Prof. Juan Fang
Beijing University of Technology
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Academic Career Length
16Years (2011-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications7 July 2020 in IEEE Internet of Things Journal
Journal Article
Juan FangAMAonan Ma
Aonan Ma
In today’s era of Internet of Things (IoT), efficient and real-time processing of massive data generated by IoT device has become the primary issue for traditional cloud computing network architectures. As a supplement of cloud computing, edge computing enhances the real-time performance of service completion by offloading services to edge servers closer to the terminal device for execution, while reducing power consumption and computing load in the cloud. In this paper, we propose the following solutions to resolve the different requests of the IoT device: In an “edge-cloud” heterogeneous network environment, create a mapping scheme between application modules and basic resource equipment, considering the two factors of tolerant task latency and system power consumption; In the application step-by-step execution process, heuristic dynamic task processing algorithm is used to reduce the task latency time. Experiments with the “iFogSim” simulator show that, application service quality is significantly improved and system power consumption is greatly reduced, which comparing with the stable application module placement strategy and the static task scheduling strategy.
26 September 2023 in IEEE Transactions on Network and Service Management
Journal Article
The rapid advancement of mobile edge computing (MEC) networks has enabled the augmentation of the computational power of mobile devices (MDs) by offloading computationally intensive tasks to resource-rich edge nodes. This paper discusses the decision-making process for task offloading and resource allocation among multiple mobile devices connected to a base station. The primary objective is to minimize the time taken to complete tasks while simultaneously reducing energy consumption on the device under a time-varying wireless fading channel. This objective is formulated as an energy-efficiency cost (EEC) minimization problem, which cannot be solved by conventional methods. To address this challenge, we propose a dynamic offloading decision algorithm of dependent tasks (DODA-DT) that adjusts local task execution based on edge node status. The proposed algorithm facilitates fair competition among all devices for edge resources. Additionally, we use a deep reinforcement learning (DRL) algorithm based on an actor-critic learning structure to train the system to quickly identify near-optimal solutions. Numerical simulations demonstrate that the proposed algorithm effectively reduces the total cost of the task in comparison to previous algorithms.
28 June 2022 in Pattern Recognition
Journal Article
LYLu Yu
Lu Yu
Wei XiangJuan Fang
Yi-Ping Phoebe ChenRZRuifeng Zhu
Ruifeng Zhu
Journal Article
Juan FangJSJiamei Shi
Jiamei ShiSLShuaibing Lu
Shuaibing LuMZMengyuan Zhang
Mengyuan ZhangZYZhiyuan Ye
Zhiyuan YeWith the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.
22 November 2023 in IEEE Transactions on Services Computing
Journal Article
SLShuaibing Lu
Shuaibing LuJWJie Wu
Jie WuPLPengfan Lu
Pengfan Lu
NWNing Wang
Ning WangHLHaiming Liu
Haiming LiuJuan FangThe vigorous development of IoT technology has spawned a series of applications that are delay-sensitive or resource-intensive. Mobile edge computing is an emerging paradigm that provides services between end devices and traditional cloud data centers to users. However, with the continuously increasing investment of demands, it is nontrivial to maintain a higher quality-of-service (QoS) under the erratic activities of mobile users. In this paper, we investigate the service provisioning and updating problem under the multiple-users scenario by improving the performance of services with long-term cost constraints. We first decouple the original long-term optimization problem into a per-slot deterministic one by using Lyapunov optimization. Then, we propose two service updating decision strategies by considering the trajectory prediction conditions of users. Based on that, we design an online strategy by utilizing the committed horizon control method looking forward to multiple slots predictions. We prove the performance bound of our online strategy theoretically in terms of the trade-off between delay and cost. Extensive experiments demonstrate the superior performance of the proposed algorithm.
21 August 2015 in The Journal of Supercomputing
Journal Article
As the very large-scale integrated circuit designs enter the deep sub-micron era, many-core processors are regarded as promising architectures to keep up with the Moore’s law. To provide effective communications between the on-chip components, network-on-chip was proposed as a new paradigm that exhibits better scalability than the traditional buses. There have been previous researches on application mappings to reduce the power consumption, the network latency and the network area overhead. However, some of the previous proposed algorithms such as the Kernighan–Lin algorithm (KL) and some genetic algorithms (GA) have the problem of finding the local best result instead of a global optimal solution. In this paper, we propose a novel application mapping algorithm for the mesh-of-tree network topology, called KL_GA algorithm. Our proposed algorithm takes the advantage of both the Kernighan–Lin algorithm and genetic algorithms to reduce the overall communication cost. Our KL_GA algorithm first generates a mapping solution using a KL-based method. In order to avoid the appearance of premature phenomena, we next apply a GA-based algorithm to get rid of the population trapped in the local optimum and re-generate a new population. Our evaluations show that, compared to the random mapping algorithm, our KL_GA algorithm saves the power by 21.6 % and reduces the network latency by 16.3 % on the average.
About Me
Fang Juan, Professor and Doctoral supervisor, is currently the Deputy Director of the Faculty of Information Technology of Beijing University of Technology, the Director of the Institute of Computer Architecture of the College of Computer Science, and the Head of the Internet of Things Engineering. She is an IEEE member, member of China Computer Federation (CCF), member of Chinese Institute of Electronics, member of CCF high-performance computing Technical Committee, member of CCF computer architecture Technical Committee, and member of CCF Internet of Things Technical Committee. She has won the titles of Excellent Talent and the Middle-aged and Young Teacher of Beijing. In 2005 and 2012, she was twice funded by the China Scholarship Council (CSC) for academic exchanges in the United States for two years.
Mainly engaged in theoretical and applied research work in high-performance computing, edge computing, and big data processing technology, she has presided over a National Natural Science Foundation of China project and a Beijing Natural Science Foundation project, as well as many other projects. She has published more than 100 research papers in international major journals and important international academic conferences. Among them, more than 80 articles were cited by SCI and EI. Her work has led to 26 national invention patents, 34 software copyrights, 11 textbooks, 2 translation books, 3 national standards, and 1 patent transformation.