Azka Amin¹, Xihua Liu², Imran Khan³, Peerapong Uthansakul^{4, *}, Masoud Forsat⁵, Seyed Sajad Mirjavadi⁵

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1487-1505, 2020, DOI:10.32604/cmc.2020.011749 - 20 August 2020

Abstract One of the most effective technology for the 5G mobile communications is Device-to-device (D2D) communication which is also called terminal pass-through technology. It can directly communicate between devices under the control of a base station and does not require a base station to forward it. The advantages of applying D2D communication technology to cellular networks are: It can increase the communication system capacity, improve the system spectrum efficiency, increase the data transmission rate, and reduce the base station load. Aiming at the problem of co-channel interference between the D2D and cellular users, this paper proposes… More >

Jin Han¹, Wenhao Jiang¹, Jin Shi^{2, *}, Sun Xin², Jin Peng², Haibo Liu³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1171-1184, 2020, DOI:10.32604/cmc.2019.07447 - 10 June 2020

Abstract The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development. The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling. These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model. In response to these problems, the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area, which are more accurate and efficient. Based on the query statistics of… More >

Ruijie Lin¹, Haitao Xu^{2, *}, Meng Li³, Zhen Zhang⁴

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 961-972, 2020, DOI:10.32604/cmc.2020.09686 - 10 June 2020

Abstract In this paper, we have proposed a differential game model to optimally solve the resource allocation problems in the edge-computing based wireless networks. In the proposed model, a wireless network with one cloud-computing center (CC) and lots of edge services providers (ESPs) is investigated. In order to provide users with higher services quality, the ESPs in the proposed wireless network should lease the computing resources from the CC and the CC can allocate its idle cloud computing resource to the ESPs. We will try to optimally allocate the edge computing resources between the ESPs and More >

Yifei Wei^{1, *}, Yu Gong¹, Qiao Li¹, Mei Song^{1, *}, Xiaojun Wang²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 501-514, 2020, DOI:10.32604/cmc.2020.010048 - 20 May 2020

Abstract In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation More >

Yifei Wei^{1, *}, Yinxiang Qu¹, Min Zhao¹, Lianping Zhang², F. Richard Yu³

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1515-1532, 2020, DOI:10.32604/cmc.2020.09130 - 30 April 2020

Abstract Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the… More >

Wenchen He^1,*, Shaoyong Guo¹, Yun Liang², Rui Ma³, Xuesong Qiu¹, Lei Shi⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1345-1364, 2019, DOI:10.32604/cmc.2019.06669

Abstract With the popularization of terminal devices and services in Internet of things (IoT), it will be a challenge to design a network resource allocation method meeting various QoS requirements and effectively using substrate resources. In this paper, a dynamic network slicing mechanism including virtual network (VN) mapping and VN reconfiguration is proposed to provide network slices for services. Firstly, a service priority model is defined to create queue for resource allocation. Then a slice including Virtual Network Function (VNF) placement and routing with optimal cost is generated by VN mapping. Next, considering temporal variations of More >

Yeting Guo¹, Fang Liu^2,*, Nong Xiao¹, Zhengguo Chen^1,3

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 777-792, 2019, DOI:10.32604/cmc.2019.06366

Abstract Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes… More >

Yifei Wei^1,*, Zhaoying Wang¹, Da Guo¹, F. Richard Yu²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 89-104, 2019, DOI:10.32604/cmc.2019.04836

Abstract To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile users’ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user… More >

Changming Zhao^1,2,*, Tiejun Wang², Alan Yang³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 423-437, 2019, DOI:10.32604/cmc.2019.07501

Abstract In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation… More >

Jixian Zhang¹, Ning Xie¹, Xuejie Zhang¹, Kun Yue¹, Weidong Li^2,*, Deepesh Kumar³

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 123-135, 2018, DOI:10.3970/cmc.2018.03728

Abstract Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training More >