ISSN 1000-1239 CN 11-1777/TP

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    Journal of Computer Research and Development    2021, 58 (6): 1246-1247.   DOI: 10.7544/issn1000-1239.2021.qy0602
    Abstract103)   HTML0)    PDF (196KB)(110)       Save
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    Energy Efficiency Evaluation Method of Data Centers for Cloud-Network Integration
    Long Saiqin, Huang Jinna, Li Zhetao, Pei Tingrui, Xia Yuanqing
    Journal of Computer Research and Development    2021, 58 (6): 1248-1260.   DOI: 10.7544/issn1000-1239.2021.20201069
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    Cloud-network integration is developing at an accelerated pace, which not only promotes the rapid growth of data center scale, but also brings huge energy consumption. How to formulate reasonable data center energy efficiency evaluation standards has become a key issue that needs to be solved urgently to guide the improvement of data center energy efficiency. It is difficult to evaluate the energy efficiency of data centers comprehensively based on a single metric, and different data center energy efficiency metrics have their own focuses, and even contradict each other. It is proposed to integrate multiple metrics to evaluate the energy efficiency of data centers comprehensively. The model adopts a combination of subjective and objective weighting methods to set weights for different energy efficiency metrics. A multi-metric fusion evaluation strategy is designed based on the cloud model to obtain a more scientific and comprehensive data center energy efficiency evaluation result. Finally, the gray correlation method is proposed to analyze the relationship between the evaluation results and various energy efficiency metrics. The analysis results have important guiding significance for the improvement of data center energy efficiency.
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    Design of an Intelligent Routing Algorithm to Reduce Routing Flap
    Shao Tianzhu, Wang Xiaoliang, Chen Wenlong, Tang Xiaolan, Xu Min
    Journal of Computer Research and Development    2021, 58 (6): 1261-1274.   DOI: 10.7544/issn1000-1239.2021.20201073
    Abstract134)   HTML0)    PDF (4082KB)(130)       Save
    Recently, researchers have begun to focus on data-driven network protocol design methods to replace traditional protocol design methods that rely on human experts. While the resulting intelligent routing technology is rapidly developing, there are also problems to be solved urgently. This paper studies the large-scale routing flapping caused by the current intelligent routing algorithm in the routing update process and the resulting decrease in forwarding efficiency of network. A smart routing algorithm, named FSR(flap suppression routing), for route flapping suppression is proposed. While pursuing the uniform link load of the entire network and making full use of the forwarding resources of the entire network, FSR seeks an update plan that is most similar to the existing routing strategies. This reduces routing flapping in each routing update cycle, reduces route convergence time, and improves overall network forwarding efficiency. Experiments have shown that FSR algorithm can significantly improve the routing convergence speed, increase the network throughput by about 30% compared with the control algorithms, and significantly reduce the path length and the probability of congestion.
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    A Fine-Grained Multi-Access Edge Computing Architecture for Cloud-Network Integration
    Wang Lu, Zhang Jianhao, Wang Ting, Wu Kaishun
    Journal of Computer Research and Development    2021, 58 (6): 1275-1290.   DOI: 10.7544/issn1000-1239.2021.20201076
    Abstract151)   HTML0)    PDF (4662KB)(148)       Save
    Nowadays, a paradigm shift in mobile computing has been introduced by the ever-increasing heterogenous terminal devices, from the centralized mobile cloud towards the mobile edge. Multi-access edge computing (MEC) emerges as a promising ecosystem to support multi-service and multi-tenancy. It takes advantage of both mobile computing and wireless communication technologies for cloud-network integration. However, the physical hardware constraints of the terminal devices, along with the limited connection capacity of the wireless channel pose numerous challenges for cloud-network integration. The incapability of control over all the possible resources (e.g., computation, communication, cache) becomes the main hurdle of realizing delay-sensitive and real time services. To break this stalemate, this article investigates a software-defined fine-grained multi-access architecture, which takes full control of the computation and communication resources. We further investigate a Q-Learning based two-stage resource allocation strategy to better cater the heterogenous radio environments and various user requirements. We discuss the feasibility of the proposed architecture and demonstrate its effectiveness through extensive simulations.
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    Software Defined Virtualized Access Network Supporting Network Slicing and Green Communication
    Wang Ting
    Journal of Computer Research and Development    2021, 58 (6): 1291-1306.   DOI: 10.7544/issn1000-1239.2021.20201079
    Abstract107)   HTML0)    PDF (5820KB)(103)       Save
    Software defined networking (SDN) is disrupting the traditional networking industry by shifting network control from the physical network devices to the centralized software, thus facilitating scalability, flexibility and efficiency of the network. In the access networks, varied access technologies and massive number of access devices lead to dramatically increased OPEX, which forces operators to find feasible solutions to increase the revenue-expenditure ratio and achieve a sustainable business model. To deal with these challenges, this paper presents a new SDN-based architecture SDVAN for the access network, which provides cost-efficient network control and management with high scalability and customization support. The new architecture SDVAN abstracts control plane of all physical devices to one centralized controller which enables flexible customization of access network through software-defined fashion. The innovative node design implements a simple programmable node which naturally provides elastic support to various access technologies and efficiently improves the resource utilization. In order to automate orchestration of network services, resource modeling and network abstraction methodologies are introduced, which exposes different levels of visibility and controllability based on the trust level. Lastly, SDVAN well implements network slicing function supporting multi-tenancy and multi-version of network appliance. The theoretical analysis and experimental results prove the effectiveness and practicality of the proposed new architecture SDVAN.
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    Algorithm of Mixed Traffic Scheduling Among Data Centers Based on Prediction
    Wang Ran, Zhang Yuchao, Wang Wendong, Xu Ke, Cui Laizhong
    Journal of Computer Research and Development    2021, 58 (6): 1307-1317.   DOI: 10.7544/issn1000-1239.2021.20201087
    Abstract116)   HTML0)    PDF (1020KB)(123)       Save
    To handle the problem of low link utilization resulting from mixing online and offline traffic in one data center transmission network and separating them with a fix way in the same link, we propose a solution of offline traffic scheduling based on online traffic prediction. It firstly predicts online traffic needed to be guaranteed preferentially in link using an algorithm calling Sliding-k that combines EWMA and Bayesian changepoint detection algorithm. This customized algorithm can make prediction sensitive to a sudden change of network environment and reduce unnecessary re-adjustments when network environment is steady at the same time. Therefore, it can exactly meet the prediction demand under different network environments. After computing the remaining space for offline traffic according to online traffic prediction result and implementing dynamic bandwidth allocation, it uses an algorithm called SEDF that can consider both traffic deadline and size to schedule offline traffic. Experimental results reflect that Sliding-k can meet the prediction needs both when network mutation occurs and when network has no change and can simultaneously improve the accuracy of traditional EWMA algorithm. The combination of Sliding-k and SEDF can improve the utilization of data center links, so as to make full use of link resources.
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    Online Joint Optimization Mechanism of Task Offloading and Service Caching for Multi-Edge Device Collaboration
    Zhang Qiuping, Sun Sheng, Liu Min, Li Zhongcheng, Zhang Zengqi
    Journal of Computer Research and Development    2021, 58 (6): 1318-1339.   DOI: 10.7544/issn1000-1239.2021.20201088
    Abstract161)   HTML1)    PDF (3553KB)(169)       Save
    By deploying communication, computing and storage resources on the edge devices, mobile edge computing (MEC) can effectively overcome the problems of long transmission distance and high response delay of traditional cloud computing. Therefore, MEC can satisfy the service requirements of emerging computation-intensive and delay-sensitive applications. Nevertheless, the resources of edge devices are limited and the workload among multiple edge devices is unbalanced in MEC. In order to address the above problems, multi-edge device collaboration becomes an inevitable trend. However, multi-edge device collaboration faces two challenges. First, task offloading and service caching are mutually coupled. Second, the workload and resource state of the edge devices have the characteristics of spatial-temporal change. The two challenges significantly increase the difficulty of solving this issue. In response to the above challenges, this paper proposes the online joint optimizing mechanism of task offloading and service caching for multi-edge device collaboration. And we decouple the joint optimizing problem into two sub-problems of service caching and task offloading in this paper. For the service caching sub-problem, a collaborative service caching algorithm based on contextual combinatorial multi-armed bandit is proposed. For the task offloading sub-problem, a preference-based double-side matching algorithm is designed. Simulation results demonstrate that the proposed algorithm in this paper can efficiently reduce the overall execution delay of tasks, and realize workload balancing among edge devices.
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