Currently system logs are an important source of information for system administrators to monitor system behaviors and to identify system problems. The manual examining is infeasible for the complex system and the existing automated methods for identifying system problems have different disadvantages such as the extreme dependency on the source code of the system, the low accuracy of predicting or identifying the system problems, or the requirement of the balanced and labeled training data set. This paper proposes a one- class Support Vector Machine (OCSVM) based method to identify the runtime system problems. Firstly, log sequences are generated for describing the running trajectories of the monitored system by parsing log messages; Secondly, variable length n-gram features are extracted, and moreover, the log sequences are represented as feature vectors based on these variable length n-gram features and Vector Space Model (VSM). Finally, all the feature vectors of the training log sequence set, which only includes the labeled normal log sequences, are input into OCSVM. Experimental results show that it performs better to use linear kernel to train OCSVM on our feature vectors than Gaussian kernel and the size of the sliding window hardly affects the performance of our method. Moreover, the proposed method achieves better performance on unbalanced training dataset than the method based on Random Indexing (RI) and weighted SVM.

In recent years, with the prevailing usage of Web data repository over emergency field, extracting the Web data plays an important role in tracking the evolution of event. With huge and still growing Web data, an important task is to track emergency event evolution and analyze its latent events or topics over time. Unfortunately, emergency events are inherently with uncertainty because the complexity of evolution and timeliness in Web data extraction. This is why the existing techniques in topic discovery and event tracking cannot effectively work, these approaches ignore the interplay between event, latent event and network information. In this paper, we propose a new emergency event tracking model EET (Emergency Event Tracking model), to effectively track emergency event and predict its latent events from time-stamped Web data. Moreover, in order to estimate the finish time of EET over a specific event, we introduce a novel Web data extraction algorithm WDEE (Web Data Extraction for emergency Event algorithm) which utilizes limit theory to determine the periodical convergence time. Experiment results demonstrate the capability of EET via WDEE to track the event evolution.

So far open source software has been developed for several decades. Linux has gradually become one of the major operating systems. The issue that Windows application migration can be migrated to Linux is raised. However, there is great difference in the implementation mechanism between Windows and Linux. In this research, we try to build an middle layer which between application and operating system to shield the differences between the underlying operating system for the upper layer application. The middle layer provides unified fixed interface by packaging different operating system calls, so as to make the same source code can be directly translated on different operating systems with no change. It can achieve the migration of the application in source code level. In this paper, we introduce the Simulation Windows Synchronization Mechanism. we build a wait and release mechanism of multi-object, simulate Windows asynchronous procedure call (APC). And also implement Windows wait able timer on Linux. And finally, an experiments is given to test and verify our approach with the dining philosophers problem which is a classic problem in computer science.

The ability to conduct advance reservations within grid environments is crucial for applications that want to utilize distributed resources in a predictable and efficient way. Advance reservations are essential for supporting deadline driven applications and the co-allocation of distributed resources. Further, advance reservations can significantly enhance the capabilities of resource brokers. Many Local Resource Management Systems (LRMSs), e.g. Torque/Maui, PBSPro and LSF, provide support for advance reservations. However, these capabilities are usually not accessible via the grid middleware. This paper presents and evaluates a job broker architecture that addresses this deficit and provides support for advance reservation on grid-level as well as a reservation-aware grid resource broker. A core component of the presented job broker architecture is the Earliest Start Time Estimator (ESE), which predicts queuing delays at grid resources. The job broker service is part of the Migol system - a grid middleware that addresses the fault tolerance of applications by offering capabilities such as automatic monitoring and recoveries.

Spacecraft automatic testing has been a focused theme in spacecraft technology researches for over a decade. However, there is less attention on the spacecraft automatic testing technology based on workflow methodology. In this paper we introduce workflow technology into spacecraft testing field in order to improve the automation level of spacecraft testing. Firstly we present the architecture of spacecraft testing system based on workflow. Then a spacecraft testing process definition language STPDL is proposed based on XPDL, which is used to model testing processes. In addition, we give the detail execution processes and core scheduling algorithm in the spacecraft testing execution system. At the end of the paper we investigate the efficiency of the testing execution system.

Since media data in streaming media system are high volume and latency sensitive, transmission security of them have been a challenge task. To solve the problem, partial encryption and other media encryption schemes have been proposed. However those schemes are tradeoffs between security and efficiency. In a multi-stream system, using one media encryption scheme to protect all the media streams will induce either unscalability or insecurity. In this paper, the idea of partial encryption is generalized from encrypting a subset of one stream to multi-streams. A mathematical model based dynamic optimal selective control mechanism for multi-datastream security is proposed. Since the simplified model is 0-1 knapsack problem, which is NP-hard, a greedy heuristic incremental approximate algorithm is designed. Theoretical and experimental analyses show the performance of the algorithm is effective enough to support real-time applications. A prototype has been implemented in our Admire system, and used in a confidential project.

Alarm correlation plays an important role in improving the service and reliability in modern telecommunication networks. Most previous research of alarm correlation did not consider the effects of noise data in the database. This paper focuses on the method of discovering alarm correlation rules from a database containing noise data. We firstly define two parameters, win-freq and win-add, as the measures of noise data and then present a robust search algorithm to solve the problem. At different size of win-freq and win-add, the experiments on the alarm database containing noise data show that the robust search algorithm can discover more rules with the bigger size of win-add. We also compare, by experiments, two different interestingness measures of confidence and correlation.