As the burst increasing of created and demand on information and data, the efficient solution on storage management is highly required in the cloud storage systems. As an important component of management, storage all...
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As the burst increasing of created and demand on information and data, the efficient solution on storage management is highly required in the cloud storage systems. As an important component of management, storage allocation scheme aims to use a low redundancy and also to achieve a high reliability. However, the two aims are hard to be unified. Considering the practical situation of Cloud systems, we propose a systematic storage allocation scheme to touch them both. And we also study the impact of many factors to the data reliability.
Network coding brings a new solution for IP congestion control, since more than one buffered packets can be encoded together and removed as a coded packet. This may significantly decrease the packet loss during the co...
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Network coding brings a new solution for IP congestion control, since more than one buffered packets can be encoded together and removed as a coded packet. This may significantly decrease the packet loss during the congestion, but at the cost of building redundant paths. However, how to minimize the overhead of redundant paths turns out to be a NP-hard problem. In this paper, we propose a novel approximation algorithm called FlowGrouping, which transforms the redundant paths building problem into a limited clique partition problem by increasing edge weights, and can find a good approximate solution within O(n 3 ) computation time.
The reliability issue of Exascale system is extremely serious. Traditional passive fault-tolerant methods, such as rollback-recovery, can not fully guarantee system reliability any more because of their large executin...
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The reliability issue of Exascale system is extremely serious. Traditional passive fault-tolerant methods, such as rollback-recovery, can not fully guarantee system reliability any more because of their large executing overhead and long recovering duration. Active fault tolerance is expected to become another important fault-tolerant approach for Exascale system. Focusing on system failure prediction, which is one key step of active fault tolerance, we construct online failure prediction model and research on the effective method of system status pretreatment. In order to improve the accuracy and real-time feature of current methods, the proposed Improved Adaptive Semantic Filter (IASF) method processes the latest system logs regularly, filtering useless information out of them according to their semantics. Adopting the main idea of Vector Space Model (VSM), IASF method creates Event Vector corresponding to each log record. By calculating the cosine of vectorial angle, it evaluates the semantics correlation between different log records, and then executes temporal and spatial redundant filter considering the burst feature of log records. IASF method is insensitive to the type of system log and does not introduce any expert system or domain knowledge. The experiment result shows that system can eliminate about 99.6% of useless log records after executing IASF method.
Trust systems provide a promising way to build trust relationships among users in distributed and opening systems. However, it is difficult to make quantitatively comparative analysis on different trust systems becaus...
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Trust systems provide a promising way to build trust relationships among users in distributed and opening systems. However, it is difficult to make quantitatively comparative analysis on different trust systems because of the different application settings and the lack of effective measures. This paper constructs a framework of trust systems in terms of linear algebra, which helps us model and implement different systems in a uniform way. Besides, we propose an ordering-based approach to evaluating trust systems, then give two relevant ordering-base measures. The experiment results suggests that our method provides an effective way to analyze and evaluate trust systems.
The Internet has become a vital information infrastructure for modern society. However, the concurrent nature of network introduces a wide-range of difficulties in traditional programming methodology in developing hig...
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As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy co...
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The Quiet DDoS attack becomes one of the most severely threat to the network safety, because this kind of attack completely adopts legal TCP flow while distributing its destination IP to evade various countermeasu...
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The Quiet DDoS attack becomes one of the most severely threat to the network safety, because this kind of attack completely adopts legal TCP flow while distributing its destination IP to evade various countermeasures deployed in the network. However, the high distributed degree of the destination IP becomes one characteristics of the attack. However, we think this characteristic make partially of the attack flow not match the behavior habit of network users. Inspired by this viewpoint, we propose a novel method to counter the Quiet DDoS attack based on the NBHU (network behavior habit of users). Furthermore, we carry on simulation of our method using NS2 platform, and the results show that this method can reduce the attack performance.
Cloud needs to have rapid and elastic resources supply capability, because of the fluctuant resources demand of end-users. Multi-scale resources elastic binding is an important method to provide cloud services with ra...
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Cloud needs to have rapid and elastic resources supply capability, because of the fluctuant resources demand of end-users. Multi-scale resources elastic binding is an important method to provide cloud services with rapid and elastic service capability. The most challenging problem in multi-scale resources elastic binding is how to predict the dynamic resource demand of end-users, and then decide when and to what extent multi-scale resources need elastic binding based on the prediction. In this paper, we present the prediction model based on RBF (Radial Basis Function) Network, which is used to predict end-users resource demand in advance. Compared with current prediction methods, it has faster prediction speed and higher prediction accuracy. Then we use traces data (the bandwidth demand of Web type of cloud services) collected from a real-world cloud provider: ChinaCache, as the training and testing data set to validate the method. Finally, we evaluate the predicted results using general prediction accuracy metrics. The results prove that the prediction model based on RBF network is able to resolve the decision problem in multi-scale resources elastic binding.
We consider the greedy scheduling based on the physical model in wireless networks with successive interference cancellation (SIC). There are two major stages in a scheduling scheme, link selection (to decide which li...
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ISBN:
(纸本)9781612842325
We consider the greedy scheduling based on the physical model in wireless networks with successive interference cancellation (SIC). There are two major stages in a scheduling scheme, link selection (to decide which link is scheduled next) and time slot selection (to deciding which slot is allocated to a given link). Most available schemes take a first-fit policy in the latter and strive to achieve good performance by careful selection of link ordering with respect to interference. Due to the accumulation effect and sequential detection nature of SIC, however, it is difficult to evaluate the interference of a link. As a result, many existing scheduling schemes become less efficient. In this paper, we take a new look on the problem and focus to the time slot selection stage. We define tolerance margin to measure the saturation of a link set and present two heuristic policies: one is to schedule a link to a slot such that the resulting set of links has a maximum tolerance margin;the other is to choose a slot such that the increase of tolerance margin is minimum. Simulation results show that the performance of the proposed schemes is better than the first-fit policy and is close to the optimal solution.
A Cloud may be seen as a type of flexible computing infrastructure consisting of many compute nodes, where resizable computing capacities can be provided to different customers. To fully harness the power of the Cloud...
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A Cloud may be seen as a type of flexible computing infrastructure consisting of many compute nodes, where resizable computing capacities can be provided to different customers. To fully harness the power of the Cloud, efficient data management is needed to handle huge volumes of data and support a large number of concurrent end users. To achieve that, a scalable and high-throughput indexing scheme is generally required. Such an indexing scheme must support parallel search to improve scalability. In this paper, we present a bitmap based indexing scheme for efficient data processing in the Cloud. Our approach can be summarized as follows. First, we build a local bitmap index for each compute node which only indexes data residing on the node. Second, we organize the compute nodes as a structured overlay and each node maintains a portion of the global index for the whole different data. The global index is also bitmap index to indicate the node each data resides in. Third, all bitmaps are compressed by adopting run-length coding for reducing storage requirement. We conduct extensive experiments on a LAN, and the results demonstrate that our indexing scheme is dynamic, efficient and scalable.
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