ISSN 1000-1239 CN 11-1777/TP


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    Journal of Computer Research and Development    2018, 55 (3): 447-448.  
    Abstract2121)   HTML77)    PDF (482KB)(1570)       Save
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    Integrated Trust Based Resource Cooperation in Edge Computing
    Deng Xiaoheng, Guan Peiyuan, Wan Zhiwen, Liu Enlu, Luo Jie, Zhao Zhihui, Liu Yajun, Zhang Honggang
    Journal of Computer Research and Development    2018, 55 (3): 449-477.   DOI: 10.7544/issn1000-1239.2018.20170800
    Abstract2626)   HTML37)    PDF (8088KB)(2262)       Save
    Edge computing, as a new computing paradigm, is designed to share resources of edge devices, such as CPU computing ability, bandwidth, storage capacity and so on, to meet the requirements of the real-time response, privacy and security, computing autonomy. With the development of Internet of things (IoT) and mobile Internet technology, edge computing is of great potential of being widely used. This paper investigates the basic features, concepts and definitions, the latest state of art, and the challenge and trends of edge computing. Based on the key challenge of guarantee of users’ quality of experience (QoE), privacy and security in edge computing, we focus on the requirement of users and consider the quality of experience of users to optimize the edge computing system. We integrate three aspects of trust properties, which are identity trust, behavior trust and ability trust, to evaluate resources and users to ensure the success of resource sharing and collaborative optimization in edge computing. This paper also investigates various computing modes such as cloud computing, P2P computing, CS and grid computing, and constructs a multi-layer, self-adaptive, uniform computing model to dynamically match different application scenarios. This model has four contributions: 1) reveal the mechanism of parameters mapping between quality of service (QoS) and quality of experience; 2) construct identity trust, behavior trust of resources and users evaluation mechanisms; 3) form an integrated trust evaluation architecture and model; 4) design a resource scheduling algorithm for stream processing scenario, considering the computing ability, storage capacity and dynamical channel capacity depends on mobility to improve the quality of experience of users. Through this model and mechanism, resources in the end point, edge network, cloud center three levels are expected to be trusted sharing and optimized using, and the users' QoE needs are well satisfied. At last, simulation results show the validity of the model.
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    MEC Coordinated Future 5G Mobile Wireless Networks
    Qi Yanli, Zhou Yiqing, Liu Ling, Tian Lin, Shi Jinglin
    Journal of Computer Research and Development    2018, 55 (3): 478-486.   DOI: 10.7544/issn1000-1239.2018.20170801
    Abstract2464)   HTML35)    PDF (3340KB)(1821)       Save
    Future 5G wireless networks are confronted with various challenges such as exponentially increasing mobile traffic and new services requiring high backhaul bandwidth and low latency. Integrating mobile edge computing (MEC) into 5G network architectures may be a promising solution. First of all, this paper introduces the functional framework of MEC systems. Then the standardization progress of MEC in 5G is presented. Supporting MEC, the functionalities of 5G core network are described in detail. Given MEC deployment strategies and the mobile network architectures of future 5G, a MEC coordinated 5G network architecture is proposed, which demonstrates that 5G will be a network featured by the coordination of communications and multi-level computing. The proposed network architecture can support various communication modes adaptively and enable the resource sharing efficiently with virtualization technologies. Some researches have been carried out on MEC coordinated 5G, such as basic theorems related to the 5G network capacity concerning both the communication and computing resources, and key technologies including the joint optimization of communication and computing resources, multicast based on computing and cache, and bandwidth-saving transmission. It can be seen that much more efforts need to be put on MEC coordinated 5G before the network can be fully understood.
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    Standardization Progress and Case Analysis of Edge Computing
    Lü Huazhang, Chen Dan, Fan Bin, Wang Youxiang, Wu Yunxiao
    Journal of Computer Research and Development    2018, 55 (3): 487-511.   DOI: 10.7544/issn1000-1239.2018.20170778
    Abstract3151)   HTML40)    PDF (10941KB)(1627)       Save
    Edge computing is a new network architecture and open platform that integrates network, computing, storage, and application core capabilities on the edge of the network. Edge computing changes the way of traditional centralized cloud computing which moves computing and storage capabilities to the edge of the network. Edge computing can greatly reduce the congestion and burden of core network and transmission network, due to canceling the data backhaul. And it can lower the delay, bring high bandwidth. Also at the same time it can quickly respond to users’ requests and improve service quality. Now, edge computing has become an important enabling technology for the future 5G, and it has been written into 3GPP standard. More and more operators, equipment vendors and chip manufacturers join to construct the edge computing ecological. How to build a unified standardized edge computing platform for future ecological construction is very important. Therefore, this paper focuses on the progress of standardization of the current edge computing. From edge computing architecture was first proposed by ETSI, to edge computing has been listed as the key technology for future 5G development in 3GPP. The approval of projects of the edge computing in CCSA is also included. The introduction of each part has a great deal of analysis and explanation of the standard content. In the end, this paper introduces China Unicom’s edge computing research achievement in recent years, including the important edge computing experimental project, the pilot scheme for future edge computing of China Unicom and the exploration of the network deployment plan of edge computing. We are looking forward to discussing the edge computing commercial cooperation mode with all sectors so as to jointly build the network edge ecology and comprehensively accelerate the vigorous development of 5G services.
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    Application Driven Network Latency Measurement Analysis and Optimization Techniques Edge Computing Environment: A Survey
    Fu Yongquan,Li Dongsheng
    Journal of Computer Research and Development    2018, 55 (3): 512-523.   DOI: 10.7544/issn1000-1239.2018.20170793
    Abstract1757)   HTML12)    PDF (1933KB)(1113)       Save
    The technical advancements of Internet, mobile computing and Internet of things (IoT) have been pushing the deep integration of human, machine and things, which fostered a lot of end-users oriented network search, online social networks, economical business, video surveillance and intelligent assistant tools, which are typically referred to as online data-intensive applications. These new applications are of large scale and sensitive to the service quality, requiring stringent latency performance. However, end-user requests traverse heterogeneous environments including edge network, wide-area network and the data center, which naturally incurs a long-tail latency issue that significantly degrades users’ experience quality. This paper surveys architectural characteristics of edge-computing applications, analyzes causes of the long-tail latency issue, categorizes key theories and methods of the network latency measurement, summarizes long-tail latency optimization techniques, and finally proposes the idea of constructing an online optimization runtime environment and discusses some open challenges.
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    Edge Computing Application: Real-Time Anomaly Detection Algorithm for Sensing Data
    Zhang Qi, Hu Yupeng, Ji Cun, Zhan Peng, Li Xueqing
    Journal of Computer Research and Development    2018, 55 (3): 524-536.   DOI: 10.7544/issn1000-1239.2018.20170804
    Abstract2475)   HTML26)    PDF (2300KB)(1431)       Save
    With the rapid development of Internet of things (IoT), we have gradually entered into the IoE (Internet of everything) era. In face of the low quality of real-time gathering sensor data in IoT, this paper proposes a novel real-time anomaly detection algorithm based on edge computing for streaming sensor data. This algorithm firstly expresses the corresponding sensor data in the form of time series and establishes the distributed sensing data anomaly detection model based on edge computation. Secondly, this algorithm utilizes the continuity of single-source time series and the correlation between multi-source time series to detect anomaly data from streaming sensor data effectively and respectively. The corresponding anomaly detection result sets are also generated in the same process. Finally, the above two anomaly detection result sets would be effectively fused in a certain way so as to obtain more accurate detection result. In other words, this algorithm achieves a higher detection rate compared with other traditional methods. Extensive experiments on the real-world dataset of household heating data from the Jinan municipal steam heating system, which collects monitoring data from 3084 apartments of 394 buildings, have been conducted to demonstrate the advantages of our algorithm.
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    Joint Task Offloading and Base Station Association in Mobile Edge Computing
    Yu Bowen, Pu Lingjun, Xie Yuting, Xu Jingdong, Zhang Jianzhong
    Journal of Computer Research and Development    2018, 55 (3): 537-550.   DOI: 10.7544/issn1000-1239.2018.20170714
    Abstract2591)   HTML37)    PDF (3288KB)(1553)       Save
    In order to narrow the gap between the requirements of IoT applications and the restricted resources of IoT devices and achieve devices energy efficiency, in this paper we design COMED, a novel mobile edge computing framework in ultra-dense mobile network. In this context, we propose an online optimization problem by jointly taking task offloading, base station (BS) sleeping and device-BS association into account, which aims to minimize the total energy consumption of both devicesand BSs, and meanwhile satisfies applications’ QoS. To tackle this problem, we devise an online Lyapunov-based algorithm JOSA by exploiting the system information in the current time slot only. As the core component of this algorithm, we resort to the loose-duality framework and propose an optimal joint task offloading, BS sleeping and device-BS association policy for each time slot. Extensive simulation results corroborate that the COMED framework is of great performance: 1) more than 30% energy saving compared with local computing, and on average 10%-50% energy saving compared with the state-of-the-art algorithm DualControl (i.e., energy-efficiency); 2) the algorithm running time is approximately linear proportion to the number of devices (i.e., scalability).
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    Convolutional Neural Network Construction Method for Embedded FPGAs Oriented Edge Computing
    Lu Ye, Chen Yao, Li Tao, Cai Ruichu, Gong Xiaoli
    Journal of Computer Research and Development    2018, 55 (3): 551-562.   DOI: 10.7544/issn1000-1239.2018.20170715
    Abstract2402)   HTML40)    PDF (2792KB)(1761)       Save
    At present, applications and services with high computational consumption migrate gradually from centralized cloud computing center to embedded environment in the network edge. FPGA is widely used in the embedded systems under edge computing because of its flexibility and high efficiency. The conventional FPGA based convolutional neural network construction method has shortcomings, such as long design cycle and small optimization space, which leads to an ineffective exploration of the design space of targeted hardware accelerator, especially in network edge embedded environment. In order to overcome these issues, a high level synthesis based general method for convolutional neural network construction on embedded FPGA oriented edge computing is proposed. The highly reusable accelerator function is designed to construct the optimized convolutional neural network with a lower hardware resource consumption. Scalable design methodology, memory optimization and data flow enhancement are implemented on the accelerator core with HLS design strategy. The convolutional neural network is built on embedded FPGA platforms. The results show the advantage of performance and power when compared with Xeon E5-1620 CPU and GTX K80 GPU, and suitable for edge computing environment.
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    Power Optimization Based on Dynamic Content Refresh in Mobile Edge Computing
    Guo Yanchao, Gao Ling, Wang Hai, Zheng Jie, Ren Jie
    Journal of Computer Research and Development    2018, 55 (3): 563-571.   DOI: 10.7544/issn1000-1239.2018.20170716
    Abstract1338)   HTML8)    PDF (1745KB)(814)       Save
    Nowadays, with the rapid development of mobile Internet and related technologies, social applications have become one of the mainstream applications. At the same time, the functions of mobile applications are also getting richer and richer, and their energy consumption requirements and information processing capabilities are also growing. In view of the problem of high energy consumption and computing power caused by mobile social platforms ignoring network status and frequently refreshing content (words, pictures, videos, etc.), a consumption optimization model based on Markov decision process (MDP) in edge computing is proposed. The model considers the network status in different environments and performs data processing through the local edge computing layer (simulating the local edge computing mode and completing data processing) according to the current power of the mobile phone and the user refresh rate. It selects optimal strategy from the decision tables generated by the Markov decision process, and dynamically selects the best network access and refreshes the best download picture format. The model not only reduces refresh time, but also reduces the power consumption of the mobile platform. The experimental results show that compared with the picture refresh mode using a single network, the energy consumption optimization model proposed in this paper reduces the energy consumption by about 12.1% without reducing the number of user refresh cycles.
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    Web Enabled Things Computing System
    Peng Xiaohui, Zhang Xingzhou, Wang Yifan, Chao Lu
    Journal of Computer Research and Development    2018, 55 (3): 572-584.   DOI: 10.7544/issn1000-1239.2018.20170867
    Abstract1771)   HTML17)    PDF (2599KB)(916)       Save
    The rising edge computing paradigm tries to shift some computing tasks from cloud to devices recently, which reduces the computing load of cloud and traffic load of the Internet. The things computing system consists of the devices which are physical world oriented with physical functionalities. It is a great challenge to design a unified system architecture for things computing system because of the system diversity. The architecture of the modern Web system is an efficient solution for the diversity issue. However,due to the resource-constrained feature extending the Web architecture to the things computing system is also very difficult. In this paper, we first introduce the concept of edge computing system and things computing system, and summarize the challenges brought by diversity and resource-constrained features of things computing system. Then, a detailed study of the state-of-the-art technologies, including REST principle, script languages and debugging technique for extending the Web to things computing system, is presented. Most of the related work tried to modify the “Uniform Interface” principle to adapt to edge system. We conclude from the examined literature that things computing system is a massive market, but there is still no unified system architecture which supports both the Web and intelligence. Finally, we present some future research directions for things computing system including the unified system architecture, efficient Web technologies, supporting intelligence and debugging techniques.
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