplified IT management: With cloud infrastructure on demand, enterprises can deploy applications in a fraction of the time and get to market sooner. Cloud service providers grant clients access to proficient IT management professionals, enabling employees to concentrate on the fundamental requirements of their business operations.

Improved reliability: Cloud computing makes data backup, disaster recovery, and business continuity easier and more cost-effective, as data can be mirrored across multiple redundant sites on the cloud provider's network. Additionally, cloud computing allows you to automatically configure or deconfigure resources based on demand. This feature can reduce costs and enhance the overall efficiency of organizations.

The Edge Computing

What Is Edge Computing?

Edge computing is a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. Named for its approach of bringing computational power to the edge of the network or device, edge computing enables quicker data processing, enhanced bandwidth, and ensures data sovereignty.

Edge computing reduces the bandwidth and latency issues caused by sending data generated by all devices to a centralized data center or cloud, which is especially important for modern applications such as data science and artificial intelligence.

What Are the Benefits of Edge Computing?

Due to low latency or data residency requirements, certain workloads need to be kept on-premises or in specific locations. This is where the advantages of edge computing come into play.

Lower latency: Milliseconds make a difference for many applications. Data transfer takes time, but data processing at the edge eliminates or reduces data transfer. Edge computing can process data locally without requiring access to the Internet. This extends computing reach to previously inaccessible or remote locations.

Model accuracy: AI heavily depends on high-precision models, particularly for edge scenarios demanding real-time responsiveness. In cases where a network's bandwidth is insufficient, the typical solution involves reducing the input data size to a model. This leads to diminished image dimensions, skipped video frames, and decreased audio sample rates. When implemented at the edge, leveraging data feedback loops becomes possible to enhance the accuracy of AI models, enabling the concurrent operation of multiple models.

Data security: Data is analyzed locally, and edge computing allows organizations to keep all sensitive data and computation within the local area network and corporate firewall. This reduces the risk of cybersecurity attacks in the cloud and allows for better compliance with strict and ever-changing data laws.

The Difference Between Edge Computing and Cloud Computing

Containerized applications find utility in both edge and cloud computing environments. These easily deployable software packages enable running applications on various operating systems. They are designed to be abstracted from the host operating system, ensuring compatibility across diverse platforms and clouds.

The primary distinction between cloud and edge containers lies in their location. Edge containers are positioned at the network edge, in close proximity to the data source, while cloud containers operate within centralized data centers. Organizations that have already integrated containerized cloud solutions can seamlessly extend their deployment to the edge.

In many cases, organizations opt for cloud-native technology to oversee their edge AI data centers. This choice is driven by the distributed nature of edge AI servers, often spanning 10,000 locations without the presence of physical security or trained staff. As a result, ensuring the security, resilience, and efficient management at scale of edge AI servers becomes imperative, prompting the adoption of cloud-native solutions.

The Application about Edge Computing vs. Cloud Computing

Although cloud computing and edge computing have different capabilities, most organizations use both. The following are the differences between cloud computing and edge computing in deployment work.

Summary

Cloud and edge computing each have advantages and different capabilities, so for many organizations, the convergence of cloud and edge is a necessity. Organizations centralize when they can and distribute when they have to. A hybrid cloud architecture enables enterprises to capitalize on the security and ease of management provided by on-premises systems while also tapping into the resources offered by public cloud services.

The definition of a hybrid cloud solution varies among organizations. It could involve training in the cloud and deploying at the edge, conducting training in the data center with the use of cloud management tools at the edge, or even training at the edge while utilizing the cloud to centralize models for federated learning. The possibilities to unite the cloud and edge are extensive.