Fog Computing vs. Cloud Computing for IoT Projects

By 2020, there will be 30 billion IoT devices worldwide, and in 2025, the number will exceed 75 billion connected things, according to Statista. All these devices will produce huge amounts of data that will have to be processed quickly and in a sustainable way. To meet the growing demand for IoT solutions, fog computing comes into action on par with cloud computing. Fog is even better at some things.

Number of connected devices worldwide from 2015 to 2025 (in billions)

Source: Statista

The purpose of this article is to compare fog vs. cloud and tell you more about fog vs cloud computing possibilities, as well as their pros and cons.

Cloud Computing

We’ve already got used to the technical term cloud, which is a network of multiple devices, computers and servers connected to each other over the Internet.

Such computing system can be figuratively divided into two parts:

  • The frontend — consists of the client devices (computers, tablets, mobile phones).
  • The backend — consists of data storage and processing systems (servers) that can be located far away from the client devices and make up the cloud itself.

These two layers communicate with each other directly by means of wireless connections.

Fog–vs–cloud–computing–photo

Cloud computing technology provides various types of services that are categorized into three groups:

  • IaaS (Infrastructure as a Service) — a remote data center with resources such as data storage capacity, processing power and networking.
  • PaaS (Platform as a Service) — a development platform with tools and components for creating, testing and launching applications.
  • SaaS (Software as a Service) — ready-made software tailored to a variety of business needs.
Read also: IaaS vs. PaaS vs. SaaS: What’s the Difference?

Connecting your company to the cloud, you get access to the above-mentioned services from any location and via different devices. Hence, availability is the greatest advantage. Moreover, there is no need to maintain local servers and worry about downtimes — the vendor supports everything for you, saving you money.

The integration of the Internet of Things with the cloud is a cost-effective way to do business. Off-premise services provide the necessary scalability and flexibility to manage and analyze data gathered by connected devices, while specialized platforms (e.g. Azure IoT Suite, IBM Watson, AWS, Google Cloud IoT) give developers the power to create IoT apps without big investments into hardware and software.

Pros of Cloud for IoT

Since connected devices have limited storage capacity and processing power, the integration with cloud computing comes to assistance:

  • Improved performance (the communication between IoT sensors and data processing systems is faster)
  • Storage capacities (highly scalable and unlimited storage space are able to integrate, aggregate and share the enormous amount of data)
  • Processing capabilities (remote data centers provide unlimited virtual processing capabilities on-demand)
  • Reduced costs (license fees are lower than the cost of the on-premise equipment and its continuous maintenance)

Cons of Cloud for IoT

Unfortunately, there is nothing immaculate, and cloud technology has some downsides, especially for the Internet of Things services.

  • High latency (more and more IoT apps require very low latency, but cloud can’t guarantee it because of the distance between client devices and data processing centers)
  • Downtime (technical issues and interruptions in networks may occur for any reason in any Internet-based system and make customers suffer from an outage; many companies use multiple connection channels with automated failover to avoid problems)
  • Security and privacy (your private data is transferred through globally connected channels alongside thousands of gigabytes of other users’ information; no surprise that the system is vulnerable to cyberattacks or data loss; the problem can be partially solved with the help of hybrid or private clouds)   
Read also: Private vs. Public Cloud Computing

Fog Computing

The term fog computing (or fogging) was coined by Cisco in 2014, so it is new for the general public. Fog and cloud computing are interconnected. In nature, fog is closer to the earth than clouds; in the technological world, it is just the same, fog is closer to end-users, bringing cloud capabilities down to the ground.

The definition may sound like this: fog is the extension of cloud computing that consists of multiple edge nodes directly connected to physical devices.

Fog–vs–cloud–computing–image

Such nodes are physically much closer to devices if compared to centralized data centers, which is why they are able to provide instant connections. The considerable processing power of edge nodes allows them to perform computation of a great amount of data on their own, without sending it to distant servers.

Fog can also include cloudlets — small-scale and rather powerful data centers located at the edge of the network. Their purpose is to support resource-intensive IoT apps that require low latency.

The main difference between fog computing and cloud computing is that cloud is a centralized system, while fog is a distributed decentralized infrastructure.

Fog computing is a mediator between hardware and remote servers. It regulates which information should be sent to the server and which can be processed locally. In this way, fog is an intelligent gateway that offloads clouds enabling more efficient data storage, processing and analysis.

One should note that fog networking is not a separate architecture and it doesn’t replace cloud computing but rather complements it, getting as close to the source of information as possible.

The new technology is likely to have the greatest impact on the development of IoT, embedded AI and 5G solutions, as they, like never before, demand agility and seamless connections.

Pros of Fog Computing

The fogging approach has many benefits for the Internet of Things, Big Data and real-time analytics. Here are the main advantages of fog computing over cloud computing:

  • Low latency (fog is geographically closer to users and is able to provide instant responses)
  • No problems with bandwidth (pieces of information are aggregated at different points instead of sending them together to one center via one channel)
  • Loss of connection is impossible (due to multiple interconnected channels)
  • High security (because data is processed by a huge number of nodes in a complex distributed system)
  • Improved user experience (instant responses and no downtimes satisfy users)
  • Power-efficiency (edge nodes run power-efficient protocols such as Bluetooth, Zigbee or Z-Wave)
Read also: IoT Protocols and Connectivity Options 

Cons of Fog Computing

The technology doesn’t have any apparent disadvantages, but some shortcomings can be named:

  • A more complicated system (fog is an additional layer in the data processing and storage system)
  • Additional expenses (companies should buy edge devices: routers, hubs, gateways)
  • Limited scalability (fog is not as scalable as cloud)

Fog Computing vs. Cloud Computing: Key Differences

Cloud vs. fog concepts are very similar to each other. But still, there is the difference between cloud and fog computing on some parameters. Here is a point-by-point comparison of fog computing and cloud computing:

  1. Cloud architecture is centralized and consists of large data centers that can be located around the globe, thousand miles away from client devices. Fog architecture is distributed and consists of millions of small nodes located as close to client devices as possible.
  2. Fog acts like a mediator between data centers and hardware, and hence  it is closer to end-users. If there is no fog layer, the cloud communicates with devices directly, which is time-consuming.
  3. In cloud computing, data processing takes place in remote data centers. Fog processing and storage are done on the edge of the network close to the source of information, which is crucial for real-time control.
  4. Cloud is more powerful than fog regarding computing capabilities and storage capacity.
  5. The cloud consists of a few large server nodes. Fog includes millions of small nodes.
  6. Fog performs short-term edge analysis due to instant responsiveness, while the cloud aims for long-term deep analysis due to slower responsiveness.
  7. Fog provides low latency; cloud — high latency.
  8. A cloud system collapses without an Internet connection. Fog computing uses various protocols and standards, so the risk of a failure is much lower.
  9. Fog is a more secure system than the cloud due to its distributed architecture.

The table below helps better understand the difference between fog and cloud, summarizing their most important features.

Cloud and Fog Computing: a Comparison Chart

Cloud

Fog

Architecture Centralized Distributed
Communication with devices From a distance Directly from the edge
Data processing Far from the source of information Close to the source of information
Computing capabilities Higher Lower
Number of nodes Few Very large
Analysis Long-term Short-term
Latency High Low
Connectivity Internet Various protocols and standards
Security Lower Higher

Final Thoughts

New requirements of the emerging technologies are the driving force behind IT development. The Internet of Things is a constantly growing industry that requires more efficient ways to manage data transmission and processing.

One of the approaches that can satisfy the demands of an ever-increasing number of connected devices is fog computing. It utilizes the local rather than remote computer resources, making performance more efficient and powerful and reducing bandwidth issues.

Companies should compare cloud vs. fog computing to make the most of the emerging opportunities and harness the true potential of the technologies.

SaM Solutions has 25 years of experience in IT consulting and custom software engineering. We have a wide range of cloud and IoT use cases, as well as vast expertise in using other technologies. Please get in touch to learn more, or if you need to develop a solution.

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