基本信息Biographical SketchResearch GrantsSelected PublicationsTeaching招生我辈岂是蓬蒿人
News

新闻标题[Congrats] In 2018, our paper "A new method of priority assignment for real-time flows in the wirelessHart network by the TDMA Protocol" has been accepted by Sensors . (SCI Impact factor 2.475)

发表时间2019-03-07

WirelessHART is a wireless sensor network that is widely used in real-time demand analyses. A key challenge faced by WirelessHART is to ensure the character of real-time data transmission in the network. Identifying a priority assignment strategy that reduces the delay in flow transmission is crucial in ensuring real-time network performance and the schedulability of real-time network flows. We study the priority assignment of real-time flows in WirelessHART on the basis of the multi-channel time division multiple access (TDMA) protocol to reduce the delay and improve the ratio of scheduled. We provide three kinds of methods: (1) worst fit, (2) best fit, and (3) first fit and choose the most suitable one, namely the worst-fit method for allocating flows to each channel. More importantly, we propose two heuristic algorithms—a priority assignment algorithm based on the greedy strategy for C (WF-C) and a priority assignment algorithm based on the greedy strategy for U(WF-U)—for assigning priorities to the flows in each channel, whose time complexity is O(max(N?m?log(m),(N?m)2)) . We then build a new simulation model to simulate the transmission of real-time flows in WirelessHART. Finally, we compare our two algorithms with WF-D and HLS algorithms in terms of the average value of the total end-to-end delay of flow sets, the ratio of schedulable flow sets, and the calculation time of the schedulability analysis. The optimal algorithm WF-C reduces the delay by up to 44.18% and increases the schedulability ratio by up to 70.7% , and it reduces the calculation time compared with the HLS algorithm.

https://doi.org/10.3390/s**


新闻标题[Congrats] In 2018, our paper "An adaptive heuristic for managing energy consumption and overloaded hosts in a cloud data center" has been accepted by Wireless Networks. (SCI Impact factor 1.981)

发表时间2018-11-23

In this paper, we address the problems of massive amount of energy consumption and service level agreements (SLAs) violation in cloud environment. Although most of the existing work proposed solutions regarding energy consumption and SLA violation for cloud data centers (CDCs), while ignoring some important factor: (1) analysing the robustness of upper CPU utilization threshold which maximize utilization of resources; (2) CPU utilization prediction based VM selection from overloaded host which reduce performance degradation time and SLA violation. In this context, we proposed adaptive heuristic algorithms, namely least medial square regression for overloaded host detection and minimum utilization prediction for VM selection from overloaded hosts. These heuristic algorithms reducing CDC energy consumption with minimal SLA. Unlike the existing algorithms, the proposed VM selection algorithm consider the types of application running and it CPU utilization at different time periods over the VMs. The proposed approaches are validated using the CloudSim simulator and through simulations for different days of a real workload trace of PlanetLab.

https://doi.org/10.1007/s11276-018-1874-1


新闻标题[Congrats] In 2018, our paper "Automatic generation of benchmarks for I/O-intensive parallel applications" has been accepted by Journal of Parallel and Distributed Computing. (SCI Impact factor 1.815)

发表时间2018-11-1

The benchmarks of I/O-intensive parallel applications are important for evaluating and optimizing HPC softwares and hardwares. However, extracting high-fidelity benchmarks which can fully reflect computation, communication, and I/O behaviors of original I/O-intensive parallel applications is very difficult. This work contributes a framework which can automatically generate benchmarks for I/O-intensive parallel applications. We demonstrate our framework on Taub and TianHe-2 supercomputers with five NAS Parallel Benchmarks (NPB) and four I/O-intensive parallel applications. The results show that our trace merging algorithm and trace compressing algorithm are better than others, and the generated benchmarks can accurately mimic the computation, communication, and I/O behaviors of original I/O-intensive parallel applications. Also, these generated benchmarks can be used to predict the performance of original applications, while reducing the prediction overhead by scaling down the execution time of benchmark proportionally.

https://doi.org/10.1016/j.jpdc.2018.10.004


新闻标题[Congrats] In 2018, our paper "Adaptive Energy-aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing" has been accepted by IEEE ACCESS. (SCI Impact factor 3.557)

发表时间2018-10-04

In cloud computing, high energy consumption and Service Level Agreements (SLAs) violation are challenging issues considering the demand of computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focus on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider the current network traffic cause performance degradation thus may not really reduce SLA violation under a variety of workloads. In this context,this paper proposes three adaptive models, namely, gradient descent based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation.

https://doi.org/10.1109/ACCESS.2018.**


新闻标题[Congrats] In 2018, our paper "Performance modeling for MPI applications with low overhead fine-grained profiling" has been accepted by Future Generation Computer Systems. (SCI Impact factor 4.639)

发表时间2018-08-16

MPI applications have been widely used in the scientific computing and cloud computing fields. Understanding how these applications will scale on HPC and cloud platforms is essential for users and system designers. However, achieving this task is difficult because of the complexity of applications and systems. In this work, we propose an automatic, fine-grained profiling approach based on linear regression. Different from those in previous studies, our approach profiles MPI applications at the basic block level. Using this fine-grained profiling level, we can provide users with detailed information on how each part of the application will scale on hundreds or thousands of cores. We can also determine the scalability limit. Additionally we use two methods to reduce the profiling cost to less than 50% of the runtime of the original application. We test our approach on TianHe-2, which is ranked number 2 on the Top500 list as of November 2017, and Taub clusters, which is developed by UIUC. The median prediction errors of our approach are 8% and 13% for two NPB benchmarks and two real applications, respectively. We also compare our approach with PEMOGEN. The results show that our approach is more accurate on large process counts.
https://doi.org/10.1016/j.future.2018.08.018


新闻标题[Congrats] In 2018, our paper "An Efficient and Secured Framework for Mobile Cloud Computing" has been accepted by IEEE Transactions on Cloud Computing. (SCI Impact factor 7.928)

发表时间2018-06-20

Smartphone devices are widely used in our daily lives. However, these devices exhibit limitations, such as short battery lifetime, limited computation power, small memory size and unpredictable network connectivity. Therefore, numerous solutions have been proposed to mitigate these limitations and extend the battery lifetime with the use of the offloading technique. In this paper, a novel framework is proposed to offload intensive computation tasks from the mobile device to the cloud. This framework uses an optimization model to determine the offloading decision dynamically based on four main parameters, namely, energy consumption, CPU utilization, execution time, and memory usage. In addition, a new security layer is provided to protect the transferred data in the cloud from any attack.

https://doi.org/10.1109/TCC.2018.**


Basic Information

Weizhe (James) Zhang (张伟哲)

Professor, Ph.D. Supervisor (教授 博导)

Research Center of Computer Network and Information Security Technology

School of Computer Science and Technology

Harbin Institute of Technology (Harbin) & (Shenzhen)

Education
Ph.D. (2001-2006)Computer Science, Harbin Institute of Technology (HIT), China
M.S. (1999-2001) Computer Science, Harbin Institute of Technology (HIT), China
B.Sc. (1995-1999)Computer Science, Harbin Institute of Technology (HIT), China


Career
Visiting Professor (2013-2014)

With Prof. Marc Snir, Department of Computer Science, UIUC, USA
Ph.D. Supervisor (2012 -)

School of Computer Science and Technology, Harbin Institute of Technology, China
Associate Professor (2007 - 2012)

School of Computer Science and Technology, Harbin Institute of Technology, China
Post-Doctoral (2007-2010)

School of Electronics and Information Engineering, Harbin Institute of Technology, China
Visiting Scholar (2005-2006)

Department of Computer Science, University of Houston, USA
Lecturer (2003-2007)
School of Computer Science and Technology, Harbin Institute of Technology, China



Contact

Phone:86-**
Fax:86-**
E-mail:wzzhang AT hit DOT edu DOT cn
Office:Room 708, Zonghe Building,Harbin Institute of Technology, Harbin, Heilongjiang, China.
Address:P.O.Box 320, No.92 West Dazhi Street, Nangang District, Harbin Institute of Technology, Harbin, Heilongjiang, China. 150001


News 2017

新闻标题[Congrats] In 2017, our paper "MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing" has been accepted by Wireless Communications and Mobile Computing.(SCI Impact factor 1.898)

发表时间2017-12-18

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.
https://doi.org/10.1155/2017/**


新闻标题[Congrats] In 2017, our paper "Linear and dynamic programming algorithms for real-time task scheduling with task duplication" has been accepted by Journal of Suprecomputing.(SCI Impact factor 1.088)

发表时间2017-05-26

A real-time task scheduling system model was analyzed under a heterogeneous multiprocessor platform with task duplication. This analysis focused on the designs and performances of linear and dynamic programming algorithms for realtime task scheduling under a heterogeneous platform with task duplication. Moreover, experimental analyses were performed to evaluate the performances of different algorithms under different conditions. The advantages of the two proposed algorithms were compared under the same situations to discover which one achieves a higher task scheduling efficiency for a heterogeneous real-time system.

http://dx.doi.org/10.1007/s11227-017-2076-9


新闻标题[Congrats] In 2017, our paper "Predicting HPC parallel program performance based on LLVM compiler" has been accepted by Cluster Computing.(SCI Impact factor 1.514)

发表时间2017-01-01

Performance prediction of parallel program plays key roles in many areas, such as parallel system design, parallel program optimization, and parallel system procurement. Accurate and efficient performance prediction on large-scale parallel systems is a challenging problem. To solve this problem, we present an effective framework for performance prediction based on the LLVM compiler technique in this paper. We can predict the performance of a parallel program on a small amount of nodes of the target parallel system using this framework toned but not execute this parallel program on a corresponding full-scale parallel system. This framework predicts the performance of computation and communication components separately and combines the two predictions to achieve full program prediction. As for sequential computation, we first combine the static branch probability and loop trip count identification and propose a new instrumentation method to acquire the number of each instruction type. We then construct a test program to measure the average execution time of each instruction type. Finally, we utilize the pruning technique to convert a parallel program into a corresponding sequential program to predict the performance on only one node of the target parallel system. As for communication, we utilize the LogGP model to model point-to-point communication and the artificial neural network technique to model collective communication. We validate our approach by a set of experiments that predict the performance of NAS parallel benchmarks and CGPOP parallel application. Experimental results show that the proposed framework can accurately predict the execution time of parallel programs, and the average error rate of these programs is 10.86%.

http://dx.doi.org/ 10.1007/s10586-016-0707-1


新闻标题[Congrats] In 2016, our paper "Trustworthy Enhancement for Cloud Proxy based on Autonomic Computing" has been accepted by IEEE Transactions on Cloud Computing

发表时间2016-11-2

Aiming to improve Internet content accessing capacity of the system, cloud proxy platforms are used to improve the visiting performance in network export environment. Limited by complexity of cloud proxy system, trustworthy guarantee of cloud system becomes a difficult problem. Considering the self-government of autonomic computing, it could enhance cloud system trustworthy and avoids system management security and reliable problems brought by complex construction. Based on the idea of self-supervisory, a mechanism to enhance security of cloud system was proposed in this paper. Firstly, a trustworthy autonomous enhancement framework for virtual machines was proposed. Secondly, a method to extract linear relationship of monitoring items in the virtual machine based on ARX model was put forward. According to the mapping relation between monitoring items and system modules, an abnormal module positioning technology based on Naive Bayes classifier was developed to realize self-sensing of abnormal system conditions. Finally, security threats of virtual machines including malicious dialogue and buffer memory of hot attacks were tested through experiments. Results showed that the proposed trustworthy enhancement mechanism of virtual machines based on autonomic computing could achieve trustworthy enhancement of virtual machines effectively and provide an effective safety protection for the cloud system.

http://dx.doi.org/10.1109/TCC.2016.**


新闻标题[Congrats] In 2016, our paper "Android platform-based individual privacy information protection system" has been accepted by Personal and Ubiquitous Computing (SCI Impact factor 1.498)

发表时间2016-09-17

With the popularity of mobile phones with Android platform, Android platform-based individual privacy information protection has been paid more attention to. In consideration of individual privacy information problem after mobile phones are lost, this paper tried to use SMS for remote control of mobile phones and providing comprehensive individual information protection method for users and completed a mobile terminal system with self-protection characteristics. This system is free from the support of the server and it can provide individual information protection for users by the most basic SMS function, which is an innovation of the system. Moreover, the protection mechanism of the redundancy process, trusted number mechanism and SIM card detection mechanism are the innovations of this system. Through functional tests and performance tests, the system could satisfy user functional and non-functional requirements, with stable operation and high task execution efficiency.

http://dx.doi.org/10.1007/s00779-016-0966-0



新闻标题[Congrats] In 2016, our paper "Network-aware Virtual Machine Migration in an Overcommitted Cloud" has been accepted by Future Generation Computer Systems (SCI Impact factor 2.78)

发表时间2016-04-04

Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention.

However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center.

This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs.

The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem.

http://dx.doi.org/10.1007/s00779-016-0966-0.

Short CV
Dr. Weizhe Zhang is currently a Professor and Ph.D. Supervisor in the School of Computer Science and Technology, Harbin Institute of Technology, Harbin & Shenzhen,China. He received his B.Eng, M.Eng and Ph.D. degree of Engineering in computer science and technology in 1999, 2001 and 2006 respectively from Harbin Institute of Technology. He has been a visiting professor at the Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC), USA, from Aug. 2013 to Aug 2014. He has been a visiting scholar at the Department of Computer Science, University of Houston (UH), USA, from Aug. 2005 to Feb 2006.



He is the Associate Editor or Editorial Board of International Journal of Grid and Distributed Computing (IJGDC), International Journal of Future Generation Communication and Networking (IJFGCN), International Journal of Hybrid Information Technology (IJHIT), International Journal of Security and Its Applications (IJSIA), International Journal of Grid and High Performance Computing(IJGHPC).He serves as the Guest Editor of Future Generation Computer Systems(FGCS), Microprocessors and Microsystems(Micro), International Journal of Grid and Distributed Computing (IJGDC). He has served as the General Chair of Workshop on Networking and Communication 2014 Fourth.He has served as the PC Chair of International Conference on Big Data Intelligence and Computing (DataCom 2015). He has served as the Session Chair of the 2013 International Conference on Computer and Applications (CCA 2013), the 2012 IEEE International Conference on Cluster Computing (Cluster 2012), and the 7th IFIP International Conference on Network and Parallel Computing (NPC 2010). He has served as PC member for a number of conferences, e.g., IEEE International Conference on Pervasive Intelligence and Computing (PICom-2015), the 11th IEEE International Conference on Embedded Software and Systems(ICESS 2014), the Second International Workshop on Real Time and Embedded Systems, the IFIP International Conference on Network and Parallel Computing (NPC 2011 and NPC 2012). He also serves as a peer reviewer of IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Systems, Journal of Information Science and Engineering, International Journal of Information Technology & Decision Making.



Dr. Zhang has published around 100 scientific papers in the well-established journals including IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, Journal of Supercomputing, Sensors, Computing, Multimedia Tools and Applications, Studies in Informatics and Control, Science in China (Series F), IEICE Transaction on Information and Systems,中国科学, 软件学报，计算机学报, 计算机研究与发展, 电子学报 and in the reputable conferences such as IEEE CLUSTER, IEEE IPDPS, IEEE ICPADS, ACM CIKM, IFIP NPC etc. He conducts research in high performance computing, parallel and distributed system, cloud computing, real-time computing and computer network&security. He is a member of the IEEE , ACM, IEICE and CCF (China Computer Federation).



Research Interests
High Performance Computing Parallel and Distributed System Cloud Computing Real-time Computing Computer Networks & Security


Research Grants
01. 2017 - 12. 2020Real-time Task Scheduling Algorithm and Schedulability Analysis on Embedded System.(No.**).National Nature Science Foundation China （NSFC）, PI

07. 2016 - 06, 2019Rapid and Fexible Construction of Large-Scale High-Fidelity Target Network.(2016YFB**). National Key R&D Program of China, PI

01. 2014 – 12. 2016High Efficiency Public Opinion Crawling Algorithm based on Content Addressable Network (No.**037). Doctoral Program of Higher Education of China (RFDP)), PI.

01. 2012 – 12. 2015Efficient Distributed Information Crawling for Internet Public Opinions (No. **), National Nature Science Foundation China (NSFC), PI.

01. 2012 – 12. 2015Performance Prediction of Parallel Applications in Single-Chip Cloud Environment. Intel-MAR, PI.

01. 2011 - 12. 2015. Efficient and Dependable Virtual Computing Environment (No. 2011CB302605), National Basic Research Program of China (973), Member.

01. 2008 – 12. 2010Multisite Co-allocation Algorithms in Computing Grid Environment (No. **), National Nature Science Foundation China (NSFC), PI.

01. 2006 – 12. 2010Aggregation and Coordination in Virtual Computing Environment (No. 2005CB321806), National Basic Research Program of China (973), Member,

01. 2006 – 12. 2008. Bioinformatics System and Application based on Computing Grid (No. 2006AA02Z334), National High TechnologyResearch and Development Program of China (863), Member.

01. 2004 – 12. 2006 Resource Management and Task Scheduling Computing Grid Environment (No. **), National Nature Science Foundation China (NSFC), Co-PI.

Journals




[TCC@19] Ibrahim Elgendy, Weizhe Zhang, Chuanyi Liu, Ching-Hsien Hsu. " An Efficient and Secured Frame work for Mobile Cloud Computing".IEEE Transactions on Cloud Computing. 2019. (accepted)

http://dx.doi.org/10.1109/TCC.2018.** (SCI Impact factor 7.928)



[JPDC@19] Meng Hao, Weizhe Zhang, You Zhang, Marc Snir, Laurence T. Yang. Automatic generation of benchmarks for I/O-intensive parallel applications, Journal of Parallel and Distributed Computing, 2019 (accepted)

https://doi.org/10.1016/j.jpdc.2018.10.004 (SCI Impact factor 1.815)



[FGCS@19] Gangzhao Lu, Weizhe Zhang, Hui He, Laurence T.Yang. " Performance modeling for MPI applications with low overhead fine-grained profiling". Future Generation Computer Systems. 2019. (accepted)

https://doi.org/10.1016/j.future.2018.08.018 (SCI Impact factor 4.639)



[WN@19] Rahul Yadav, Weizhe Zhang, Keqin Li, Chuanyi Liu, Muhammad Shafiq, Nabin Kumar Karn. An adaptive heuristic for managing energy consumption and overloaded hosts in a cloud data center. Wireless Networks. 2019(accepted)

https://doi.org/10.1007/s11276-018-1874-1 (SCI Impact factor 1.981)



[JoS@19] Weizhe Zhang, Yao Hu, Hui He, Yawei Liu, Allen Chen. "Linear and dynamic programming algorithms for real-time task scheduling with task duplication".Journal of Supercomputing. 2019, 75(2), 494-509 http://dx.doi.org/10.1007/s11227-017-2076-9(SCI Impact factor 1.088)



[Sensors@18] Yulong Wu, Weizhe Zhang, Hui He, Yawei Liu. A new method of priority assignment for real-time flows in the wirelessHart network by the TDMA Protocol. Sensors 2018, 18(12), 4242.
https://doi.org/10.3390/s**(SCI Impact factor 2.475)



[SCN@18] Huanran Wang, Hui He, Weizhe Zhang. “Demadroid: Object Reference Graph-Based Malware Detection in Android”.Security and Communication Networks, vol. 2018, Article ID **, 16 pages, 2018. https://doi.org/10.1155/2018/**/ (SCI Impact factor 1.067)
[TCC@17] Weizhe Zhang, Hucheng Xie, Ching-Hsien Hsu. "Automatic Memory Control of Multiple Virtual Machines on a Consolidated Server". IEEE Transactions on Cloud Computing. 2017, 5(1), 2-14

http://dx.doi.org/10.1109/TCC.2014.**



[Cluster Comput. @17] Weizhe Zhang, Meng Hao, Marc Snir. Predicting HPC parallel program performance based on LLVM compiler. Cluster Computing. 2017, 20(2), 1179-1192http://dx.doi.org/10.1007/s10586-016-0707-1 (SCI Impact factor 1.514)
[FGCS@17] Weizhe Zhang, Shuo Han, Hui He, Huixiang Chen. "Network-aware Virtual Machine Migration in an Overcommitted Cloud". Future Generation Computer Systems. 2017, 76(11), 428-442. http://dx.doi.org/10.1016/j.future.2016.03.009(SCI Impact factor 2.78)
[Pers Uniquit Comput@16] Weizhe Zhang, Xiong Li, Naixue Xiong, Athanasios V. Vasilakos. "Android Platform-based Individual Privacy Information Protection System". Personal and Ubiquitous Computing. 2016, 20, 875-884 http://dx.doi.org/10.1007/s00779-016-0966-0. (SCI Impact factor 1.498)
[JoS@16] Weizhe Zhang, Meng Hao, Zhiyong Xu. "Communication Optimization for RDMA-based Science Data Transmission Tools".Journal of Supercomputing. 2016, 72(9), 3312-3327http://dx.doi.org/ 10.1007/s11227-015-1399-7 (SCI Impact factor 0.858)
[JoS@16] Weizhe Zhang, Gangzhao Lu, Hui He, Qizhen Zhang, Chuanliang Yu. "Exploring Large-Scale Small File Storage for Search Engines". Journal of Supercomputing. 2016, 72(8), 2911-2923http://dx.doi.org/10.1007/s11227-015-1394-z(SCI Impact factor 0.858)
[TC@16] Weizhe Zhang, Albert M.K. Cheng, Jaspal Subhlok. "DwarfCode: A Performance Prediction Tool for Parallel Applications". IEEE Transactions on Computers. 2016, 65(2), 495-507http://dx.doi.org/10.1109/TC.2015.**(SCI Impact factor 1.659)
[J Sensors@16 ] Weizhe Zhang, Boyu Song, and Enci Bai. "A Trusted Real-Time Scheduling Model for Wireless Sensor Networks". Journal of Sensors. Volume 2016 (2016), Article ID **, 8 pages. http://dx.doi.org/10.1155/2016/** (SCI Impact factor 1.182)
[MTAP@15] Weizhe Zhang, Hui He, Tai-hoon Kim. "Xen-based Virtual Honeypot System for Smart Device". Multimedia Tools and Applications. 2015 , Volume 74, Issue 19, pp 8541-8558http://dx.doi.org/10.1007/s11042-013-1499-4 (SCI Impact factor 1.346)
[Sensors@15] Weizhe Zhang, Enci Bai, Hui He, Albert M. K. Cheng. "Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms". Sensors. 2015, 15, 13778-13804. http://dx.doi.org/10.3390/s**(SCI Impact factor 2.245)
[MPE@14] Weizhe Zhang, Hucheng Xie, Boran Cao, and Albert M. K. Cheng. "Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm". Mathematical Problems in Engineering. Volume 2014 (2014), Article ID 287475, 9 pages. http://dx.doi.org/10.1155/2014/287475(SCI Impact factor 0.762)



[NCA@14] Weizhe Zhang, Hui He, Boran Cao. "Identifying and Evaluating the Internet Opinion Leader Community based on K-Clique Clustering". Neural Computing and Applications. 2014, 25(3):595-602. http://dx.doi.org/10.1007/s00521-013-1529-1(SCI Impact factor 1.569)
[COMPUTING@14]Weizhe Zhang, You Zhang, Tai-hoon Kim. "Detecting bad information in mobile wireless networks based on the wireless application protocol". Computing. 2014, 96(9):855-874http://dx.doi.org/10.1007/s00607-013-0325-1(SCI Impact factor 0.593)
[IJDSN@14]Weizhe Zhang, Hui He, Qizhen Zhang, Tai-hoon Kim. "PhoneProtector: Protecting User Privacy on the Android-Based Mobile Platform". International Journal of Distributed Sensor Networks. Volume 2014 , Article ID 282417, 10 pages. http://dx.doi.org/10.1155/2014/282417(SCI Impact factor 0.665)



[BRI@13]Weizhe Zhang, Xuehui Wang, Bo Lu, Tai-hoon Kim. "Secure Encapsulation and Publication of Biological Services in the Cloud Computing Environment". BioMed Research International. Volume 2013, Article ID 170580, 8 pages.http://dx.doi.org/10.1155/2013/170580 (SCI Impact factor 2.706)
[STUD INFORM CONTROL@13] Weizhe Zhang, Hui He, "Fault Tolerance for Conjugate Gradient Solver Based on FT-MPI", Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (1), pp. 51-60, 2013 http://sic.ici.ro/sic2013_1/art06.php (SCI Impact factor 0.605)
[IEICE T INF SYST@11] Kaipeng Liu, Binxing Fang, Weizhe Zhang. "Exploring social relations for personalized tag recommendation in social tagging systems". IEICE Transactions on Information and Systems. 2011, E94-D (3):542-551. http://dx.doi.org//10.1587/transinf.E94.D.542 (SCI Index) (Corresponding Author)
[IEICE T INF SYST@10] Xiao Xu, Weizhe Zhang, Hongli Zhang, Binxing Fang. “Exploring web partition in DHT-based distributed web crawling”. IEICE Transactions on Information and Systems, E93-D(11):2907-2921, 2010.http://dx.doi.org//10.1587/transinf.E93.D.2907 (SCI Index)
[IEICE T INF SYST@10] Xiao Xu, Weizhe Zhang, Hongli Zhang, Binxing Fang. “Efficient Distributed Web Crawling Utilizing Internet Resources”. IEICE Transactions on Information and Systems, E93-D(10):2747-2762, 2010. http://dx.doi.org/10.1587/transinf.E93.D.2747(SCI Index)(Corresponding Author)
[SCI CHINA SER F@06] Weizhe Zhang, Binxing Fang, Mingzeng Hu, and Hongli Zhang, “Multisite co-allocation scheduling algorithms for parallel jobs in computing grid environments”,Science in China (Series F), 36(10):1240-1262, 2006.http://dx.doi.org/10.1007/s11432-006-2034-2 (SCI Index)
[LNCS@04]Weizhe Zhang,Mingzeng Hu. "Complexity Analysis of Load Balance Problem for Synchronous Iterative Applications". In: Lecture Notes in Computer Science(LNCS) 3251, H. Jin, Y. Pan, N. Xiao, and J. Sun (Eds.), Berlin Heidelberg: Springer-Verlag, 2004, 201-208. (Proc. of the 3rd International Conf. on Grid and Cooperative Computing (GCC 2004), Wuhan, P.R.China, Oct. 21-24, 2004). http://dx.doi.org/10.1007/978-3-540-30208-7_32(SCI Index)
[LNCS@03]Weizhe Zhang, Hongli Zhang, Hui He, Mingzeng Hu. "Multisite Task Scheduling on Distributed Computing Grid". In: Lecture Notes in Computer Science(LNCS) 3033, Berlin Heidelberg: Springer-Verlag, 2004, 57-64. (Proc. of the 2nd International Conf. on Grid and Cooperative Computing (GCC 2003), Shanghai, P.R.China, Dec. 7-10, 2003. http://dx.doi.org/10.1007/978-3-540-24680-0_8(SCI Index)
[软件学报@15] 白恩慈，张伟哲. 一种计算混合关键任务响应时间的方法.软件学报,2015,26(S2):257-262
[软件学报@12] 张伟哲，张宏莉，许笑，何慧. 分布式搜索引擎系统效能建模与评价. 软件学报. 2012,23(2):253-265
[计算机研究与发展@12] 张伟哲，张鸿，刘欣然，陈琳，李东.基于语料阶梯评价的互联网论坛舆论领袖筛选算法. 计算机研究与发展, 2012, 49(Suppl.):145-152
[电子学报@12] 张伟哲，王佰玲，何慧，谭卓鹏. 基于异质网络的意见领袖社区发现. 电子学报, 2012, 40(10):1927-1932.
[计算机学报@11] 张伟哲，张宏莉，张迪，程涛.云计算平台中多虚拟机内存协同优化策略研究. 计算机学报. 2011, 34(12):2265-2277.
[软件学报@10] 张伟哲，张宏莉，张元竞. 基于判例构造的并行作业性能预测. 软件学报, 2010, 21(sup):238-250

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[软件学报@10] 许笑，张伟哲，张宏莉，方滨兴. 广域网分布式Web爬虫. 软件学报. 2010,21(5):1067-1082
[软件学报@07] 张伟哲，田志宏，张宏莉，何慧，刘文懋. 虚拟计算环境中的多机群协同调度算法. 软件学报.2007, 18(8):2027-2037.
[中国科学E辑@06] 张伟哲，方滨兴，胡铭曾，刘欣然，张宏莉，高雷. 计算网格环境下基于多址协同的作业级任务调度算法. 中国科学E辑:信息科学. 2006, 36(10):1240-1262.
[计算机学报@06]张伟哲，方滨兴，胡铭曾，张宏莉. 基于信任QOS增强的计算服务调度算法. 计算机学报. 2006，29(7): 1157-1166

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[通信学报@06]张伟哲，刘欣然，云晓春，张宏莉，胡铭曾，刘凯鹏. 信任驱动的网格作业调度算法. 通信学报. 2006，27(2) :73-79
[计算机研究与发展@06] 张伟哲，胡铭曾，张宏莉，刘凯鹏. 多QoS约束网格作业调度问题的多目标演化算法. 计算机研究与发展. 2006，43(11):1855-1862.
[计算机研究与发展@04]张伟哲，胡铭曾，刘凯鹏.基于网络性能的计算网格主机聚类. 计算机研究与发展. 2004，41(12): 2135-2140



Conferences
[PDSW'14] Babak Behzad, Hoang-Vu Dang, Farah Hariri, Weizhe Zhang, Marc Snir. "Automatic Generation of I/O Kernels for HPC Applications". In: Proc. of the 9th Parallel Data Storage Workshop (PDSW’14), (associated with the 2014 Internatonal Conference for High Performance Computing, Networking, Storage, and Analysis(SC 2014)), New Orleans, LA, USA, November. 16-21, 2014, Piscataway, NJ, USA: IEEE Press.31-36



[CLUSTER'12]Weizhe Zhang, Tianyu Han, Yuanjing Zhang, Albert M.K.Cheng. "Performance Prediction for MPI Parallel Jobs". In: Proc. of the 2012 International Workshop on Power and QoS Aware Computing (PQoSCom’12), (associated with the IEEE Cluster 2012 (Cluster 2012)), Beijing, China, September. 24-28, 2012, Piscataway, NJ, USA: IEEE Press.136-142

[IPDPS'12] Weizhe Zhang, Hongli Zhang, Huixiang Chen, Qizhen Zhang, Albert M.K.Cheng. "Improving the QoS of Web Applications across Multiple Virtual Machines in Cloud Computing Environment". In: Proc. of the 1st International Workshop on Workflow Models, Systems, Services and Applications in the Cloud (CloudFlow 2012), (associated with the 26^th International Parallel and Distributed Processing Symposium (IPDPS 2012)), Shanghai, China, May. 21-25, 2012, Piscataway, NJ, USA: IEEE Press.2241-2247

[CIKM'10] Kaipeng Liu, Binxing Fang, Weizhe Zhang. "Ontology Emergence from Folksonomies". In:Proceedings of the 19th ACM international conference of Information and knowledge management (CIKM2010), Toronto, Canada. Oct., 1109-1118. 2010

[ICPADS'09] Xiao Xu, Weizhe Zhang, Hongli Zhang, Binxing Fang. “A Forwarding-based Task Scheduling Algorithm for Distributed Web Crawling over DHTs”. In: Proc. of the 2009 IEEE International Workshop on Internet-based Virtual Computing Environment (iVCE’09) (associated with the 15th International Conference on Parallel and Distributed Systems (ICPADS’09)) , Shenzhen, Guangdong, China, December. 8-11 Piscataway, NJ , 2009

[ICICSE'09]Weizhe Zhang, Yuanjing Zhang, Hongli Zhang Xuemai Gu and Albert M.K. Cheng. “A Memory-Efficient Multi-Pattern Matching Algorithm based on the Bitmap”, In: Proc. of the 4th International Conference on Internet Computing for Engineering and Science (ICICSE’09). Harbin, China, December. 2009.

[ICYCS'08] Weizhe Zhang, Hongli Zhang, Xinran Liu, and Xuemai Gu, “Parallel Job Scheduling with Time-varying Constraints for Heterogeneous Multiple-Cluster Systems”, In: Proc. of the 9th International Conference for Young Computer Scientists (ICYCS’08),. Zhang JiaJie, China, Nov. 2008

[IPDPS'06]Weizhe Zhang, Albert M.K.Cheng，and Mingzeng Hu, “Multisite Co-allocation Algorithms for Computational Grid”, In: Proc. of the 3rd High-Performance Grid Computing Workshop(HPGC’06), (associated with the International Parallel and Distributed Processing Symposium 2006 (IPDPS’06)), Rhodes Island, Greece, Apr. 25~29, 2006.

[PDCAT'05]Weizhe Zhang, Mingzeng Hu, and Hongli Zhang, “Load Balance Heuristics for Synchronous Iterative Applications on Heterogeneous Cluster Systems”, In: Proc. of the 6th International Conf. on Parallel and Distributed Computing, Applications and Technologies(PDCAT 2005)，Dalian, P.R.China, Dec. 5~8, 2005, New York: IEEE Press, 2005

[GCC'04]Weizhe Zhang and Mingzeng Hu, “Complexity Analysis of Load Balance Problem for Synchronous Iterative Applications”, In: Lecture Notes in Computer Science(LNCS) 3251, H. Jin, Y. Pan, N. Xiao, and J. Sun (Eds.), Berlin Heidelberg: Springer-Verlag, 2004, 201~208. (Proc. of the 3rd International Conf. on Grid and Cooperative Computing (GCC 2004), Wuhan, P.R.China, Oct. 21~24, 2004).

[HCW'04] Weizhe Zhang, Binxing Fang, Hui He, Hongli Zhang, and Mingzeng Hu, “Multisite Resource Selection and Scheduling Algorithm on Computational Grid”, In: Proc. of the 13th Heterogeneous Computing Workshop (HCW’04), (associated with the International Parallel and Distributed Processing Symposium 2004 (IPDPS’04)), Santa Fe, New Mexico, USA, Apr. 26~30, 2004, New York: IEEE Press, 2004


Courses
Parallel and Distributed SystemRealTime ComputingMulticore ProgrammingComputer NetworkComputer Network Security


招生信息
硕士招生: 每年3-8人。



博士招生: 每年2名。



欢迎对相关领域有研究兴趣的同学报考哈尔滨工业大学（本部）或者哈尔滨工业大学（深圳）。

桃李满天
博士

2013级：白恩慈（在读）

2014级：ABDESSAMIA FOUDIL（在读），刘鑫（在读）

2015级：郝萌（在读）， 鲁刚钊（在读），Rahul Yadav （在读）

2016级：王德胜（在读），吴毓龙（在读）

2017级：王法瑞（在读），Ibrahim Elgendy（在读）

2018级：刘广睿（在读），凌晨（在读）

2019级：期待你的加盟

硕士

2010届：张元竞（微软）(优秀硕士毕业生(金牌))

2011届：程涛 （腾讯），谭卓鹏（百度）

2012届：邸文晨（e代驾）

2013届：曹博然（航天信息），陈慧祥（University of Florida）

2014届：张祐（百度），王学惠（去哪儿网）(优秀硕士毕业生(金牌))

2015届：李肖强（阿里巴巴），詹春艳（中汇信息技术（上海）有限公司）， 谢虎成（百度）(优秀硕士毕业生(金牌))

2016届：郝萌（直博）， 鲁刚钊（直博），韩硕（中科院软件所）（优秀硕士毕业生(金牌)）

2017届：王德胜（硕博），李雄（百度）， 宋博宇（腾讯）

2018届： 胡尧（网易），孙奥（百度）， 武兴隆（饿了么），李星辰（南京信息研究所）

2019届： 姜喆（在读），陈煌（在读），孙强（在读），谢根栓（在读）

2020届： 李启飞（在读），李蔚恒（在读）， 周擎阳（在读）， 彭佳滨（在读）

2021届：期待你的加盟



软件工程硕士：

2010届：亓楠，陆文斌

2011届：黄大勇，韩中海，余铁锡，王帅

2012届：陈林君，周聪

2013届：龙禹，王小辉，娄志云，张文凤(优秀硕士毕业生(银牌))

2014届：韩天宇，徐玥

2016届：曹庆兰, 严成武，张志恒（优秀硕士毕业生(金牌)）

2017届：嵩聚星（优秀硕士毕业生（金牌））， 于文强

2018届：王一名（在读）， 王阳睿（在读）

2015年7月全体学生合影


2016年5月全体学生聚餐