Abstract
The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing and mobile edge computing (MEC) to provide customized networks while reducing the service processing time. Due to the uncertainty of user requests and the environment, reasonable resource allocation is always particularly challenging. A novel dynamic resource allocation scheme for MEC slice systems, which formulates resource allocation and computation offloading issues as an optimization problem subject to the latency and rate, is proposed. Based on the dynamics of the slice requirements, quantity, and service time, the proposed problem is converted to a Markov decision process (MDP), and a state, action, and reward function are proposed. By exploiting the deep deterministic policy gradient (DDPG) algorithm, the wireless resources and computing resources are configured dynamically according to the requirements of different types of slices to maximize the revenue of the network operator. The simulation results demonstrate the influence of the slice arrival rate and total resources on the allocation policy. Compared with other schemes, the proposed scheme can provide a more effective performance when resources are scarce.
Highlights
The innovation and development of communication technology indicate that the future network will be a new era of the “smart Internet”, which is fully mobile, fully interconnected, and fully automated
In the 5G network, services are divided into three major scenarios [3]: enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine type communication, based on rate requirements, delay requirements, and connectivity requirements to achieve a vertical classification of services
A novel dynamic resource allocation scheme based on deep deterministic policy gradient (DDPG) in the mobile edge computing (MEC) slice system is proposed, which can be applied to provide resources for different slices in real time
Summary
The innovation and development of communication technology indicate that the future network will be a new era of the “smart Internet”, which is fully mobile, fully interconnected, and fully automated. In [27], [28], and [29], the authors investigated the issue of the partial offloading of tasks in multi-users edge networks with massive MIMO systems This issue was modeled as a mixed-integer nonlinear problem for the joint optimization of the offloading ratio, resource allocation and power to minimize the energy consumption of users and servers. (2) Under the condition of ensuring the differentiated QoS requirements of heterogeneous traffic, it is necessary to carry out autonomous offloading decisionmaking and dynamic resource allocation of slices based on the randomness of user tasks and request times. Radio and computing resources are dynamically configured for the slices at the system level according to the delay and rate requirements of each user In this regard, a new deep deterministic policy gradient (DDPG)-based dynamic resource allocation scheme for MEC slice systems is proposed to meet different QoS requirements for slices in a multiple-network slicing scenario with computation-intensive users. (4) The simulation results show that the proposed resource allocation algorithm can maximize the operator's revenue while meeting the QoS requirements of different slices and can dynamically adjust the radio and computing resources required by the slices
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