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Emerging power services with massive connections and real-time control have different requirements on delay, rate, reliability, etc. In order to meet the diversified requirements of power services, network slicing can provide guarantee for the wide application of 5G in power. The current research on network slicing adapting power services is still in its infancy. This paper proposes a trade-off analysis method for radio access network (RAN) slice delay and throughput based on dynamic intelligent resource allocation to meet the different QoS requirements of different services in smart grid scenarios. Firstly, clustering analysis is used to classify the communication requirements of smart grid services. A throughput demand model for enhanced mobile broadband (eMBB) slice users based on importance and a delay constraint model for ultra-reliable and low latency communication (uRLLC) slice users are constructed, and a throughput model for slice users based on cell capacity limitation is constructed. Then, the problem model of maximizing the throughput of the eMBB slice by reasonably allocating resource blocks and power under the premise of ensuring the delay of the uRLLC slice is established. The Lyapunov drift plus penalty function method is used to convert the above problem into a tractable form, and then the problem is solved by the active set method. Simulation results show that the different QoS requirements of smart grid services will significantly affect the actual queue length of the uRLLC slice as well as the throughput of the eMBB slice.

To bridge the gap between the resource-constrained mobile devices and the resource-demanding applications, mobile cloud computing (MCC) emerges for offloading complex tasks to a cloud server. Based on this concept, cloudlets, which move available resource to the vicinity of the mobile network, enhance further the system accessibility and performance. Moreover, to strengthen the network capacity in traffic intensive area, dense small cell network (DSCN) is proposed as one of the promising solutions. In this study, the operation of cloudlets and DSCN is collaboratively studied in order to further improve the system performance. On the one hand, users can select a cloudlet and dynamically adapt the connection according to the performance and the cost, which is referred to as a user-essential dynamic cloudlet selection problem. On the other hand, a cloudlet needs to set the optimal selling price and the size of resource for the users, which is considered as a cloudlet resource allocation problem. To jointly address the problems of dynamic cloudlet selection and resource allocation, the authors propose a hierarchical evolutionary game to maximise the utilities. Simulation studies are carried out to demonstrate the effectiveness of the proposed algorithms, which, indeed, improve the entire system performance significantly.

Driven by the construction of the Ubiquitous Electricity Internet of things, various services have increasingly higher requirements for wireless communication indicators. The 5G network with low latency, large connection and large bandwidth is urgently needed to fundamentally meet various business requirements and network security requirements of the smart grid. Because of the type of service arrival of smart grid is unknown and lacks prior knowledge, reinforcement learning (RL) is used to conduct this research. Considering the differentiated service characteristics of the smart grid and the challenges of flexibility and adaptability of the communication platform, this paper aims to solve the resource allocation problem of the radio access network (RAN) slice of smart grid. In this paper, we firstly introduce three typical power services and propose a service priority concept. After reviewing the fundamental concepts and proving the convergence of RL algorithm, we propose a dynamic resource allocation strategy of the RAN slice for smart grid based on RL. Finally, simulation results prove that the proposed RL algorithm can achieve resource utilization and quality of experience (QoE) improvement against the fair allocation scheme.

Mobile cloud computing (MCC) is an emerging technology to relieve the tension between compute-intensive mobile applications and resource-constrained mobile terminals by offloading computing tasks to remote cloud servers. In this paper, we consider a novel MCC architecture consisting of remote cloud server, cloudlet and mobile terminal to guarantee low latency and low energy mobile consumption. To overcome the main bottlenecks of wireless bandwidth between mobile terminal and cloudlet, and the computation capability of cloudlet, the joint optimization strategy is proposed to enhance the quality of mobile cloud service. We formulate the wireless bandwidth and computing resource allocation model as a triple-stage Stackelberg game, and solve it by using backward method. In addition, the interplays of triple-stage game are discussed and the subgame optimal equilibrium for each stage is analyzed. An iterative algorithm is proposed to obtain Stackelberg equilibrium. Numerical results demonstrate the effectiveness of the proposed algorithm.

As one of the important application scenarios of the Green Internet of Things (IoT), the development of smart grid is an important means to promote the energy system revolution. Future power grid will rely heavily on smart devices based on the IoT concept. As the 5G standard matures and is put into commercial use, the green interconnected 5G mobile network has shown great potential. With 5G network slicing technology, a wireless private network can be virtualized for each industry to better meet the requirements of smart grid security, reliability and flexibility. Considering the differentiated service characteristics of the smart grid and the challenges of flexibility and adaptability of the communication platform, this paper aims to solve the resource allocation problem of the radio access network (RAN) slice of smart grid. Since the type of service arrival of smart grid is unknown and lacks prior knowledge, deep reinforcement learning (DRL) is used to conduct this research. The mapping from the RAN slice resource management to DRL of the smart grid is analyzed at first, and then discussion from the elastic application and the real-time application is presented. Furthermore, a smart grid RAN slice allocation strategy based on DRL is proposed. Finally, simulation results prove that the proposed algorithm can satisfy the maximization of the resource allocation demand of the smart grid on the RAN side while reducing the cost.

Based on software defined networking (SDN) and network function virtualization (NFV) technologies, network slicing can be customized to meet the end-to-end (E2E) quality of service (QoS) requirements of various power services in smart grids. E2E mission-critical services in smart grids should satisfy high reliability and low latency requirements, such as distribution automation, etc. In this paper, we slice the resource in core networks and flexibly coordinate different service function chains (SFCs) on the substrate physical network to enable different E2E mission-critical service delivery. Since the E2E average transmission delay is related to the congestion of each link in core networks, E2E transmission delay optimization problem is equivalent to minimize the maximum link congestion factor problem, which is Mixed Integer Linear Programming (MILP). Considering that the constraint of this problem is large and there are integer variables, the Generalized Benders Decomposition is adopted. The numerical results demonstrate the effectiveness of our proposed algorithm.

With the rapid development of the power grid, the variety of power services has become more and more diverse, resulting in different service needs. As one of the important technologies of 5G, network slicing aims to accommodate different services over the same physical network. In this paper, after briefly analyzing the background of smart grid, we investigate the related research of network slicing. Each kind of services in smart grid has its own bandwidth, reliability and delay tolerance requirements. Therefore, for the purpose of service guarantees in smart grid, we propose a slice management mechanism for resource allocation based on 5G network slicing technology. Due to the good self-learning and adaptive capabilities, we use BP(back-propagation) neural network to train the appropriate weights. Correspondingly, a heuristic algorithm for dynamic weights based on BP neural network to solve the optimization problem is proposed. Finally, simulation results demonstrate the effectiveness of our algorithm within the mechanism.

To meet the diversity of smart grid services, network slicing can be considered as a cost-effective way to provide customized services for different QoS (Quality of Service) requirements of each service. Considering the downlink transmission between gNB (gNodeB) and UEs (User Equipments), this paper focuses on logically divided eMBB (Enhance Mobile Broadband) slices and uRLLC (Ultra Reliable & Low Latency Communication) slices, which formulates a multi-objective optimization problem for different QoS requirements of various services in smart grid. The throughput of the eMBB slicing service is maximized while ensuring the delay of the uRLLC slicing service. Based on this, the Lyapunov optimization theorem is used to convert it into an equivalent drift-plus-penalty minimization problem, which could be solved by Lagrange dual decomposition method. Furthermore, simulation results demonstrate the effectiveness of our proposed RA-DT algorithm.

With the rapid development of the new generation of smart grid, the variety of smart grid services are becoming more and more diversified, which lead to various service demands. According to different service requirements, it is necessary to allocate network resources flexibly and dynamically. In order to solve the problem of resource allocation among network slices in smart grid, we propose a two-layer dynamic resource scheduling strategy for network slicing. It can be divided into inter slice resource allocation and intra slice resource allocation. The former refers to the allocation of network resources to network slices. The latter refers to that the network slice allocates the allocated resources to the users in the slice. The simulation results show that the proposed two-layer network slicing dynamic resource scheduling strategy is superior to the current resource scheduling strategy in terms of quality of experience (QoE) of users and system throughput, which can better meet the needs of users.