Abstract
With the rapid development and popularization of 5G and the Internet of Things, a number of new applications have emerged, such as driverless cars. Most of these applications are time-delay sensitive, and some deficiencies were found during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved at present. In 5 g environments, edge computing can better meet the needs of low delay and wide connection applications, and support the fast request of terminal users. However, edge computing only has the edge layer computing advantage, and it is difficult to achieve global resource scheduling and configuration, which may lead to the problems of low resource utilization rate, long task processing delay and unbalanced system load, so as to lead to affect the service quality of users. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes a genetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve task scheduling and resource allocation, and designs a series of experiments to verify the effectiveness of the GSA-EDGE algorithm. The experimental results show that the proposed method can reduce the time delay of task processing compared with the local task processing method and the task average allocation method.
Highlights
Most of the emerging application scenarios based on 5G technology are either computationally intensive or data intensive, presenting “wide link” and requiring “low latency”
This paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes a genetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve task scheduling and resource allocation, and designs a series of experiments to verify the effectiveness of the GSA-EDGE algorithm
The experimental results show that the proposed method can reduce the time delay of task processing compared with the local task processing method and the task average allocation method
Summary
Most of the emerging application scenarios based on 5G technology are either computationally intensive or data intensive, presenting “wide link” and requiring “low latency”. The European Telecommunications Standards Institute (ETSI) and the Edge Computing Consortium (ECC) have proposed different concepts of edge computing These concepts have reached a consensus: complete data processing near the data generation terminal and provide services nearby. This paper first describes the concept and architecture of edge computing, introduces the three-tier architecture of “Cloud-Edge-Terminal”, and presents a task scheduling strategy based on genetic simulated annealing algorithm. Edge computing refers to an open platform that integrates core capabilities of network, computing, storage, and applications on the edge of the network close to the source of things or data, and provides edge intelligent services nearby to meet the needs of industry digitization in agile connection, real-time business, data optimization, application intelligence, Key requirements for security and privacy protection. Provide IT service environment and computing capabilities at the edge of mobile networks, and emphasize proximity to mobile users to reduce network operation and service delivery delays and improve user experience
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