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 comprises the steps of establishing diversified task unloading models according to unloading delays corresponding to different service types in an MEC environment; establishing a buying and selling game model between the user and the MEC server; respectively establishing maximized income models of the user and the MEC server; adopting a Lyapunov optimization algorithm to improve the user model, and solving an optimal purchase strategy of the user through a Lagrange multiplier method and KKT conditions; solving an optimal quotation strategy of the MEC server based on the strategy; and if the optimal purchase strategy and the optimal quotation strategy satisfy a Stackleberg equilibrium solution, allocating computing resources of different users as required according to the optimal strategy by the MEC server. By means of the method, compromise of user unloading income and time delay can be achieved, and elastic control and on-demand distribution of task unloading and computing resource distribution can be achieved.

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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