Joint computing and communication resource allocation for satellite communication networks with edge computing

Zhang, Shanghong; Cui, Gaofeng; Long, Yating; Wang, Weidong

doi:10.23919/jcc.2021.07.019

Cited by 47 publications

(21 citation statements)

References 41 publications

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“…In addition to the network resources, the delay performance of the nodes is also conditionally constrained. Equation (6) shows the delay constraints of the nodes.…”

Section: Constraint Conditionsmentioning

confidence: 99%

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Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

et al. 2023

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The dramatic increase in the volume of users and services makes scheduling network resources for smart grids a key challenge. Network slicing is an important technology to solve this problem. We introduce edge computing networks into the smart grid to intelligently allocate resources based on users' quality of service (QoS) and available resources. However, existing heuristic resource scheduling algorithms often lead to resource fragmentation and thus fall into local optima. To this end, we propose a deep reinforcement learning (DRL)-based virtual network embedding algorithm to optimize the resource allocation strategy of smart grids from a network virtualization perspective. We extract the network properties of the smart grid to construct a policy network as a training environment for DRL agents. Finally, DRL derives the probability of each node being embedded based on the extracted attributes of edge computing nodes and completes user request (UR) embedding based on this probability. The experimental results show that the algorithm proposed in this paper has excellent performance with guaranteed low latency, 21% improvement in long-term revenue and 5.6% improvement in UR success rate compared with the other two algorithms.

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“…In addition to the network resources, the delay performance of the nodes is also conditionally constrained. Equation (6) shows the delay constraints of the nodes.…”

Section: Constraint Conditionsmentioning

confidence: 99%

“…The core network can be divided into several domains. The services of different domains can be distributed to the same edge network [6]. The nodes of edge computing are responsible for managing the transactions in the domain.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

et al. 2023

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“…Resource allocation is an important research direction to improve the transmission performance of the satellite-–ground network [ 20 ]. Paper [ 21 ] established a joint communication and computation optimization model for satellite networks, which utilized stochastic geometry and queuing theory to achieve the best response delay. Reference [ 22 ] designed a dynamic channel resource allocation algorithm for the satellite ground integration architecture, which improved the number of Internet of Things terminal connections.…”

Section: Related Workmentioning

confidence: 99%

Matching-Based Resource Allocation for Satellite–Ground Network

Ding

Zhu

Meng

et al. 2022

Sensors

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With the vigorous development of information and communication technology, mobile internet has undergone tremendous changes. How to achieve global coverage of the network has become the primary problem to be solved. GEO satellites and LEO satellites, as important components of the satellite–ground network, can offer service for hotspots or distant regions where ground-based base stations’ coverage is limited. Therefore, we build a satellite–ground network model, which transforms the satellite–ground network resource allocation problem into a matching issue between GEO satellites, LEO satellites, and users. A GEO satellite provides data backhaul for users, and a LEO satellite provides data transmission services according to users’ requests. It is important to consider the relationships between all entities and establish a distributed scheme, so we propose a three-sided cyclic matching algorithm. It is confirmed by a large number of simulation experiments that the method suggested in this research is better than the conventional algorithm in terms of average delay, satellite revenue, and number of users served.

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“…To fully exploit both communication and computing resources in LEO satellite networks, in [13], the computation offloading and resource allocation for the terrestrial-satellite networks are jointly optimized to minimize the weighted sum energy consumption of all users. In [14], aiming at minimizing the weighted sum latency of all users in satellite networks where edge servers are deployed in both satellites and gateway stations, the joint computing and communication resource management problems with two different satellite edge computing and local execution scenarios are investigated. However, the storage capacity constraint of MEC server is not taken into account in both [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

“…In [14], aiming at minimizing the weighted sum latency of all users in satellite networks where edge servers are deployed in both satellites and gateway stations, the joint computing and communication resource management problems with two different satellite edge computing and local execution scenarios are investigated. However, the storage capacity constraint of MEC server is not taken into account in both [13] and [14]. Differently, Li et al [15] concentrate on the cache-enabled LEO satellite network.…”

Section: Introductionmentioning

confidence: 99%

Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks

et al. 2023

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Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.INDEX TERMS LEO satellites, multi-access edge computing, computation offloading, resource allocation, caching.

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Joint computing and communication resource allocation for satellite communication networks with edge computing

Cited by 47 publications

References 41 publications

Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

Matching-Based Resource Allocation for Satellite–Ground Network

Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks

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