ISSN 1000-1239 CN 11-1777/TP

Table of Content

15 July 2011, Volume 48 Issue 7
Architecture and Key Technologies of LingCloud
Lu Xiaoyi, , Lin Jian, , and Zha Li
2011, 48(7):  1111-1122. 
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The research on basic model, architecture, key technologies and system implementation is the hot topic of cloud computing in academic community and IT industry. By analyzing the characteristics of two existed types of cloud computing systems, including vertical cloud system and hybrid cloud system, three important and challenging issues of building a production cloud computing system are observed, which are efficiently supporting of heterogeneous application load modes, multi-tenant resource leasing, and adopting a uniform system to consolidate massive cloud resources. Upon these problems, this paper introduces the construction and goals of LingCloud, presents an asset-leasing model based architecture of LingCloud, and analyses the key technologies of LingCloud, involving asset partition management, virtual network management, virtual appliance management, virtual cluster leasing framework, and global user management in cloud. At present, the LingCloud system has been implemented and deployed in the real-world environments. According to the evaluation, LingCloud is better than other related systems of OpenNebula and Enomaly ECP in the aspects of uniformity, flexibility, security, usability, and efficiency. The performance result shows that the logic deployment speed in LingCloud is 4.1 times of that in the OpenNebula and VIDA hybrid system in the case of 64 virtual machines deployment concurrently.
Automatic Power-Aware Reconfiguration of Processor Resource in Virtualized Clusters
Jin Hai, Deng Li, Wu Song, Shi Xuanhua, and Zhou Like
2011, 48(7):  1123-1133. 
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Virtualization provides significant benefits of system maintenance, load balancing, fault tolerance, and power-saving in clusters or data centers. It also enables dynamic reconfiguration of computing resource for application environment. However, current dynamic resource control approaches mainly focus on how to satisfy the application-level quality of service (QoS) when application workloads vary with time. They are driven by application performance, which is often specific to certain class of applications and also weakens the response capability of control systems. In this paper, a resource-use-status-driven resource reconfiguration scheme (RUSiC) is presented to automatically adapt to dynamic workload changes to meet the demands of application performance. According to the new characteristics introduced by system virtualization, the scheme is designed to be a two-layer resource reconfiguration model to exactly grasp the demands of applications. Based on real resource use status, the scheme adjusts proper resource configuration for applications in time. Furthermore, the scheme introduces power-saving into resource reconfiguration process and avoids considerable unnecessary power and cooling consumption by reducing the number of active physical nodes in a new resource configuration. Experiments demonstrate that the scheme can quickly detect and respond to shifting resource demands as application workloads change over time.
Nova-BFT: A Replicated State Machine Protocol Supporting Multiple Fault Models
Wang Yongjian,, Pei Xiang, Li Tao, Luan Zhongzhi, and Qian Depei,
2011, 48(7):  1134-1145. 
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Cloud computing has greatly simplified the ways that the clients can access the resources, and the pain is the increasing complexity of the supporting system development and deployment. The Byzantine faults caused by software bugs, management misbehaviors have become a major source that affects the reliability of the system. The cost in communication, security and the robustness issue under attack result in that the Byzantine fault tolerance technology can’t be used directly in the practical systems which satisfy the benign fault model in most period of their lifecycle. How to satisfy the requirements of multiple fault-model has become an important problem in system design. To deal with the situation, we design Nova-BFT, a replicated state machine protocol which can support multiple fault models effectively. Nova-BFT fulfills performance robustness under attack by sacrificing some peak throughput in the fault-free scenario, and adaptively supports the benign fault model by adjusting configuration parameters. Experiments show that Nova-BFT prototype has a 4-5 kop/s throughput in the Byzantine fault model assumption and it can also fulfill the benign fault model requirements of most practical systems.
A Method on Protection of User Data Privacy in Cloud Storage Platform
Hou Qinghua, Wu Yongwei, Zheng Weimin, and Yang Guangwen
2011, 48(7):  1146-1154. 
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Nowadays, many researchers and IT companies pay close attention to cloud storage. A lot of applications use cloud storage to store data. But many people and companies worry about the security and privacy problem of cloud storage. Cloud storage platform is prone to compromise or abuse from adversaries including the cloud administrators. The security and privacy problem of cloud storage is the security and privacy problem of distributed file system, which is the core of cloud storage. With SSL secure connection and secure virtual machine monitor (Daoli), the security and privacy of the user’s data can be protected in the distributed file system, even facing a total compromise of the distributed file system or the operating system. With secure virtual machine monitor, conventional attacks and attacks from cloud administrators can be prevented. Two schemes for different application scenarios are put forward. In one scheme, every chunk of user’s file is protected, so the privacy of every chunk is guaranteed. In another method, only the whole file is protected, and the privacy of the whole file is guaranteed not all chunks. The overhead of the addition of SSL secure connection and secure virtual machine monitor are evaluated. In consideration of the privacy of user’s data, the overhead can be tolerated.
Lifetime Privacy and Self-Destruction of Data in the Cloud
Zhang Fengzhe, Chen Jin, Chen Haibo, and Zang Binyu
2011, 48(7):  1155-1167. 
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Data privacy protection is one of the primary concerns and major challenges for online services, such as cloud computing and outsourced data center. The concern is getting serious with the computing practices shifting towards cloud computing. Once user data is uploaded, end users are hard to guarantee that the data is protected and can be completely destructed by any means. Users can only rely on blind trust on the online service vendors. However, the privacy of user data can be compromised in multiple ways including careless operations of cloud administrators, bugs and vulnerabilities inside cloud infrastructure and even malicious cloud vendors. In this research, we seek to provide users with a concrete way to protect or destroy uploaded data. We utilize the technique of trusted computing as the trusted root in the hardware layer, and the hypervisor as the trusted agent in the software layer. The trusted hypervisor is responsible for protecting sensitive user data or destructing them at users command. Even administrators of the cloud cannot bypass the protection. This paper presents Dissolver, a novel system that keeps the data privacy in the whole life-time and ensures the destruction at the users command. Performance evaluation shows that the prototype system imposes reasonably low runtime overhead.
A GQoP Guaranteed Grid QoS Adaptive Scheduling Algorithm
Wang Wei, Luo Junzhou, and Song Aibo
2011, 48(7):  1168-1177. 
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Grid computing aims to discover, allocate and coordinate all kinds of service capabilities, which are interconnected by Internet. In service-oriented grid, how to guarantee user QoS (quality of service) requirements becomes increasingly urgent, which is one of the most important study areas in grid computing. In the past, most guarantee mechanisms proposed in the literature concerned more about the guarantee of performance QoS than that of security QoS. This is a relative lack of research on this area. In this paper, we use the concept of GQoP (grid quality of protection)to describe security QoS, and a measurement technology is given to quantize it. We start with definition of quality, description, measurement and mapping of GQoP, which is considered as a subclass of GQoS in this research. And a fusion model about GQoP and GQoS is proposed to resolve the resources conflict between them. Moreover, A GQoP guaranteed QoS adaptive scheduling algorithm is developed and it is adopted into grid system to guarantee user QoS and GQoP. This algorithm regulates QoS grade by each resource provider according to a probability distribution. It can guarantee the usersQoS grade very well, and can also achieve global utility optimum gradually through a constructed Markov chain.
Prefetching Mechanism for On-demand Software Streaming
Zhong Liang, Hu Chunming, Wo Tianyu, Li Jianxin, and Kang Junbing
2011, 48(7):  1178-1189. 
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In recent years, the “software as a service”, largely enabled by the Internet, has become an innovative software delivery model to provide network and service accessing of software. Dynamic on-demand deployment of software is a key method to achieve the above delivery model. Software streaming delivering is needed to support this deployment manner. During the streaming delivering of software, the execution waits until the missing data block is downloaded, which greatly influences the execution performance and user experience. A prefetching mechanism is presented for software streaming delivering based on N-Gram prediction model and an incremental data mining algorithm. By using historical access logs for data mining, then dynamically updating and polishing the prefetching rules, the proposed prefetching framework supports both file-level prefetching and block-level prefetching. The experimental results show that this prefetch-enable filesystem achieves a launch time reduced by 10% to 50%, as well as hit rate between 81% and 97%.
Reliability and Performance Model of Tree-Structured Grid Services Based on Multivariate Exponential Distribution
Tian Hongbo, Zhang Xingjun, Zhao Xiaoyi, Dong Xiaoshe, and Wu Weiguo
2011, 48(7):  1190-1201. 
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The previous models for reliability and performance of grid services assume that all the states of resource nodes and links are s-independent. This assumption can simplify the modeling and analysis. However it limits the applicability of the models because all the states of resource nodes and links in fact are s-dependent and follow the multivariate exponential distribution. In order to address this issue, this paper presents a novel model for reliability and performance of tree-structured grid service based on multivariate exponential distribution, which assumes that all the states of resource nodes and links are s-dependent and follow the multivariate exponential distribution. Based on the model, a novel algorithm for evaluating the grid service performance distribution and the service reliability indices is suggested. This algorithm constructs a tree of disjoint products and gets the solution of model in the form of disjoint products. The illustrative example and experimental results show that the proposed model for reliability and performance of tree-structured grid service based on multivariate exponential distribution can not only be compatible with previous tree-structured grid service model but also be adapt to the complex grid services in which all the states of resource nodes and links are s-dependent and follow the multivariate exponential distribution; and that the novel algorithm based on tree of disjoint products outperforms the previous algorithms.
Predicting the Parallel File System Performance via Machine Learning
Zhao Tiezhu, Dong Shoubin, Verdi March,, and Simon See,5
2011, 48(7):  1202-1215. 
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Parallel file system can effectively solve the problems of massive data storage and I/O bottleneck. Because the potential impact on the system is not clearly understood, how to evaluate and predict performance of parallel file system becomes the potential challenge and hotspot. In this work, we aim to research the performance evaluation and prediction of parallel file system. After studying the architecture and performance factors of such file system, we design a predictive mode of parallel file system based on machine learning approaches. We use feature selection algorithms to reduce the number of performance factors to be tested in validating the performance. We also mine the particular relationship of system performance and impact factors to predict the performance of a specific file system. We validate and predict the performance of a specific Lustre file system through a series of experiment cases. Our evaluation and experiment results indicate that threads/OST, num of OSSs (Object Storage Server), num of disks and num and type of RAID are the four most important parameters to tune the performance of Lustre file system. The average relative errors of predictive results can be controlled within 25.1%—32.1%, which shows the better prediction accuracy.
Virtual Machine VCPU Scheduling in the Multi-core Environment:Issues and Challenges
Jin Hai, Zhong Alin, Wu Song, and Shi Xuanhua
2011, 48(7):  1216-1224. 
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The policies and mechanisms of VCPU (virtual CPU) scheduling in a virtual machine system are key factors to determine the system performance. Because the architecture of the software stack in the virtual machine system is different from the traditional computer systems, when scheduling the VCPUs in virtual machines, simply adopting scheduling strategies and algorithms of existing operating systems without any modifications can lead to drastic degradation of the system performance. Moreover, with the multi-core technology being employed for physical processors, the complexity of the VCPU scheduling is increased. Firstly, the architecture of the virtual machine system and its two-stage scheduling framework are depicted and analyzed in detail in this paper. Because the deterministic mapping relationship between application threads and physical cores is difficult to establish in the two-stage framework, and part functions of operating systems move down to virtual machine monitor, VCPU scheduling will confront many problems and challenges that mainly embody four aspects: the semantic gap between guest operating systems and a virtual machine monitor, the synchronization mechanisms in a multiprocessor operating system, the structure of shared cache in multi-core processors and emerging asymmetric multi-core structure. And then advantages and limitations of the existing solutions for these problems are discussed and analyzed deeply, and suggestions for further researches are presented.
A Membership Management Mechanism for Large Scale P2P Environment
Huang Yuanqiang, Luan Zhongzhi, and Qian Depei
2011, 48(7):  1225-1236. 
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Node topology plays an important role in P2P-based systems. It serves as a basis to support application functions, and has major impact on their efficiency, cost and robustness. Membership management can construct node topology according to application’s semantic requirements by maintaining neighbor view on every node. However, large-scale P2P environment with the features of dynamic node joining/leaving, dynamic network condition fluctuation, dynamic resources varying and a large number of nodes, greatly challenges the perturbation-resistance and scalability of membership management. Many typical membership management techniques either ignored the issue of system perturbation, or ignored the issue of system size’s expansion/shrinking, and can’t work well in large-scale P2P environment. In this paper, a new membership management mechanism considering multiple factors of dynamic variation is given to maintain stable node topology in harsh environment. The proposed mechanism mainly consists of perturbation-resisted membership discovery protocol and scalable membership maintenance method. Experimental results verify the effectiveness of the proposed methods. Some useful conclusions obtained through the analysis of the experimental data lay a solid foundation for further research.
Blind Identification for Image Authenticity Based on Lambert Illumination Model
Chen Haipeng, Shen Xuanjing, Lü Yingda, and Jin Yushan
2011, 48(7):  1237-1245. 
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Based on Lambert illumination model, blind identification algorithm for image authenticity is proposed. For an image, there is an error function between its actual light intensity and calculated light intensity; and for different light source models, there are different constraint functions of light source to light beam. Therefore, for infinite light source images and local light source images, a corresponding algorithm is presented, which calculates the light source direction of different areas in an image according to the error function and the corresponding constraint function. And the image tampering can be determined by the inconsistency in light source direction of different areas in an image. That is to say, if light source directions of different areas in an image are inconsistent, it can be considered that the image has been tampered; on the other hand, if light source directions of different areas in an image are consistent, it can be considered that the image has not been tampered. This has realized blind identification for image authenticity. Experimental results show that the light source direction of different areas in an image could be calculated accurately by the proposed corresponding algorithms for the two kinds of images mentioned above. Thus, the image authenticity could be identified effectively.
Refining Image Annotation by Graph Partition and Image Search Engine
Liu Zheng, and Ma Jun
2011, 48(7):  1246-1254. 
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Automatic image annotation has been an active research direction due to its great importance in content-based image retrieval(CBIR). However, the results of existing image annotation methods are still far from practical. Therefore, it is of vital importance to design a high-performance approach which could refine the initial annotations. This paper presents a novel algorithm to solve image annotation refinement problem(IAR) by graph partition and image search engine. Our algorithm focuses on pruning the noisy words in candidate annotation set to enhance image annotation performance. The main idea of the proposed algorithm lies in that candidate annotations are served as graph vertices, and the relevance between two candidate annotations is used to construct the edge weight. Then, the image annotation refinement problem can be converted to the weighted graph partition problem. The edge weight is the annotation similarity weighted by two parameters. Parameter 1 is the relationship between candidate annotation and image visual features, and parameter 2 refers to the importance of candidate annotation in host Web page. Next, we compute max cut of the graph using a heuristic algorithm. After the graph is bi-partitioned, one of the two vertex sets is chosen as final annotations. Experimental results on non-Web images and Web images show that our algorithm outperforms the existing image annotation refinement techniques.
Improving Motion Synthesis by Semantic Control
Liu Weibin, Liu Xingqi, Xing Weiwei, and Yuan Baozong,
2011, 48(7):  1255-1262. 
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A suitable motion similarity measurement plays an important role in motion analysis for motion graph based human motion synthesis. Most existing methods of motion synthesis are based only on numerical similarity measure of motions, which cannot identify the semantically similar motions in the presence of significant spatial and temporal variations. This paper focuses on introducing semantic control into motion graph based motion synthesis. Relational features are implemented for describing and specifying motions at a high semantic level to cope with spatial variations within a class of semantically similar motions. Motion templates are then automatically derived based on a self-learning procedure from the training motions for capturing the spatio-temporal characteristics of an entire given class of semantically related motions. Automatic semantic annotation is performed on the unknown motion data document by identifying the presence of certain motion class from matching their respective motion class templates. Finally, the semantic control is introduced into motion graph based human motion synthesis, which provides user the higher level of intuitive semantically controls on motion synthesis. Experiments of motion synthesis demonstrate the effectiveness of the approach which achieves high quality output of human motion synthesis from motion capture database.
A Fast and Lossless Compression Algorithm for Point-Based Models Based on Local Minimal Spanning Tree
Wang Pengjie, Pan Zhigeng, Xu Mingliang, and Liu Yongkui
2011, 48(7):  1263-1268. 
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Point-based graphics has become one of the hottest topics in 3D computer graphics recently. Since point-based models are often too large to be stored and transferred in limited hardware and bandwidth easily, it is necessary to design effective compression methods. We propose an efficient and fast lossless geometry compression algorithm for point-based models. Firstly, point-sampled surface is split into many equal sized surface patches. Over the points of each patch, a minimal spanning tree is constructed and encoded in breadth first order. During this process, each point is predicted from its father in the spanning tree. Then both predicted and actual positions are broken into sign, exponent and mantissa, and their corrections are separately compressed by using arithmetic coding in the different contexts. The achieved bit-rate and time usage in our algorithm outperforms the previous lossless compression methods of point-based models. Our algorithm nicely complements those lossy compression algorithms of point-based models, and it can be used under the situation where lossy compression is not preferred.
Trusted Platform Module in Embedded System
Zhang Huanguo, Li Jing, Pan Danling, and Zhao Bo
2011, 48(7):  1269-1278. 
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How to effectively enhance the security of embedded system is an issue that is difficult to tackle and it attracts much attention in the field of information security. Relative research shows that trusted platform module (TPM) plays a crucial role in efficiently enhancing the security of information system. However, existing TPM is designed for PCs, and thus cannot satisfy the specific application needs of embedded systems. Addressing this issue, we analyze the challenges in the research of TPM posed by the embedded system environment, and propose a novel embedded trusted platform module (ETPM) that can adapt to such environment. As important components in ETPM, bus arbitration improves the platforms control ability and increases the security of embedded system; symmetric cryptography engine accelerates the speed of symmetric encryption and decryption; and system backup-recovery enhances the reliability of the embedded system. Furthermore, ETPM supports star style measurement module in embedded system environment. ETPM has been tested in trusted PDA, and experiment results show that ETPM is practical, efficient, reliable and secure.
An Software Vulnerability Number Prediction Model Based on Micro-Parameters
Nie Chujiang, Zhao Xianfeng, Chen Kai, and Han Zhengqing
2011, 48(7):  1279-1287. 
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As the cost caused by software vulnerabilities keeps increasing, people pay more and more attention to the researches on the vulnerability. Although discovering vulnerability is difficult because of the defect of vulnerability analysis, to predict the number of vulnerabilities is very useful in some domain, such as information security assessment. At present, the main methods to estimate the density of the vulnerabilities focus on the macro level, but they can not reflect the essential of vulnerability. A prediction model based on micro-parameter is proposed to predict the number of vulnerability with the micro-parameters of software, and it extracts the typical micro-parameters from some software series for the purpose of discovering the relationship between the vulnerability number and micro-parameters. With the hypothesis of vulnerability inheriting, the prediction model abstracts the micro-parameters from software and tries to find a linear relationship between the vulnerability number and some micro-parameters. This model also gives a method to predict the vulnerability number of software with its micro-parameters and the vulnerability number of its previous versions. This method is verified with 7 software series, and the results show the prediction model is effective.
A Dynamic Intrusive Intention Recognition Method Based on Timed Automata
Peng Wu, Hu Changzhen, Yao Shuping, and Wang Zhigang
2011, 48(7):  1288-1297. 
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Intrusive intention recognition is to reason and determine the ultimate purpose which an attacker wants to achieve according to his attack actions and protection measures in a specific network. In order to deal with the dynamical character of offensedefense confrontation in the network security field, an intrusive intention recognition method based on timed automata is proposed in this paper. At first a hierarchical attack path graph generation method is presented to recognize intrusive intention in order to reduce complexity of understanding and analysis. Then DS evidence theory is applied to fuse alerts of intrusion detection system to abstract the attacker’s actions and compute their confidence. How to describe state transition process of vulnerability under attack actions and system responses is presented with time automata carefully. Next in the hierarchical attack path graph, the algorithm to infer intrusive intention based on the state and dependency relationship of nodes is given. Probabilities of attack intentions are fluctuated with the ongoing intrusive actions and responses using our algorithms. According to these consequences, some recommendations can be proposed to improve the network security situation. Finally several experiments are done in a local network, and the results of the experiments prove the feasibility and validity of this method.
Interaction Feature Modeling of Virtual Object in Immersive Virtual Assembly
Zhu Yingjie, Li Chunpeng, Ma Wanli, Xia Shihong, Zhang Tielin, and Wang Zhaoqi
2011, 48(7):  1298-1306. 
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Immersive virtual assembly, a man-in-loop virtual reality system, can be used for the design and assembly of large equipment, personnel training and other fields. As one of its key technologies, how to realize “real” man operating on “virtual” objects is still a challenging task. In this paper, an interaction feature modeling method based on interactive zone and finite state machine (FSM) is proposed. Interactive zone is the visual representation of the interaction process. Different settings of interactive zone lead to different interaction monitoring functions. The possible interactions between user and object depend on object’s binding zones. Virtual objects can understand the intention of user’s action through the interactive zone, and then transit their interactive state according to the related FSM. The state of objects determines the action that user can take. According to the current state, the object will provide effective operational clues to guide the user to interact with them correctly. On that basis, task-level process control is proposed to manage the assembly process. Last but not least, direct hand interaction and indirect interaction using tools can be handled in the same way through the probe zone. This method has good versatility, extendibility and reusability of interaction process. The effectiveness is verified through the application of actual projects.
Atmospheric Quality Assessment Model Based on Immune Algorithm Optimization and Its Applications
Han Xuming, Zuo Wanli, Wang Limin, and Shi Xiaohu,
2011, 48(7):  1307-1313. 
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Owing to the low search precision of the traditional immune clonal selection algorithm, an improved immune clonal selection algorithm is proposed in this paper, which introduces vaccination strategy and local Gaussian mutation operator. The roulette selection, binary digit gene bit selection and inoculation strategies are all used during the vaccine pick-up, selection, and inoculation. Thus the phenomena without crossover for the genes of the antibody in the traditional immune clonal selection algorithm could be overcome, and the rate of the choiceness antibodies is improved. The local Gaussian mutation operator is also introduced into the improved algorithm. The step of Gaussian mutation operator is applied by self-adaptively adjusting continuously to improve the performance of local search. Besides, expanding search space strategy is applied to avoid getting into the local extremum, so the whole search capability of the proposed algorithm is greatly improved. Furthermore, an atmospheric quality assessment model based on immune clonal selection algorithm is proposed and it is applied to the field of atmospheric quality assessment. The experimental results show that the proposed algorithm could improve the precision and efficiency effectively for the problems to be solved. The proposed assessment model has good practicability and application perspective.