Edge Computing vs Cloud Computing: Meaning, Types, Examples & Differences

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edge computing vs cloud computing | iTMunch

Edge computing vs cloud computing – An increasing number of organizations are debating these options. Most people are familiar with the cloud but edge computing is a relatively new concept. The question is, is there a need for a technology like edge computing when cloud computing exists? Should you invest the time to learn more? 

Small, medium and large scale organizations are realizing the power of cloud computing day after day. More than 70% of companies around the world have at least one app on the cloud [1] and more than 28% of a company’s total Information Technology (IT) budget is kept aside especially for cloud computing [2]. In 2018, the global cloud computing market size was valued at $272 billion and by 2023, the market is predicted to grow to $623 billion [3].

These numbers indicate that cloud computing is on its boom and being accepted across the world. However, a new technology called ‘edge computing’ is being deployed by organizations and has a lot of benefits over cloud computing, but what exactly is edge computing? Is it a type of cloud computing or a different technology altogether? If it is different from cloud computing, is it a better alternative and what are the differentiating factors? If you are looking for answers to such questions, we have tried to explain them in this blog. Let us begin by understanding what edge computing is.

What is Edge Computing?

To understand what edge computing exactly is, it is imperative to take the growth of Internet of Things (IoT) and IoT devices under consideration. IoT devices employed in any organization’s infrastructure generate vast amounts of data on the outer edge of computing networks. This data produced is sent to the central network server which is hosted in a data centre and once processed, it is further sent back to the IoT devices out on the edge of the network. Now, there are two issues with this arrangement. One, it takes time. Though it is a matter of milliseconds, it can be extremely critical. Second, the high volumes of data travelling back and forth puts an enormous amount of strain on bandwidth. Additionally, this blend of high traffic volume and distance slows down the network causing network latency which can have serious repercussions on the expensive IoT devices.

Edge computing bridges this gap successfully by relocating all the important data processing to a ‘network edge’. It revolves around bringing data storage and compute power closer to the data sources or IoT devices where it is needed. In this technology, data is not processed on the cloud and filtered via remote data centres. Instead, it is processed closer to the data source, which is called the ‘edge’ of the network. It basically reduces lag-time significantly and saves a considerable amount of bandwidth. The technology can help in lowering dependence on the cloud and enhance data processing speed as an outcome. 

With the increase in IoT adoption, a growth in the edge computing market has also been seen. In 2019, the global edge computing industry was valued at $3.5 billion [4] and by 2027, the size of the same is expected to grow to $43.4 billion at a CAGR of 37.4% [5]. Additionally, survey data suggests that in the next 3 years, enterprises will spend about 30% of their IT budgets on edge computing [6].

SEE ALSO: What is IoT & how does it work? Internet of Things Explained

Types of edge computing & how can it be deployed

There are two types of edge computing: Cloud Edge and Device Edge. Cloud edge is the type of edge computing in which public cloud is extended to a series of PoP (Point-of-Presence) locations. It is basically an extended form of the conventional cloud that sees the cloud provider responsible for the maintenance and working of the complete model. Device edge is the kind of edge computing when a software essentially exists and runs primarily on the existing hardware which makes it possible to process data in real time accurately and quickly.

Edge computing can be deployed in 3 main ways:

Fog Computing

In this deployment model, all the information is evenly distributed between the devices and a centralized computing infrastructure. 

Mobile edge computing

Also referred to as MEC, mobile edge computing is an architecture that brings the computational and storage capabilities of the cloud closer to the mobile network of the end user.

Cloudlet Computing

This form of edge computing offers an infrastructure that uses smaller data centres for the purpose of offloading data which brings the cloud closer to the end-user.

Now that we have been acquainted with the technology, let us take a deeper look into it by trying to understand its benefits.

Benefits of edge computing

Following are the benefits associated with employing edge computing technology in a company’s infrastructure:

1. Latency and speed

Analyzing, processing and performing the necessary processes on data locally and then transmitting these enormous quantities of data is challenging, especially when being conducted from distant industrial areas. It also takes time, though it is a matter of milliseconds to complete, the difference of even one millisecond is crucial in certain scenarios. 

Situations of hazardous accidents and equipment failures need instantaneous and real-time analysis of data. In such cases, there is not much time for the data to go back and forth to the cloud. This issue can be fixed by adding intelligence to the machinery and devices that are located at the edge of the network. It reduces the load of the overall traffic of the enterprise at scale which in turn enhances performance of the entire organization’s applications and equipment. 

2. Reduced costs

Not all data is the same and not every information produced beholds the same value. While some data is imperative to the operations of your organization, the rest is non-essential. With edge computing, spending the same amount of money on transporting, securing and managing the expendable data can be avoided. The technology allows you to categorize and prioritize data and ultimately retain the information that’s important for you within your edge locations. Doing this reduces your costly bandwidth requirements significantly.

3. Security

When all of your data is in only one place, i.e. the cloud, your entire business and its operations dependent on this data are vulnerable. Even a single Distributed Denial of Service (DDoS) attack can hamper a multinational company’s operations. Applying edge computing, you distribute the data and the data analysis tools across your company. As a result, you distribute the risk as well. Yes, it can be argued that by employing edge computing you are increasing the number of possible attack surfaces. However, by doing so you decrease the impact of the attack on the company as a whole.

As data is analyzed locally in edge computing, the data remains protected by the security layer of the on-premise enterprise. It also helps companies deal with the local compliance, privacy regulations and data sovereignty issues.

Examples of Edge Computing

The best way to understand how this emerging technology works is through examples. Here are a few edge computing examples where the technology is found to be most useful.

Streaming services

streaming service example - Netflix | iTMunch

Streaming services like Netflix, Disney+, Hulu and Amazon Prime are the future of entertainment. Nearly three quarters of American households subscribe to at least 1 streaming service [7] and an average user in the US subscribes to 3.4 streaming services of all that’s available out there [8]. The demand for streaming services has only increased in the past couple of years, especially since the pandemic. Because of the increasing demand, latency is a demand faced by all content streaming services.

According to research, viewer engagement (in terms of play duration) increases as latency declines [9]. Edge computing enables these OTT platforms improve network performance by placing high-demand, popular content closer to the end consumers in edge data centres.

Smart homes 

Just like streaming services, smart homes are gaining popularity. More people are using smart devices in their homes and this puts a lot of network load on the traditional cloud computing technologies which in turn causes latency. Processing and performing necessary action on the information locally would mean less latency and rapid responses in emergency situations. 

Autonomous vehicles

Automobile companies General Motors spent millions of dollars, $581 million precisely, in 2016 to acquire a self driving startup Cruise Automation to come up with an autonomous prototype. Moreover, the value of the global autonomous vehicle industry is expected to grow to $590 billion by 2026 [10]. AI-powered or self-driven cars run smoothly when several groundbreaking technologies come together in play to produce, accumulate and process a mammoth of data.

An autonomous vehicle can’t make it even half a mile from the garage if it faces bandwidth or latency problems. The framework of edge computing enables the AI-driven vehicles to process the massive data generated locally while transmitting the critical information about the condition of the roads and nearby vehicles. 

After understanding what edge computing is, how it works and what its benefits are, it is time to look into cloud computing.

What is Cloud Computing?

cloud computing data centre | iTMunch

In a cloud computing framework, data is collected and processed in one centralized location, which is a data centre. All the devices and authorities that want to access this data or use the applications associated with it need to be connected to the cloud. As everything in the cloud computing architecture is centralized, the cloud environment is easy to control and secure while allowing remote access. 

Because of cloud computing infrastructure’s centralized nature, it cannot gather and analyze data as efficiently and quickly as an edge computing architecture. Though the cloud lags behind cloud computing in speed, it makes up for it in terms of capacity and power. As cloud computing is based on a data center infrastructure that is scalable, its storage and processing capacity can be expanded as and when needed. This is a great benefit for small businesses that are looking for options to expand rapidly.

Cloud computing and its service models

Depending upon specific requirements, cloud computing services can be provided in terms of the following service models:

Platform-as-a-Service (PaaS)

Platform-as-a-Service Amazon Web Services (AWS) | iTMunch

Also known as cloud platform services, PaaS provides a framework for developers that can be used to build customized applications and software. All storage, servers and networks are managed by a third-party provider while the developers just have to maintain management of the applications. In other words, it allows the user to buy access to platforms and enables them to deploy their applications/software on the cloud. 

This type of cloud computing might possess constraints on the software’s nature. PaaS cloud computing examples are Microsoft Azure, Amazon Web Services, Rackspace, DigitalOcean, Cisco Metapod and Google Compute Engine.

Software-as-a-Service (SaaS)

By purchasing SaaS, also known as cloud application services, users get access to an application or a software that is hosted on the cloud. SaaS uses the power of the internet to deliver software and applications, that are managed by third-party vendors, to its users. SaaS software and apps can be accessed directly on web browsers and do not require any installations or downloads on the client’s end. SaaS cloud computing examples are Dropbox, Salesforce, Google Workspace, Cisco, WebEx and GoToMeeting.

Infrastructure-as-a-Service (IaaS)

Commonly known as cloud infrastructure services, IaaS is essentially a self-service for monitoring and assessing computers, storage, network and other devices. It allows users to manage and control operating systems, network connectivity, applications and storage without controlling the cloud. A few IaaS examples are AWS Elastic Beanstalk, Google App Engine, OpenShift and Windows Azure.

When an SME is associated with a cloud computing vendor, it means the vendor provides them access to cloud storage and certain software platforms through the internet. To access these services, an organization pays a cloud computing vendor on an on-demand or pay-as-you-go basis and the backend of the cloud platform or application is managed by the vendor itself. 

SEE ALSO: The Difference Between AI & IoT

Deployment models of cloud computing

Cloud deployment models can be categorized on the basis of size, access and proprietorship. it additionally describes the purpose and the nature of the cloud. There are 4 cloud deployment models: Community Cloud, Public Cloud, Private Cloud and Hybrid Cloud.

Community Cloud

A community cloud enables organizations with similar requirements and interests to share the cloud. Consequently, this reduces capital expenditure costs as it is distributed among the organizations using it. 

Public Cloud

Public clouds are owned by cloud services providers and can be used by the public. This type of cloud hosting allows access of services and systems to its clients on a commercial basis. Google, IBM, Microsoft and Amazon offer public cloud services.

Private Cloud

From a technical perspective, the architecture of a private and public cloud is similar. The difference between the two is that a public cloud can be used by the general public while a private cloud is owned by one specific company. In private clouds, the system and services can be accessed only within the set boundaries or an organization. It is also called a corporate cloud or an internal cloud. 

Hybrid Cloud

Hybrid cloud is an integrated type of cloud computing that is the amalgamation of two or more types of cloud servers. It can be a different permutation and combination of public, private or community clouds. The architecture of two or more types of clouds is combined while retaining the benefits of the individual cloud entities. 

Benefits of using cloud computing

1. Scalability

Cloud technology enables organizations to begin with small cloud deployment models and expand as per their growth pace. It allows organizations to add extra services and resources when needed. Scaling backwards is also possible if the situation demands so. 

2. Flexibility

In organizations, giving cloud access to employees gives them more flexibility in terms of work practices. When on holiday or working remotely, they can access important data from home. All they need is an internet connection and they can connect to the virtual office easily.

3. Cost saving in terms of expansion

Though cloud computing in itself is an expensive deal, opting for a pay-as-you-go model via a cloud computing vendor is cost effective. By deploying cloud computing, organizations can reduce operational and capital expenditures considerably, especially when they’re planning to expand their computing capabilities.

SEE ALSO: Cloud Computing- A Fuss or A Force to Be Reckoned with?

Difference between edge computing and cloud computing

Before we get into dissecting and comparing edge computing vs cloud computing, it is imperative to understand that edge computing is not the end of cloud computing now it is a total substitute for cloud computing. Both serve a different purpose and have different uses. Now let’s dive in and understand the key differences between cloud and edge computing.

Point of Difference: Suitable organizations

Cloud Computing: Ideal for organizations and projects that have enormous data storage requirements.

Edge Computing: Suitable for projects, companies and industries whose primary concern is latency. Medium-scale businesses with budget limitations can use edge computing to save capital.

Point of Difference: Security

Cloud Computing: Requires a comparatively less of a vigorous plan for security.

Edge Computing: Requires a robust plan of security which is inclusive of advanced authentication methods and proactively tackles cyber breaches.

Point of Difference: Programming

Cloud Computing: Usually made for one target platform and uses only one programming language.

Edge Computing: Various platforms can be used for programming and all might have different run-times.

Is edge computing needed when cloud computing exists?

Hundreds of thousands of companies are moving towards edge computing. However, edge computing is not a replacement for cloud computing nor it is the only solution. For organizations and IT vendors that face computing challenges, cloud computing continues to be a viable solution. Some companies also use it in tandem with edge computing for an all-inclusive solution. 

Edge computing vs cloud computing isn’t really an either/or situation. As the Internet of Things technology and devices are becoming more powerful and widespread, organizations need to apply efficient edge computing architectures to harness the potential of this groundbreaking technology. By integrating edge with cloud centralized cloud computing (fog computing), organizations can leverage the potential of both technologies while minimizing their limitations. 

Merging the data-collecting capacity of edge computing with the impeccable storage and processing power of the cloud, organizations can keep their IoT devices operations fast and efficient without compromising on the valuable analytical information that can help them enhance their services and drive innovation.

The future of technology and network infrastructure is not likely to be found just in the cloud or edge but somewhere in the middle ground. As organizations all over the world move closer towards infrastructure transformation, they will find ways to leverage the benefits of both edge computing and cloud computing to overcome their shortcomings.

SEE ALSO: The Future of Cloud Computing Today

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Sources

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[3] MarketsandMarkets (2019) “Cloud Computing Market Worth $623.3 Billion by 2023 – Exclusive Report by MarketsandMarkets™” [Online] Available from: https://www.prnewswire.com/news-releases/cloud-computing-market-worth-623-3-billion-by-2023–exclusive-report-by-marketsandmarkets-300802108.html [Accessed November 2020]

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[5] Grand View Research (2020) “Edge Computing Market Worth $43.4 Billion By 2027 | CAGR: 37.4%” [Online] Available from: https://www.grandviewresearch.com/press-release/global-edge-computing-market [Accessed November 2020]

[6] Analysys Mason (2020) “Edge computing: operator strategies, use cases and implementation” [Online] Available from: https://www.analysysmason.com/research/content/white-papers/edge-computing-strategies-rma16/ [Accessed November 2020]

[7] PC Mag (2019) “Majority of US Homes Have a Video Streaming Service” [Online] Available from: https://in.pcmag.com/youtube-tv/132404/majority-of-us-homes-have-a-video-streaming-service [Accessed November 2020]

[8] Forbes (2019) “How Many Streaming Video Services Does The Average Person Subscribe To?” [Online] Available from: https://www.forbes.com/sites/tonifitzgerald/2019/03/29/how-many-streaming-video-services-does-the-average-person-subscribe-to/?sh=3519500d6301 [Accessed November 2020]

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[10] BlueWeave Consulting (2020) “Global Autonomous Vehicles Market Size, By Vehicle Type (Passenger, Commercial, and Defense), By Level of Automation (Level 3, Level 4, and Level 5), By Component (Hardware, Software, and Service), and by Region (North America, Europe, Asia Pacific, Middle East & Africa and Latin America); Trend Analysis, Competitive Market Share & Forecast, 2016-26” [Online] Available from: https://www.blueweaveconsulting.com/global-autonomous-vehicles-market/toc [Accessed November 2020]

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