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Edge Computing vs. Cloud Computing: 10 Key Comparisons – Toolbox

Cloud and edge are the future of computing. Edge computing brings computers closer to the source of data to minimize response times. Conversely, cloud computing delivers cutting-edge computing technology over the internet for a fixed, recurring fee. This article highlights the key comparisons between these two computing platforms.

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Edge vs. Cloud Computing

Edge and cloud computing work together to add tremendous value to numerous industry verticals. By working together, edge and cloud computing can instantaneously help complete resource-intensive tasks such as large-scale artificial intelligence (AI) and machine learning (ML) operations. With 5G and other technological advancements gaining popularity in 2022, edge and cloud computing are both expected to see numerous new opportunities across industries.

Before we begin with the main comparisons between these two computing platforms, let’s look at their definitions.

Edge computing

Edge computing makes processing and storage of data instantaneous by bringing computing systems as close as possible to the device, application, or component that collects or generates data. 

Processing time is enhanced as all the data is processed at the edge, minimizing the need for communicating with a central processing system. This makes data processing more efficient and decreases internet bandwidth requirements, thus keeping operating costs low and enabling applications to be used in remote locations with limited connectivity. Gartner predicts that by 2025, 75% of enterprise data processing will take place at the edge.

Edge computing

Edge computing

By bringing the processing and storage of data closer to its source, edge computing enables better control over data, reduces costs, provides faster insights and actions, and enables more continuous and streamlined operations. Edge computing also helps bolster cybersecurity by reducing the need for interaction with public cloud platforms and networks. The simplest examples of edge computers include laptops, smartphones, and IoT sensors.

Edge computing is not a new concept. Its origins lie in content distribution networks developed in the late 1990s to serve video, and other web content from edge servers placed close to users. The early 2000s saw these networks evolve, hosting applications on edge servers to develop the earliest form of commercial edge computing. 

The hosted applications included shopping carts, dealer locators, ad insertion engines, and real-time data aggregators. Today, edge computing simplifies real-time data processing and minimizes latency for futuristic applications such as autonomous vehicles, the internet of things (IoT), voice assistants, and traffic management.

Traditional business applications see data-generating endpoints transmit information to a centralized enterprise application using either local area networks (LAN), wide area networks (WAN) such as the internet, or a combination of both. The centralized platform then processes the data, and the output is transmitted back to the endpoint. 

However, this arrangement is slowly becoming less viable as the number of devices connected to enterprise networks and the volume of data generated scale up tremendously. Continuing to use centralized processing networks could considerably strain local networks and the internet at large. Enterprises can leverage edge computing solutions to address slow response times due to congestion, thus enhancing the reliability of their big data processing systems.

Cloud computing

Cloud computing involves the use of hosted services, such as servers, data storage, networking, and software over the internet where the data is stored on physical servers maintained by a cloud service provider. 

Cloud computing has already revolutionized numerous industries by disrupting how enterprises think about IT resources. Thanks to cloud computing, businesses can access applications, storage, physical and virtual servers, networking capabilities, development tools, and other cutting-edge technology on-demand, over the internet, for a nominal fee. Cloud computing services are hosted in remote data centers managed by a third-party vendor or privately by an organization.

A broader definition of cloud computing encompasses the technology behind the cloud, including virtualized IT infrastructures such as operating systems, servers, and networks. This virtual technology uses special software to consolidate and securely divide computing power, regardless of the limitations posed by physical hardware. Virtualization in cloud computing allows cloud providers to optimize the usage of their infrastructure. For instance, a single hardware server can be split into multiple, distinct virtual servers that cater to different users.

Cloud computing has three main types: public, private, and hybrid. Public cloud platforms are owned and managed by third-party vendors who provide their services to multiple clients over the internet. The vendor pays for all hardware, software, and supporting infrastructures such as internet connection and electricity. After agreeing with the provider, clients can access the infrastructure, platform, or software hosted by the vendor using a web browser. Examples of public cloud include Microsoft Azure, Amazon Web Services (AWS), and Google Cloud.

Private cloud platforms are cloud computing resources exclusively accessed and used by a single organization. Enterprises adopt the cloud delivery methodology for their in-house applications to maximize cost savings and resource utilization. Compared to public cloud infrastructure, which is hosted remotely and managed by a third-party vendor, private cloud infrastructure can be located on-premise and is managed by the company that uses it. However, third-party service providers can also host private cloud platforms on behalf of organizations for a fee.

Finally, hybrid cloud platforms use technology to bring together public and private cloud platforms and enable them to share data and applications. This allows organizations to exercise more deployment options, thus enhancing flexibility and helping optimize compliance and cybersecurity.

Today, organizations across scale and industry leverage cloud computing for everything from email, virtual desktops, and data backup to disaster recovery, software development and testing, client-facing web applications, and big data analytics. For instance, finance companies rely on large-scale cloud solutions to detect and prevent fraud in real-time, while healthcare organizations are using cloud-powered solutions to tailor treatments according to the needs of individual patients. Cloud infrastructure offers end users faster service and enhanced convenience than traditional IT infrastructure.

See More: Why Kubernetes Is Vital for Moving Cloud Native Technologies To the Edge

10 Key Comparisons: Similarities and Differences Between Edge and Cloud Computing

Edge computing and cloud computing are two sides of the same coin; they help organizations enhance their data processing capabilities and reach their clients faster. This section outlines the key similarities and differences between the two.

Similarities between edge and cloud computing

Edge and cloud computing share similarities in use cases, automation & analytics capabilities, pricing models, and regulatory assistance.

Similarities Between Edge and Cloud Computing

Similarities Between Edge and Cloud Computing

1. Boast of futuristic use cases

This decade is expected to see numerous partnerships between edge and cloud computing service providers, with more vendors diversifying their offerings and providing both edge and cloud services. This is because use cases such as 5G and autonomous vehicles will need hybrid ecosystems that host data close to the endpoints (for real-time responsiveness) and in a central location (for massive-scale data analytics).

In its ‘Trend: Cloud Strategies Shift Towards the Edge’ report, Forrester predicts that the edge will turn into the next hybrid cloud target architecture as organizations look for opportunities to act on their customers’ behalf using voice, image and video at scale. Similarly, a report by IDC predicts that by 2024, one in every four organizations will expect use cases that integrate edge data with applications already hosted in the cloud.

The rate at which organizations create and process data in 2022 is higher than ever before, and this information is stored in locations across the world. Edge and cloud solutions will work together to address issues surrounding latency, responsiveness, security, analytics, management, and governance.

2. Make automation accessible

Automation is the future of operations, and cloud and edge are here to make it happen. Telecom companies, for instance, are already implementing network automation at the edge. Similarly, organizations with a use case for internet of things (IoT) solutions use autonomous robots and other machinery operating at the edge. Network automation at the edge makes autonomous edge devices more independent and less likely to fail. 

5G and software-defined networking (SDN) solutions enable network automation in the telecom domain, while the automatic deployment of applications, the rise of smart devices, and AI and ML inferencing make enterprise edge automation possible.

At the same time, cloud platforms are being used to automate enterprise tools and processes. This automation aims to reduce or altogether remove the dependency on manual efforts in the deployment and management of enterprise services and workloads. Enterprises are already applying cloud automation to enhance the efficiency and security of their systems.

The automation of edge and cloud computing helps improve the efficacy of enterprise workloads, especially when compared to the traditional deployment and operation of IT infrastructure. 

Traditional methods are manual, resource-intensive, and often entail repetitive tasks, including the sizing, configuration, and provisioning of virtual machines (VMs) and other resources; establishment and load balancing of VM clusters; creation of storage logical unit numbers (LUNs); invoking and deployment of virtual networks; and the monitoring and management of performance and availability.

3. Streamline analytics

Both edge and cloud networks are built for big data analytics. These computing solutions use numerous advanced analytical methodologies on large datasets (multiple terabytes or zettabytes of data) that are structured, semi-structured, or unstructured. The processing power of large-scale edge and cloud infrastructure makes it easy to analyze and gain insights from any dataset, no matter how huge or widespread it may be.

Cloud- and edge-powered big data analysis enables companies to plot market trends, predict buying patterns, and know their consumers. This knowledge is then used for targeted marketing and personalized advertising. Social media, gaming, and other service platforms use edge- and cloud-enabled big data analytics to study user behavioral patterns and glean meaningful insights to serve personalized content suggestions.

4. Provide flexible pricing models

Vendor-based cloud and edge computing models remove the capital expenditure associated with buying software and hardware, setting it up in on-premise data centers, and maintaining the servers and their need for motive power, internet connectivity, temperature control, and IT and security personnel.

Leading vendors such as Amazon, Dell, and Microsoft provide both edge computing and cloud computing services. The pricing models used by these providers feature a set fee paid at fixed intervals and depend on the usage and configuration of the computing solution. Also, edge and cloud solutions vendors only charge clients for the services they use, giving client companies access to advanced computing platforms without associated procurement, maintenance, and management costs.

Companies can also set up their edge and cloud infrastructure. However, it is usually cheaper and easier for them to outsource this requirement to a third-party vendor based on the quality of the components used and the scale of operations.

5. Enable hassle-free regulatory compliance

As regulators strive to understand more about the operations and benefits of edge and cloud computing systems, it is imperative for organizations that use these computing platforms to comply with all relevant regulations.

With vendors setting up cloud and edge computing centers in more and more major countries worldwide, efforts are being taken to ensure regulatory compliance within all jurisdictions in which these service providers operate.

Both computing platforms allow for data at rest and data in motion to be encrypted and processed within the mandated jurisdiction. Outsourcing edge and cloud computing requirements to well-known vendors who follow a shared responsibility model makes complying with local and global regulations a straightforward, hassle-free task. 

However, companies that set up their edge or cloud networks will find it easier to comply with data processing and storage regulations thanks to the advanced encryption and security features of edge and cloud computing solutions.

See More: What Is Cloud Migration? Definition, Process, Benefits and Trends

Key differences: Edge vs. cloud computing

While both edge and cloud computing solutions are agile, scalable, reliable, secure, and enhance productivity and performance, some vital differences exist between the two computing platforms.

Difference Between Cloud Computing and Edge Computing

Difference Between Cloud Computing and Edge Computing

1. Speed & Agility
Edge Computing Cloud Computing
Edge solutions bring their analytical and computational powers as close to the data source as possible.

This increases responsiveness and boosts the throughput of the applications hosted on edge computers. In fact, for certain applications, a well-designed and sufficiently capable edge platform would be able to outperform cloud-based systems.

Edge computing is far more ideal than cloud platforms for applications that require minimal response times for safe and efficient operations.

Machines can leverage edge computing to mimic the perception speed of a human being, which is immensely helpful for applications such as augmented reality (AR) and autonomous vehicles.

While traditional cloud computing setups are unlikely to match the speed of an expertly configured edge computing network, cloud computers have their way of exuding agility.

Cloud computing services are generally on-demand for starters and can be accessed through self-service. This means that even vast volumes of computing resources are just a few clicks away and can be deployed by an organization in a matter of minutes.

Secondly, cloud platforms give enterprises easy access to various technologies, allowing for agile innovation and the speedy creation of new applications. Any enterprise can access cutting-edge infrastructure services, incredible computing power, and near-unlimited storage at a moment’s notice.

The cloud gives organizations the freedom to test new ideas, experiment with data, and differentiate user experiences.

2. Scalability
Edge Computing Cloud Computing
In an edge computing ecosystem, scalability must account for device heterogeneity. This is because different devices come with varying performance levels and energy considerations.

Additionally, edge networks operate in more dynamic conditions when compared to cloud computers. This means that an edge network would require reliable infrastructure to ensure robust connections for timely scaling of resources.

Finally, security measures on the network can introduce latency in node-to-node communication, decelerating scaling operations.

Scalability is one of the key benefits of cloud computing services. Organizations can easily scale up data storage, network, and processing capabilities using an existing cloud computing subscription or in-house infrastructure.

Scaling is typically quick and easy and brings with it zero downtime or disruption. Especially in the case of third-party cloud services, all the infrastructure is in place, and scaling up is as simple as a few extra authorizations by the client.

3. Productivity & Performance
Edge Computing Cloud Computing
In an edge network, computing resources are placed in close physical proximity to end-users. This means that client data is processed using analytical tools and AI-powered solutions within a few milliseconds.

As such, operational efficiency—one of the critical advantages of this system—is enhanced. This leads to heightened productivity and performance for clients with the proper use case.

Cloud computing removes the need for ‘racking and stacking’, such as setting up hardware and patching software associated with on-site datacenters. This enhances the productivity of IT personnel, allowing them to focus on higher-value tasks.

Cloud computing vendors also improve organizational performance, boost economies of scale, and minimize network latency for their clients by regularly adopting the latest computing hardware and software.

Finally, organizations do not have to worry about over-provisioning or falling short of resources due to fluctuating demand levels. By always ensuring the perfect amount of resources, cloud platforms help ensure near-perfect productivity and performance.

4. Reliability
Edge Computing Cloud Computing
Failover management is crucial for edge computing services.

In a correctly configured edge network, losing a few nodes does not prevent users from accessing a service at total efficiency. Edge computing vendors also implement redundant infrastructure to ensure recovery from failures and impeccable business continuity.

Further, systems can be implemented to alert users in case of component failure, thus allowing IT personnel to respond rapidly. However, an edge computing network is inherently less reliable than a cloud platform due to its decentralized nature.

Finally, a key advantage of edge computing is its ability to operate without access to the internet. This is because edge computers often rely on LAN connectivity to transmit and process information and only use the internet for transferring data to the cloud for storage and analytics.

Cloud computing is often more reliable than edge computing.

Due to its centralized nature, data backup, business continuity, and disaster recovery are easier and less expensive in the case of cloud computing.

Copies of critical data are stored in multiple sites accessed automatically if the closest location is inaccessible. Large cloud platforms often can continue operations without a hitch, even if an entire data center goes down.

However, cloud computing needs a strong internet connection on both the server-side and the client-side to operate reliably. Without internet connectivity, the cloud server cannot communicate with connected endpoints, thus bringing operations to a halt unless continuity measures exist.

5. Security
Edge Computing Cloud Computing
The distributed nature of edge computing systems has led to a shift in the cybersecurity paradigm typically associated with cloud computing. This is because edge computers can transmit data directly between nodes without first communicating with the cloud.

Such an arrangement calls for cloud-independent encryption mechanisms that operate on even the most resource-constrained edge devices. However, this might negatively affect the cybersecurity posture of edge computers vis-à-vis cloud networks. As rightly said, a chain is only as strong as its weakest link.

However, by restricting the transmission of sensitive data to the cloud, edge computing enhances privacy as data is less likely to be intercepted while in motion.

Cloud computing platforms are inherently more secure due to vendors’ and organizations’ centralized implementation of cutting-edge cybersecurity measures.

Cloud providers often implement advanced technologies, policies, and controls that enhance their general cybersecurity posture.

Protecting data is also easier in the case of cloud platforms due to the widespread adoption of end-to-end encryption protocols.

Finally, cybersecurity experts implement measures that safeguard cloud-based infrastructure and applications from potential threats and guide client companies on doing the same.

See More: What Is Horizontal Cloud Scaling? Definition, Process, and Best Practices


It is critical to understand that edge computing and cloud computing are two different technologies that cannot be substituted for one another. The main difference is responsiveness: edge computing is ideal for processing data in real-time, while cloud computing is better suited for processing voluminous information that is not time-sensitive.

These computing platforms have individual and joint applications in a wide variety of futuristic scenarios. Together, edge and cloud computing will help shape a new computing paradigm for enterprises across industries.

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This UrIoTNews article is syndicated fromGoogle News

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