Video analytics is a complex field that involves processing and analyzing large amounts of video data to extract valuable insights and information. This may involve operating devices that function to study the data through video analytics technology. There has been a growing interest in using cloud, edge, hybrid, and fog computing to support video analytics in recent years. Each of these computing structures offers different and unique benefits that are suited to different use cases and applications.
Cloud computing is the delivery of computing services over the internet. This includes storage, processing, and analytics. Cloud computing solutions allow organizations to process and analyze large amounts of video data in real-time, using powerful servers and advanced algorithms. This is done by streaming video data to the cloud, where it is analyzed. Cloud computing solutions offer flexibility, and ease of use, making it an ideal choice for organizations with a large number of cameras that generate a high volume of video data. It is also scalable - organizations do not have to invest in additional hardware which are costly.
Edge computing refers to the processing of data closer to the source, such as at the edge of a network or on a device. In the context of video analytics, edge computing solutions allow for the analysis of video data to be done at the point of capture, such as at a security camera. This can reduce the amount of data that needs to be transmitted to the cloud, which can help to reduce costs and improve performance. The device is able to make decisions in real-time, which is a benefit over cloud computing. Edge computing solutions are well-suited to environments where internet connectivity is limited or unreliable, and for low-latency applications such as autonomous vehicles, and surgical video where near-instant response is critical.
Recently, edge computing has been trending, to meet the demands on the focus on data of organizations. While cloud computing has it benefits, there are also certain scenarios where edge computing are beneficial.
The convergence of cloud computing and edge computing is possible with a hybrid computing. Hybrid computing combines the benefits of cloud and edge computing, by using a combination of both centralized and decentralized computing resources. This can involve using cloud computing to process and store large amounts of video data, and edge computing to perform real-time analysis at the point of capture. Hybrid computing solutions can offer the best of both worlds, by combining the scalability and flexibility of cloud computing with the performance and low-latency of edge computing. This hybrid environment serves for better performance, and allows for greater control, as they can centralize and distribute more effectively.
Hybrid Cloud Computing
There is also the term hybrid cloud computing which refers to the use of a combination of on-premises, private cloud, and public cloud services that work coherently. For example, a hybrid cloud architecture arrangement might involve storing raw video data on-premises or in a private cloud for privacy and security reasons, while using the public cloud for more computer-intensive tasks such as running video analytics algorithms. Hence, this enables flexibility for storage locations.
Multi-cloud computing refers to the use of multiple distinct cloud services from different providers in a single application or architecture. In the context of video analytics, this might involve using multiple cloud providers for different aspects of the video analytics workflow. For example, one provider might be used to store and process the raw video data, while a provider might be used to run the video analytics algorithms and another different provider to archive the video once processed. This approach allows the use of solutions from offering providers and also provides dependability in the case of failure from the sources.
Fog computing, also known as edge-cloud computing or fogging, is an extension of cloud computing that extends to the edge of a network, where data is generated. Fog computing leverages the power of edge computing and cloud computing to provide low-latency, high-bandwidth, real-time processing capabilities to Internet of Things (IoT) devices. With fog computing, IoT devices will be able to send and process data locally, reducing the burden on the cloud and allowing for more efficient use of resources.
Difference between fog and edge computing
Fog computing and edge computing are often used interchangeably. While they are similar concepts, there are some subtle differences between the two. In summary, Edge computing is a subset of fog computing and focuses on the devices that have time-sensitive processing capabilities, whereas fog computing focuses on the network, by networking close to the edge. Both aim to move the computational power and storage closer to the devices that generate the data. The key point here is that both Edge computing and Fog computing are decentralized computing architectures that move computational and storage resources closer to the devices and data sources.
Apart from interoperable devices, there are also standalone solutions, where analytics is done on a single device without communicating with other devices or servers, which are suitable for cases that require to have the data locally.
In conclusion, the computing structures for video analytics are evolving rapidly, and cloud, edge, hybrid, and fog computing are becoming increasingly popular options. These different computing structures can be effectively integrated to provide a comprehensive solution for video analytics that address costs, performance and scalability, along with other considerations. They can also work with other technologies for an intelligent, complete ecosystem. Cloud computing is well-suited to organizations with larger amounts of video data, while edge computing is ideal for low-latency applications. These are the two common technology structures applicable to video analytics. Hybrid computing combines the best of both, and fog computing allows for real-time processing capabilities for IoT devices. Otherwise, standalone devices are used for local data. As each of these computing structures offers different, unique benefits for various targeted uses, the choice between them also depends on the specific use case, as discussed in a podcast by McKinsey Digital comparing cloud and edge computing.