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Jilin Zhang
Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310037, China.

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Journal article
Published: 12 December 2018 in Sensors
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In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface.

ACS Style

Congfeng Jiang; Tiantian Fan; Yeliang Qiu; Hongyuan Wu; Jilin Zhang; Neal N. Xiong; Jian Wan. Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors 2018, 18, 4395 .

AMA Style

Congfeng Jiang, Tiantian Fan, Yeliang Qiu, Hongyuan Wu, Jilin Zhang, Neal N. Xiong, Jian Wan. Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors. 2018; 18 (12):4395.

Chicago/Turabian Style

Congfeng Jiang; Tiantian Fan; Yeliang Qiu; Hongyuan Wu; Jilin Zhang; Neal N. Xiong; Jian Wan. 2018. "Interdomain I/O Optimization in Virtualized Sensor Networks." Sensors 18, no. 12: 4395.

Journal article
Published: 31 July 2018 in Future Generation Computer Systems
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It is very meaningful and also has good business value to map relationships among users from virtual network to reality. But the current research mainly concentrates on community discovery in social networks. To address the issue, we propose an approach that can mine colleagueship among users in social networking and then find out the members for specific organizations. In this paper, first we define 6 parameters for quantitatively describing the relationship between a user and a group of users on Twitter. And then, we define 7 hypotheses for describing the interactive features between colleagues on Twitter so as to apply the 6 parameters to colleagueship mining and member recognition. Then through empirical research we systematically evaluate the influence of each of the 6 parameters on identifying colleagueship on Twitter. Finally, we present an optimal evaluation model for our approach to determine whether a user is to be a member of a specific organization. Given an organization with its public account and a list of sample users on Twitter, our approach can dig out the users who affiliate with the organization. We also conduct an experiment to evaluate our approach. The experimental results demonstrate that our approach is superior to the main existing schemes in terms of a high recognition rate.

ACS Style

Huayou Si; Zhihui Chen; Wei Zhang; Jian Wan; Jilin Zhang; Neal N. Xiong. A member recognition approach for specific organizations based on relationships among users in social networking Twitter. Future Generation Computer Systems 2018, 92, 1009 -1020.

AMA Style

Huayou Si, Zhihui Chen, Wei Zhang, Jian Wan, Jilin Zhang, Neal N. Xiong. A member recognition approach for specific organizations based on relationships among users in social networking Twitter. Future Generation Computer Systems. 2018; 92 ():1009-1020.

Chicago/Turabian Style

Huayou Si; Zhihui Chen; Wei Zhang; Jian Wan; Jilin Zhang; Neal N. Xiong. 2018. "A member recognition approach for specific organizations based on relationships among users in social networking Twitter." Future Generation Computer Systems 92, no. : 1009-1020.

Journal article
Published: 29 March 2018 in IEEE Access
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In recent years, distributed systems have mainly been used to train machine learning (ML) models. However, as a result of the different performances among computational nodes in a distributed cluster and delays in network transmission, the accuracies and convergence rates of ML models are relatively low. Therefore, it is necessary to design a reasonable strategy that provides dynamic communication optimization to improve the utilization of the cluster, accelerate the training times, and strengthen the accuracy of the training model. In this paper, we propose the adaptive synchronous parallel strategy for distributed ML. Through the performance monitoring model, the synchronization strategy of each computational node with the parameter server is adjusted adaptively by considering the full performance of each node, thereby ensuring higher accuracy. Furthermore, our strategy prevents the ML model from being affected by irrelevant tasks in the same cluster. Experiments show that our strategy fully improves clustering performance, and it ensures the accuracy and convergence speed of the model, increases the model training speed, and has good expansibility.

ACS Style

Jilin Zhang; Hangdi Tu; Yongjian Ren; Jian Wan; Li Zhou; Mingwei Li; Jue Wang. An Adaptive Synchronous Parallel Strategy for Distributed Machine Learning. IEEE Access 2018, 6, 19222 -19230.

AMA Style

Jilin Zhang, Hangdi Tu, Yongjian Ren, Jian Wan, Li Zhou, Mingwei Li, Jue Wang. An Adaptive Synchronous Parallel Strategy for Distributed Machine Learning. IEEE Access. 2018; 6 ():19222-19230.

Chicago/Turabian Style

Jilin Zhang; Hangdi Tu; Yongjian Ren; Jian Wan; Li Zhou; Mingwei Li; Jue Wang. 2018. "An Adaptive Synchronous Parallel Strategy for Distributed Machine Learning." IEEE Access 6, no. : 19222-19230.

Journal article
Published: 20 March 2018 in IEEE Access
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The cyber-physical-social (CPS) computing and Networking is a human centric and holistic computing framework that need convert the low-level data of physical, cyber, and social worlds into higherlevel information which can provide insights and help humans make complex decisions. Here we focus on human fishing behavior recognition for Vessel Monitoring Systems (VMS), an application of CPS. And the recognition of fishing behavior is the key task for studying human fishing activities, monitoring illegal fishing and protecting fishery resources. However, VMS data basically consist of sequentially recorded position information and do not directly indicate whether a fisherman is fishing or not; thus, converting these low-level CPS data into intuitive information to humans is the primary task. In this paper, an identification model based on multi-step clustering algorithm (MSC-FBI) is proposed to automatically learn and discover fishing behaviors at sea. First, a temporal-spatial distance model is established; then, an improved multistep clustering algorithm is used to identify human fishing behaviors, and finally, the patterns of different behaviors are extracted from the trajectory and the unsupervised behavior learning model is established. Using this method, many experiments on different fishing trajectory data were implemented and compared to a traditional identification method based on the Gaussian Mixture Model (GMM-FBI). The experimental results illustrate the proposed model’s superior performance.

ACS Style

Jilin Zhang; Jiali Geng; Jian Wan; Yifan Zhang; Mingwei Li; Jue Wang; Neal N. Xiong. An Automatically Learning and Discovering Human Fishing Behaviors Scheme for CPSCN. IEEE Access 2018, 6, 19844 -19858.

AMA Style

Jilin Zhang, Jiali Geng, Jian Wan, Yifan Zhang, Mingwei Li, Jue Wang, Neal N. Xiong. An Automatically Learning and Discovering Human Fishing Behaviors Scheme for CPSCN. IEEE Access. 2018; 6 (99):19844-19858.

Chicago/Turabian Style

Jilin Zhang; Jiali Geng; Jian Wan; Yifan Zhang; Mingwei Li; Jue Wang; Neal N. Xiong. 2018. "An Automatically Learning and Discovering Human Fishing Behaviors Scheme for CPSCN." IEEE Access 6, no. 99: 19844-19858.

Journal article
Published: 21 September 2017 in Sensors
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In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

ACS Style

Jilin Zhang; Hangdi Tu; Yongjian Ren; Jian Wan; Li Zhou; Mingwei Li; Jue Wang; Lifeng Yu; Chang Zhao; Lei Zhang. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors. Sensors 2017, 17, 2172 .

AMA Style

Jilin Zhang, Hangdi Tu, Yongjian Ren, Jian Wan, Li Zhou, Mingwei Li, Jue Wang, Lifeng Yu, Chang Zhao, Lei Zhang. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors. Sensors. 2017; 17 (10):2172.

Chicago/Turabian Style

Jilin Zhang; Hangdi Tu; Yongjian Ren; Jian Wan; Li Zhou; Mingwei Li; Jue Wang; Lifeng Yu; Chang Zhao; Lei Zhang. 2017. "A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors." Sensors 17, no. 10: 2172.

Research article
Published: 21 March 2017 in Mobile Information Systems
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With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

ACS Style

Jilin Zhang; Junfeng Xiao; Jian Wan; Jianhua Yang; Yongjian Ren; Huayou Si; Li Zhou; Hangdi Tu. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System. Mobile Information Systems 2017, 2017, 1 -12.

AMA Style

Jilin Zhang, Junfeng Xiao, Jian Wan, Jianhua Yang, Yongjian Ren, Huayou Si, Li Zhou, Hangdi Tu. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System. Mobile Information Systems. 2017; 2017 ():1-12.

Chicago/Turabian Style

Jilin Zhang; Junfeng Xiao; Jian Wan; Jianhua Yang; Yongjian Ren; Huayou Si; Li Zhou; Hangdi Tu. 2017. "A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System." Mobile Information Systems 2017, no. : 1-12.

Journal article
Published: 12 July 2016 in Computer Physics Communications
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GPU not only is used in the field of graphic technology but also has been widely used in areas needing a large number of numerical calculations. In the energy industry, because of low carbon, high energy density, high duration and other characteristics, the development of nuclear energy cannot easily be replaced by other energy sources. Management of core fuel is one of the major areas of concern in a nuclear power plant, and it is directly related to the economic benefits and cost of nuclear power. The large-scale reactor core expansion equation is large and complicated, so the calculation of the diffusion equation is crucial in the core fuel management process. In this paper, we use CUDA programming technology on a GPU cluster to run the LU-SGS parallel iterative calculation against the background of the diffusion equation of the reactor. We divide one-dimensional and two-dimensional mesh into a plurality of domains, with each domain evenly distributed on the GPU blocks. A parallel collision scheme is put forward that defines the virtual boundary of the grid exchange information and data transmission by non-stop collision. Compared with the serial program, the experiment shows that GPU greatly improves the efficiency of program execution and verifies that GPU is playing a much more important role in the field of numerical calculations.

ACS Style

Jilin Zhang; Chaoqun Sha; Yusen Wu; Jian Wan; Li Zhou; Yongjian Ren; Huayou Si; Yuyu Yin; Ya Jing. The novel implicit LU-SGS parallel iterative method based on the diffusion equation of a nuclear reactor on a GPU cluster. Computer Physics Communications 2016, 211, 16 -22.

AMA Style

Jilin Zhang, Chaoqun Sha, Yusen Wu, Jian Wan, Li Zhou, Yongjian Ren, Huayou Si, Yuyu Yin, Ya Jing. The novel implicit LU-SGS parallel iterative method based on the diffusion equation of a nuclear reactor on a GPU cluster. Computer Physics Communications. 2016; 211 ():16-22.

Chicago/Turabian Style

Jilin Zhang; Chaoqun Sha; Yusen Wu; Jian Wan; Li Zhou; Yongjian Ren; Huayou Si; Yuyu Yin; Ya Jing. 2016. "The novel implicit LU-SGS parallel iterative method based on the diffusion equation of a nuclear reactor on a GPU cluster." Computer Physics Communications 211, no. : 16-22.

Journal article
Published: 28 January 2016 in Multimedia Tools and Applications
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As the largest video sharing site around the world, YouTube has been changing the way people entertain, gain popularity, and advertise. Discovering the major sources that drive views to a video and understanding how they impact the view growth pattern have become interesting topics for researchers as well as advertisers, media companies, or anyone who wish to have a shortcut to stardom. The work of this paper is to identify three major view sources, related video recommendation, YouTube search, and video highlight such as popular video list on YouTube homepage or video embedding on social networking sites, and examine the patterns of views from each view source. First, the impact of each view source on the view diversity and on the view share of each individual video is analyzed. It is found that while search and highlight create an effect of rich-get-richer, the related video recommendation equalizes the view distribution and helps users find niche videos. Second, the contribution of the three view sources to video popularity growth is investigated. The investigation reveals that search and related video recommendation are the two major sources that persistently drive views to a video. The view rates from recommendation and search are generally stabilized to be constant view rates. Third, the underlying factors that affect the long-term view rate from referrer videos are explored. The results indicate that the top referrer video set of a video is fairly stable and the view rate from recommendation is mainly determined by view rates of top referrer videos. Finally, whether highlight increases the view rate of a video after the duration of promotion is studied. The observations suggest that video highlight does not directly impact the view rate of a video after the event finishes. The findings presented in the paper provide several key insights into the impact and patterns of view contributions for each major source of the video views.

ACS Style

Renjie Zhou; Samamon Khemmarat; Lixin Gao; Jian Wan; Jilin Zhang. How YouTube videos are discovered and its impact on video views. Multimedia Tools and Applications 2016, 75, 6035 -6058.

AMA Style

Renjie Zhou, Samamon Khemmarat, Lixin Gao, Jian Wan, Jilin Zhang. How YouTube videos are discovered and its impact on video views. Multimedia Tools and Applications. 2016; 75 (10):6035-6058.

Chicago/Turabian Style

Renjie Zhou; Samamon Khemmarat; Lixin Gao; Jian Wan; Jilin Zhang. 2016. "How YouTube videos are discovered and its impact on video views." Multimedia Tools and Applications 75, no. 10: 6035-6058.

Journal article
Published: 01 July 2013 in International Journal of Distributed Sensor Networks
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In distributed wireless sensor networks (DWSNs), the data gathered by sink is always massive and consumes a lot of resources. It is suitable for cloud computing platform to apply service in data processing system. In cloud computing, IAAS platform provides services and calculation to the user through the virtual machine. The management of virtual machine images not only consumes a huge amount of storage space but also gives large pressure on network transmission. By using deduplication technology in openstack, this paper designed and implemented, an image management system IM-dedup, which uses static chunking (SC) to divide image file into blocks of data, avoid duplication data blocks transmission on network by using fingerprint pretransmission technology, and reduce storage space by deploying kernel mode file system with deduplication in the image storage server. The experimental results showed that the system not only reduced 80% usage of the virtual machine image storage, but also saved at least 30% of transmission time. Furthermore, the research on virtual machine image format showed that “VMWare Virtual Machine Disk Format” (VMDK), “Virtual Desktop Infrastructure” (VDI), “QEMU Copy On Write2” (QCOW2), and RAW image formats are more suitable for the IM-dedup system.

ACS Style

Jilin Zhang; Shuting Han; Jian Wan; Baojin Zhu; Li Zhou; Yongjian Ren; Wei Zhang. IM-Dedup: An Image Management System Based on Deduplication Applied in DWSNs. International Journal of Distributed Sensor Networks 2013, 9, 1 .

AMA Style

Jilin Zhang, Shuting Han, Jian Wan, Baojin Zhu, Li Zhou, Yongjian Ren, Wei Zhang. IM-Dedup: An Image Management System Based on Deduplication Applied in DWSNs. International Journal of Distributed Sensor Networks. 2013; 9 (7):1.

Chicago/Turabian Style

Jilin Zhang; Shuting Han; Jian Wan; Baojin Zhu; Li Zhou; Yongjian Ren; Wei Zhang. 2013. "IM-Dedup: An Image Management System Based on Deduplication Applied in DWSNs." International Journal of Distributed Sensor Networks 9, no. 7: 1.

Journal article
Published: 30 June 2012 in INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences
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ACS Style

JiLin Zhang -; Chang Zhou -; Jian Wan -; Li Zhou -; Yuyu Yin -; CongFeng Jiang -; Yongjian -. OBSC: A Server-side Cache Scheme for Object-Based Distributed Storage System. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 2012, 4, 217 -224.

AMA Style

JiLin Zhang -, Chang Zhou -, Jian Wan -, Li Zhou -, Yuyu Yin -, CongFeng Jiang -, Yongjian -. OBSC: A Server-side Cache Scheme for Object-Based Distributed Storage System. INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences. 2012; 4 (11):217-224.

Chicago/Turabian Style

JiLin Zhang -; Chang Zhou -; Jian Wan -; Li Zhou -; Yuyu Yin -; CongFeng Jiang -; Yongjian -. 2012. "OBSC: A Server-side Cache Scheme for Object-Based Distributed Storage System." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 11: 217-224.

Journal article
Published: 30 March 2012 in Advanced Science Letters
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With the prevalence of cloud computing, virtualized block-level storage systems have received much attention from both academic and industrial fields. Various block-level storage systems have been implemented, such as Amazon Elastic Block Store (EBS), Eucalyptus' EBS implementation, and the Virtual Block Store (VBS) system. In this paper, we present a Multiple Volume Servers virtualized Block-Level Store System based on VBS, named Orthrus. Compared with VBS, Orthrus can achieve higher storage reliability and efficiency. We propose a ListenDetect-Switch mechanism for Orthrus to deal with contingent volume servers failure, and design a connection equal distribution strategy to balance the load of volume servers. Extensive experimental results show that the aggregated I/O throughputs of Orthrus with two volume servers are almost twice as high as that of VBS.

ACS Style

Jian Wan; Yi-Cheng Wang; Ji-Lin Zhang; Li Zhou. Orthrus: A Block-Level Virtualized Storage System with Multiple-Volume Servers. Advanced Science Letters 2012, 7, 68 -72.

AMA Style

Jian Wan, Yi-Cheng Wang, Ji-Lin Zhang, Li Zhou. Orthrus: A Block-Level Virtualized Storage System with Multiple-Volume Servers. Advanced Science Letters. 2012; 7 (1):68-72.

Chicago/Turabian Style

Jian Wan; Yi-Cheng Wang; Ji-Lin Zhang; Li Zhou. 2012. "Orthrus: A Block-Level Virtualized Storage System with Multiple-Volume Servers." Advanced Science Letters 7, no. 1: 68-72.

Journal article
Published: 30 March 2012 in Advanced Science Letters
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ACS Style

Congfeng Jiang; Jilin Zhang; Xianghua Xu; Jian Wan. Application Power Characteristics Using Dynamic Frequency Scaling: Experiments and Implications. Advanced Science Letters 2012, 7, 64 -67.

AMA Style

Congfeng Jiang, Jilin Zhang, Xianghua Xu, Jian Wan. Application Power Characteristics Using Dynamic Frequency Scaling: Experiments and Implications. Advanced Science Letters. 2012; 7 (1):64-67.

Chicago/Turabian Style

Congfeng Jiang; Jilin Zhang; Xianghua Xu; Jian Wan. 2012. "Application Power Characteristics Using Dynamic Frequency Scaling: Experiments and Implications." Advanced Science Letters 7, no. 1: 64-67.

Journal article
Published: 01 February 2012 in Advanced Science Letters
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ACS Style

Wei Zhang; Jian Wan; Wanqing Li; Jilin Zhang; Jixiang Zhu. Online Circuit Evolution System Based on Adjustable Genetic Algorithm and Fast Pre-Evaluator. Advanced Science Letters 2012, 5, 624 -628.

AMA Style

Wei Zhang, Jian Wan, Wanqing Li, Jilin Zhang, Jixiang Zhu. Online Circuit Evolution System Based on Adjustable Genetic Algorithm and Fast Pre-Evaluator. Advanced Science Letters. 2012; 5 (2):624-628.

Chicago/Turabian Style

Wei Zhang; Jian Wan; Wanqing Li; Jilin Zhang; Jixiang Zhu. 2012. "Online Circuit Evolution System Based on Adjustable Genetic Algorithm and Fast Pre-Evaluator." Advanced Science Letters 5, no. 2: 624-628.

Journal article
Published: 11 August 2011 in Journal of Software
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With the scale of computing system increases, system performance and reliability, described by various Quality of Service(QoS) metrics, cannot be guaranteed if only the objective is to minimize the total power consumptions separately, despite of the violations of QoS. In this paper a feedback control based power aware job scheduling algorithm is proposed to minimize power consumption in computing system and to provide QoS guarantees. In the proposed algorithm, jobs are scheduled according to the real-time and historical power consumption as well as the QoS requirements. Simulations show that the proposed algorithm can reduce power consumptions significantly while still providing QoS guarantees and the performance degradation is acceptable. The results also show that fine-grained job-level power aware scheduling can achieve better power/performance balancing between multiple processors or cores than coarse-grained methods. And the results also suggest that conventional hardware based per-component and system-wide power management methods can save more power consumptions if they are in assistance with job-level adaptation.

ACS Style

Congfeng Jiang; Xianghua Xu; Jian Wan; Jilin Zhang; Yinghui Zhao. Power Aware Job Scheduling with QoS Guarantees Based on Feedback Control. Journal of Software 2011, 6, 1562-1569 .

AMA Style

Congfeng Jiang, Xianghua Xu, Jian Wan, Jilin Zhang, Yinghui Zhao. Power Aware Job Scheduling with QoS Guarantees Based on Feedback Control. Journal of Software. 2011; 6 (8):1562-1569.

Chicago/Turabian Style

Congfeng Jiang; Xianghua Xu; Jian Wan; Jilin Zhang; Yinghui Zhao. 2011. "Power Aware Job Scheduling with QoS Guarantees Based on Feedback Control." Journal of Software 6, no. 8: 1562-1569.

Journal article
Published: 01 July 2011 in Journal of Computers
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Resource management is one of the main issues in Cloud Computing. In order to improve resource utilization of large Data Centers while delivering services with higher QoS to Cloud Clients, an automatic resource allocation strategy based on market Mechanism (ARAS-M) is proposed. Firstly, the architecture and the market model of ARAS-M are constructed, in which a QoS-refectitive utility function is designed according to different resource requirements of Cloud Client. The equilibrium state of ARAS-M is defined and the proof of its optimality is given. Secondly, A Genetic Algorithm (GA)-based automatic price adjusting algorithm is introduced to deal with the problem of achieving the equilibrium state of ARAS-M. Finally, ARAS-M is implemented on Xen. Experiment resultsACS Style

Xindong You; Jian Wan; Xianghua Xu; Congfeng Jiang; Wei Zhang; Jilin Zhang. ARAS-M: Automatic Resource Allocation Strategy based on Market Mechanism in Cloud Computing. Journal of Computers 2011, 6, 1 .

AMA Style

Xindong You, Jian Wan, Xianghua Xu, Congfeng Jiang, Wei Zhang, Jilin Zhang. ARAS-M: Automatic Resource Allocation Strategy based on Market Mechanism in Cloud Computing. Journal of Computers. 2011; 6 (7):1.

Chicago/Turabian Style

Xindong You; Jian Wan; Xianghua Xu; Congfeng Jiang; Wei Zhang; Jilin Zhang. 2011. "ARAS-M: Automatic Resource Allocation Strategy based on Market Mechanism in Cloud Computing." Journal of Computers 6, no. 7: 1.

Journal article
Published: 11 September 2009 in Chinese Journal of Computers
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ACS Style

Jue Wang; Chang-Jun Hu; Ji-Lin Zhang; Jian-Jiang Li. An Optimized Strategy for Collective Communication in Data Parallelism. Chinese Journal of Computers 2009, 31, 318 -328.

AMA Style

Jue Wang, Chang-Jun Hu, Ji-Lin Zhang, Jian-Jiang Li. An Optimized Strategy for Collective Communication in Data Parallelism. Chinese Journal of Computers. 2009; 31 (2):318-328.

Chicago/Turabian Style

Jue Wang; Chang-Jun Hu; Ji-Lin Zhang; Jian-Jiang Li. 2009. "An Optimized Strategy for Collective Communication in Data Parallelism." Chinese Journal of Computers 31, no. 2: 318-328.