A Resource Allocation Method in Distributed Heterogeneous Environment in Mobile Edge Computing

Abstract

The invention belongs to the technical field of wireless communication, and relates to a resource allocation method in a distributed heterogeneous environment in mobile edge computing; the method includes establishing a diversified task offloading model according to offloading delays corresponding to different service types in the MEC environment ;Establish the buying and selling game model between the user and the MEC server; and establish the maximum revenue model of the user and the MEC server respectively; use the Lyapunov optimization algorithm to improve the user model, and solve it through the Lagrange multiplier method and KKT conditions The optimal purchase strategy of the user is obtained; based on this strategy, the optimal quotation strategy of the MEC server is solved; if the optimal purchase strategy and the optimal quotation strategy satisfy the Stein-Kolberg equilibrium solution, the MEC server uses the optimal strategy for different Users' computing resources are allocated on demand. This method can achieve a compromise between user offloading revenue and delay, as well as elastic control and on-demand allocation of task offloading and computing resource allocation.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and relates to a resource allocation method in a distributed heterogeneous environment in mobile edge computing. Background technique [0002] The rapid development and integration of the mobile Internet and the Internet of Things (IoT) has led to an exponential increase in mobile terminal equipment and data traffic. At the same time, emerging applications such as virtual reality / augmented reality, intelligent driving, and intelligent medical care continue to emerge , to further promote the explosive growth of computing-intensive services and applications, and put forward higher requirements for the computing power and battery life of mobile terminals. However, traditional mobile terminals are often limited by size and weight, which still have serious constraints in terms of CPU computing power, battery life, and storage capacity. As an emerging and promising computing paradigm, M...

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Problems solved by technology

[0006] Through a large number of research findings and based on the above discussions, the existing algorithms still fail to solve the problem of different IoT devices and application services under the time-varying network environment of the IoT-oriented MEC (such as the randomness generated by massive IoT device tasks). Differentiated requirements in terms of latency and energy consumption

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