张伟哲 中国
仰天大笑出门去
教授||博士生导师
目前就职计算机科学与技术学院
所在学科 计算机科学与技术
永久地址 http://homepage.hit.edu.cn/wzzhang

News

[Congrats] In 2018, our paper "An Efficient and Secured Framework for Mobile Cloud Computing" has been accepted by IEEE Transactions on Cloud Computing
发布时间:2018-06-20
简单介绍:

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.2018.2847347

Basic Information

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

Professor&Ph.D Supervisor (2017 - )

School of Computer Science and Technology, Harbin Institute of Technology(Shenzhen), China

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: 86-451-86418272
Fax: 86-451-86413331
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

[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)
发布时间:2017-12-18
简单介绍:

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/10.1155/2017/6741972

[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)
发布时间:2017-05-26
简单介绍:

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

[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)
发布时间:2017-01-01
简单介绍:

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

[Congrats] In 2016, our paper "Trustworthy Enhancement for Cloud Proxy based on Autonomic Computing" has been accepted by IEEE Transactions on Cloud Computing
发布时间:2016-11-2
简单介绍:

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.2016.2603508

[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)
发布时间:2016-09-17
简单介绍:

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


[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)
发布时间:2016-04-04
简单介绍:

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.

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