Edge computing scheduling method and system based on software definition

Abstract

The invention discloses an edge computing scheduling method and system based on software definition. The edge computing scheduling method method comprises the following steps that: 1) a local computing unit collects a local computing task i and uploads the local computing task i to a distributed controller; 2) the distributed controller determines that the i is computed locally or unloaded to an edge computing service node to be executed according to a value of a two-tuple (v<L>[t], v<C>[t]), wherein v<L>[t]=1 represents that the i is executed locally in a t-th time slot, and v<C>[t]=1 represents that the i is unloaded to the edge computing service node to be executed in the t-th time slot; and introducing a value for determining the two-tuple (v<L>[t], v<C>[t]), wherein S={0, 1,..., Q}*{0, 1,..., M}*{0, 1,..., N1}, Q represents the maximum capacity of a task queue buffer area, M represents the number of data packets contained in the i, and N represents the number of time slots required by local calculation of the i.

Description

Technical field[0001]The invention belongs to the field of edge computing of computer networks, and specifically is a software-defined edge computing scheduling method and system.Background technique[0002]Since the 21st century, the continuous development and evolution of information network technology has brought new opportunities and developments to various industries and fields such as basic communications, financial economy, and traditional manufacturing, and has greatly promoted cross-industry cross-integration. With the rapid development of the Internet of Things and the popularity of wireless networks, the era of the Internet of Everything has come, and the number of network edge devices has increased rapidly, making the data generated by such devices have reached the zettabyte (ZB) level. In the era of centralized big data processing with cloud computing model as the core, its key technologies can no longer efficiently process the data generated by edge devices. The main man...

AI Technical Summary

Problems solved by technology

In the era of centralized big data processing centered on the cloud computing model, its key technologies are no longer able to efficiently process data generated by edge devices, mainly in the following areas: 1) The linear growth of centralized cloud computing capabilities cannot match the explosive growth of massive edge Data; 2) The transmission of massive data from network edge devices to the cloud center causes a sharp increase in the load of network transmission bandwidth, resulting in longer network delays; 3) The data at the edge of the network involves personal privacy, making privacy and security issues particularly prominent; 4 ) Network edge devices with limited power consumption transmit data to the cloud center and consume a large amount of power

The protocol system supported by existing network equipment is huge, resulting in high complexity, which not only limits the technical development of IP networks, but also cannot meet the current application trends such as cloud computing, big data, and server virtualization. Users' demand for traffic continues to expand, and various New services continue to emerge, increasing network operation and maintenance costs

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