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  • Open Access


    Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

    Yifei Wei1, *, 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

    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 >

  • Open Access


    QoS-Aware and Resource-Efficient Dynamic Slicing Mechanism for Internet of Things

    Wenchen He1,*, 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 >

  • Open Access


    Task-Based Resource Allocation Bid in Edge Computing Micro Datacenter

    Yeting Guo1, Fang Liu2,*, Nong Xiao1, Zhengguo Chen1,3

    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 >

  • Open Access


    Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing

    Yifei Wei1,*, 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 >

  • Open Access


    Machine Learning Based Resource Allocation of Cloud Computing in Auction

    Jixian Zhang1, Ning Xie1, Xuejie Zhang1, Kun Yue1, Weidong Li2,*, 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 >

  • Open Access


    A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

    Changming Zhao1,2,*, 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 >

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