TOP 5 Research Trends and Latest Developments of Edge Computing
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Fancy Wang 0904 2022
IoT + Edge Computing
The Internet of Things has become the No. 1 hot word in research papers related to edge computing, reflecting the close relationship between edge computing and the Internet of Things. The development constraints of IoT technology are mature, and the technical demand for edge computing is stronger.
The combination of the two lies in two aspects: on the one hand, it is necessary to solve how IoT devices can access edge computing in a low-cost way; on the other hand, it is also necessary to solve how edge computing can cope with the massive, heterogeneous, and dynamic characteristics of IoT services.
5G+ edge computing
The development of 5G technology makes the communication delay reach a level lower than the computing delay, which will fundamentally change many existing computing models, and will also cause more and more computing loads to be transferred from front-end mobile devices to edge computing servers.
This poses new requirements and challenges for the architecture of edge computing, and it is necessary to make important improvements on the basis of the existing cloud computing cluster architecture to adapt to the highly implementable, data-intensive, highly mobile, heterogeneous and dynamic 5G mobile services need.
Virtualization technology
Due to the heterogeneity of front-end devices, the computing requests served by edge computing are also highly heterogeneous. This requires edge servers to flexibly run various computing services.
Virtualization technology is one of the mainstream directions to solve this problem. By implementing network functions on different systems, different environments and even different hardware on general computing resources, flexible management of network functions is realized.
Compared with the virtualization technology in traditional cloud computing, the virtualization technology of edge computing has higher latency requirements. Not only that, but the computing resources of edge servers are much less than that of cloud servers, so that virtualization technology needs to be as lightweight as possible.
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Compute offload
Computing offloading is one of the classic problems in cloud computing, and it is also a very important core problem in edge computing. Computing offloading in edge computing means that computing tasks are transferred from the front-end device to the edge server to run.
After the task is executed, the edge server returns the calculation result to the front-end device or transmits it to the cloud server as required. Research in this direction focuses on answering several key core questions--whether it needs to be offloaded, which tasks to offload, to which server, and in what way.
Compared with task offloading in cloud computing, an important feature of edge computing is the transmission method of front-end devices and the selection of edge servers, which will seriously affect the performance of computing offloading.
Low-power IoT systems supporting edge computing
The proposal of edge computing is not aimed at a specific application scenario, and more of it plays a role similar to a content distribution network to reduce the access delay of applications. This feature can just solve the problems of limited energy and limited resources in the IoT system.
In addition to the exploration of various applications, the common issues in this direction also include low-power embedded systems (supporting computing offload, low-power task transmission, energy-efficient data collection, etc.)
Edge Computing and Artificial Intelligence Algorithms
The collision of edge computing and artificial intelligence has produced a series of problems in two directions, namely, artificial intelligence algorithms based on edge computing, and edge system optimization based on artificial intelligence.
Compared with the traditional artificial intelligence algorithm, the change of the former system architecture brings about the problem of coordination between multiple devices.
The latter uses artificial intelligence algorithms and data generated by the edge computing system process to optimize and make decisions on the edge system itself. Considering that one of the important missions of edge computing is to bring artificial intelligence into various IoT devices, this direction is attracting more and more attention.