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International Conference on Software Engineering (ICSE)

作者： Hengbiao Yu Zhenbang Chen Xianjin Fu Ji Wang Zhendong Su Jun Sun Chun Huang Wei Dong National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China ETH Zurich Switzerland School of Information Systems Singapore Management University Singapore

ISBN: (数字)9781450371216

ISBN: (纸本)9781728165196

Message passing is the standard paradigm of programming in high-performance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV and evaluated it with 111 real-world MPI verification tasks. The pure symbolic execution-based technique successfully verifies 61 out of the 111 tasks (55%) within one hour, while in comparison, MPI-SV verifies 100 tasks (90%). On average, compared with pure symbolic execution, MPI-SV achieves 19x speedups on verifying the satisfaction of the critical property and 5x speedups on finding violations.

关键词： Message passing Model checking Tools Task analysis Tuning Standards Software engineering

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A Distributed Control Method Based on Neighbor Reward for Robot Swarm

A Distributed Control Method Based on Neighbor Reward for Ro...

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作者： Linlin Du Yuhong Huang YUHUA TANG Da Huang The State Key Laboratory of High Performance Computing National University of Defense Technology College of Computer National University of Defense Technology

Real-time control strategy development remains challenging in the field of controlling multi-robot *** to frequent and massive interactions among robots,it is hard to implement a realtime control strategy in the multi-robot *** phenomenon further limits the development of multi-robot application which adopts the real-time control *** deal with above challenges,this paper proposes a decentralized control policy for multi-robot to avoid collision in an unknown *** policy adopts reward value to measure robots' motion and *** reward value,robots can determine which neighbor robot to communicate with and to learn from,which decreases the interaction cost incurred in the multi-robot *** the reward,the control strategy of each robot can be optimized through learning from its *** experiments are conducted in a simulator to evaluate the effectiveness of our policy.

关键词： Multi-robot systems Swarm robotics Self-organization

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Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

arXiv

引用

arXiv 2024年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. Sun, Q.N. Sun, X.N. Sun, Z.B. Takata, J. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Guangdong Zhuhai519000 China School of Physics Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technolo

We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3−30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15 PeV. Below the knee, the spectral index is found to be −2.7413±0.0004± 0.0050, while above the knee, it is −3.128 ± 0.005 ± 0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of −0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components. © 2024, CC BY.

关键词： Cosmic rays

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The YAG Lidar System Applied in LHAASO 37

The YAG Lidar System Applied in LHAASO

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Sun, Q.N. Geng, L.S. Li, X. Chen, L. Liu, H. Wang, Y. Zhu, F.R. Zhang, Y. Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica dell’Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo

The atmospheric quality plays an important role in the air shower observation by the Wide Field-of-view Cherenkov Telescope Array (WFCTA) of LHAASO. A YAG imaging lidar system was developed to continuously monitor the calorimetric information. The accuracy of atmospheric monitoring is dependent on the pulse energy, the YAG laser’s beam parameters and the angular repeatability of a high-precision 3D lifting Rotating Platform (HiRoP). Therefore, we designed an optical system for this lidar with a beam splitter to divide the laser beam into a reference beam and a calibrating beam with a certain ratio and coupled the beam paths with the movement of HiRoP. Thus, every pulse energy of the calibrating beam, which has the same energy fluctuation with respect to the reference beam recorded by a power meter, could be calculated by the ratio of the two beams. Great cares were also taken to characterize the beam size, polarization and divergence of the laser. Meanwhile, a high-precision home-made thermotank was designed to control the temperature and humidity to improve the performance and stability of our laser system, which resulting in a thermal fluctuation less than 2 ◦C inside the container in the winter at an altitude of 4410 m. As a result, the pulse energy fluctuation of the laser beam for calibration was improved from 5 % to less than 2 %. As a result, we have successfully attained distinguishable full-WFCTA-view scanning Laser images in different air conditions, which could be used for the atmospheric quality analysis in further. © Copyright owned by the author(s).

关键词： Yttrium aluminum garnet

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A benchmark test of boson sampling on Tianhe-2 supercomputer

引用

National science Review 2018年第5期5卷 715-720页

作者： Junjie Wu Yong Liu Baida Zhang Xianmin Jin Yang Wang Huiquan Wang Xuejun Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and AstronomyShanghai Jiao Tong University Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China

Boson sampling, thought to be intractable classically, can be solved by a quantum machine composed of merely generation, linear evolution and detection of single photons. Such an analog quantum computer for this specific problem provides a shortcut to boost the absolute computing power of quantum computers to beat classical ones. However, the capacity bound of classical computers for simulating boson sampling has not yet been identified. Here we simulate boson sampling on the Tianhe-2 supercomputer, which occupied the first place in the world ranking six times from 2013 to 2016. We computed the permanent of the largest matrix using up to 312 000 CPU cores of Tianhe-2, and inferred from the current most efficient permanent-computing algorithms that an upper bound on the performance of Tianhe-2 is one 50-photon sample per ～100 min. In addition, we found a precision issue with one of two permanent-computing algorithms.

关键词： boson sampling permanent quantum supremacy quantum computing supercomputer

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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A holistic stream partitioning algorithm for distributed stream processing systems 20

A holistic stream partitioning algorithm for distributed str...

引用

20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Li, Kejian Liu, Gang Lu, Minhua Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781728126166

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions. © 2019 IEEE.

关键词： Distributed parameter control systems

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Quantum photonic network on chip

引用

Chinese Physics B 2018年第5期27卷 59-73页

作者： Qun-Yong Zhang Ping Xu Shi-Ning Zhu National Laboratory of Solid State Microstructures and School of Physics Nanjing University Nanjing 210093 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

We provide an overview of quantum photonic network on chip. We begin from the discussion of the pros and cons of several material platforms for engineering quantum photonic chips. Then we introduce and analyze the basic building blocks and functional units of quantum photonic integrated circuits. In the main part of this review, we focus on the generation and manipulation of quantum states of light on chip and are particularly interested in some applications of advanced integrated circuits with different functionalities for quantum information processing, including quantum communication, quantum computing, and quantum simulation. We emphasize that developing fully integrated quantum photonic chip which contains sources of quantum light, integrate circuits, modulators, quantum storage, and detectors are promising approaches for future quantum photonic technologies. Recent achievements in the large scale photonic chips for linear optical computing are also included. Finally, we illustrate the challenges toward high performance quantum information processing devices and conclude with promising perspectives in this field.

关键词： quantum photonic chip entanglement production and manipulation quantum communication quantum computing

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An Actor-based Programming Framework for Swarm Robotic Systems

An Actor-based Programming Framework for Swarm Robotic Syste...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Wei Yi Bin Di Ruihao Li Huadong Dai Xiaodong Yi Yanzhen Wang Xuejun Yang Artificial Intelligence Research Center (AIRC) National Innovation Institute of Defense Technology (NIIDT) Beijing China Tianjin ArtiFIcial Intelligence Innovation Center (TAIIC) Tianjin China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changesha China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Programming cooperative tasks for autonomous swarm robotic systems has always been challenging. In this paper, we introduce a concept `Actor', as a virtualization for robot platforms. Every robot platform in the swarm robotic system carries out the task and interacts with others as an Actor. We designed an Actor-based framework for the management of autonomous swarm robotic systems including modules and interfaces for the Actor, the collective Actor, and task management. The Actor-based framework enables task developers to explicitly model cooperative tasks without intricacies about the detailed robotic algorithms or the specific robot brands, and eases the burden on robotic algorithm developers by providing common functionalities. The proposed framework is implemented in C++ and validated quantitatively and qualitatively with a swarm of thirty drones by simulations and a swarm of ten drones by in-field tests.

关键词： Swarm robotics C++ languages Programming Task analysis Virtualization Intelligent robots Drones

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Dynamic service deployment for budget-constrained mobile edge computing

Dynamic service deployment for budget-constrained mobile edg...

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作者： Zhou, Jingya Fan, Jianxi Wang, Jin Jia, Juncheng School of Computer Science and Technology Soochow University Suzhou China College of Computing Georgia Institute of Technology Atlanta China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Currently, Mobile edge computing (MEC) is facing a great challenge that is how to make full use of edge resources to provide a seamless support for compute-intensive latency-sensitive applications. Prior studies often make a simple assumption that tasks can be executed upon every edge server, but the assumption does not hold in practical scenarios. Because a specific application task often corresponds to a certain service that provides the corresponding running environment, whereas an edge server only has limited resources and cannot offer too many services. How to decide service deployment of so many types of services among multiple edge servers is also a big challenge. To address the challenge, we study dynamic service deployment for latency-sensitive applications. We first model the long-term budget-constrained latency minimization problem as a multi-slot latency minimization problem based on the Lyapunov framework. By doing this, the hardness of a problem is significantly reduced, since we never require future information to solve the long-term optimization. Furthermore, we extend our study by joining the task scheduling optimization, where every edge server is fully utilized in an even more efficient collaborative manner. Our extensive experiments show that the proposed algorithms can bring short latency with low cost. © 2019 John Wiley & Sons, Ltd.

关键词： Mobile edge computing

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