14513289
Mining Console Logs for Large-Scale System Problem Detection
The console logs generated by an application contain messages that the application developers believed would be useful in debugging or monitoring the application. Despite the ubiquity and large size of these logs, they are rarely exploited in a systematic way for monitoring and debugging because they are not readily machine-parsable. In this paper, we propose a novel method for mining this rich source of information. First, we combine log parsing and text mining with source code analysis to extract structure from the console logs. Second, we extract features from the structured information in order to detect anomalous patterns in the logs using Principal Component Analysis (PCA). Finally, we use a decision tree to distill the results of PCA-based anomaly detection to a format readily understandable by domain experts (e.g. system operators) who need not be familiar with the anomaly detection algorithms. As a case study, we distill over one million lines of console logs from the Hadoop file system to a simple decision tree that a domain expert can readily understand; the process requires no operator intervention and we detect a large portion of runtime anomalies that are commonly overlooked.
3222518
Control Considerations for Scalable Event Processing
The growth in the scale of systems and networks has created many challenges for their management, especially for event processing. Our premise is that scaling event processing requires parallelism. To this end, we observe that event processing can be divided into intra-event processing such as filtering and inter-event processing such as root cause analysis. Since intra-event processing is easily parallelized, we propose an architecture in which intra-event processing elements (IAPs) are replicated to scale to larger event input rates. We address two challenges in this architecture. First, the IAPs are subject to overloads that require effective flow control, a capability that was not present in the components we used to build IAPs. Second, we need to balance the loads on IAPs to avoid creating resource bottlenecks. These challenges are further complicated by the presence of disturbances such as CPU intensive administrative tasks that reduce event processing rates. We address these challenges using designs based on control theory, a technique for analyzing stability, accuracy, and settling times. We demonstrate the effectiveness of our approaches with testbed experiments that include a disturbance in the form of a CPU intensive application.
850350
Online System Problem Detection by Mining Patterns of Console Logs
We describe a novel application of using data mining and statistical learning methods to automatically monitor and detect abnormal execution traces from console logs in an online setting. Different from existing solutions, we use a two stage detection system. The first stage uses frequent pattern mining and distribution estimation techniques to capture the dominant patterns (both frequent sequences and time duration). The second stage use principal component analysis based anomaly detection technique to identify actual problems. Using real system data from a 203-node Hadoop [1] cluster, we show that we can not only achieve highly accurate and fast problem detection, but also help operators better understand execution patterns in their system.
2239051
Detecting large-scale system problems by mining console logs
Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We then analyze these features using machine learning to detect operational problems. We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software's internals.
1951107
CCSMOMS:A Composite Communication Scheme for Mobile Object Management System
In this paper, we discuss the design and implementation of a mobile object management system that makes use of the existing GSM (Global System for Mobile Communication) networks and it's extension-GPRS (general packet radio service) for data communication. We use a novel composite communication scheme (CCS), which uses SMS (short message service) and GPRS to achieve reliable and economical communication between mobile objects and the control center. We also use unified data management (UDM) for control center management, which significantly reduces the cost of maintaining separate data centers. We have deployed our services in two medium-sized cities with real users. The cost evaluation shows the high performance, usability and cost-effectiveness of this service in real production environment
7346557
Predictive Control for Dynamic Resource Allocation in Enterprise Data Centers
It is challenging to reduce resource over-provisioning for enterprise applications while maintaining service level objectives (SLOs) due to their time-varying and stochastic workloads. In this paper, we study the effect of prediction on dynamic resource allocation to virtualized servers running enterprise applications. We present predictive controllers using three different prediction algorithms based on a standard auto-regressive (AR) model, a combined ANOVA-AR model, as well as a multi-pulse (MP) model. We compare the properties of the predictive controllers with an adaptive integral (I) controller designed in our earlier work on controlling relative utilization of resource containers. The controllers are evaluated in a hypothetical virtual server environment where we use the CPU utilization traces collected on 36 servers in an enterprise data center. Since these traces were collected in an open-loop environment, we use a simple queuing algorithm to simulate the closed-loop CPU usage under dynamic control of CPU allocation. We also study the controllers by emulating the utilization traces on a test bed where a Web server was hosted inside a Xen virtual machine. We compare the results of these controllers from all the servers and find that the MP-based predictive controller performed slightly better statistically than the other two predictive controllers. The ANOVA-AR-based approach is highly sensitive to the existence of periodic patterns in the trace, while the other three methods are not. In addition, all the three predictive schemes performed significantly better when the prediction error was accounted for using a feedback mechanism. The MP-based method also demonstrated an interesting self-learning behavior
15069022
An integrated baseball digest system using maximum entropy method
In this paper, we propose a novel system that is able to automatically detect and classify highlights from baseball game videos in TV broadcast. The digest system gives complete indexes of a baseball game which cover all of the status changes in a game. We achieve this by seamlessly integrating image, audio and speech clues using a maximum entropy based method. What distinguishes our system from previous ones is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through machine learning process. Integration of multimedia features is important because with the current state-of-the-art image and audio analysis techniques, most image and audio features we can extract from videos are very low level, and detecting/classifying sports game highlights based on features from single medium are doomed to yield poor performances. Acquiring domain knowledge through learning process is preferred over heuristic rules because machine learning process is more powerful for discovering and expressing domain knowledge. We perform extensive experiments on game videos including various stadiums, teams and broadcasted by different TV stations.
26522967
Maximum entropy model-based baseball highlight detection and classification
In this paper, we propose a novel system that is able to automatically detect and classify baseball highlights by seamlessly integrating image, audio, and speech clues using a unique framework based on maximum entropy model (MEM). What distinguishes our system is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through automatic machine learning processes. Our MEM-based framework provides a simple platform capable of integrating multimedia features as well as their contextual information in a uniform fashion. Unlike the Hidden Markov Model and the Bayes Network-based approaches, this framework does not need to explicitly segment and classify the input video into states during its data modeling process, and hence remarkably simplifies the training data creation and the highlight detection/classification tasks. Experimental evaluations demonstrate the superiority of our proposed baseball highlight detection system in its capability of detecting more major baseball highlights, and in its overall accuracy of detecting these major highlights.
14994096
Application of Synthetic Neural Network for Fault Diagnosis of Steam Turbine Flow Passage
It is difficult to determine the normal value for fault diagnosis of steam turbine flow passage, because the flow passage fault or regenerative system fault may result in the decrease of the relative internal efficiency of steam turbine. Based on analyzing the feature of flow passage and regenerative system, the flow passage condition of steam turbine can be evaluated by relative internal efficiency of stage groups. According to various effect factors, the methods to determine the normal value of relative internal efficiency of stage groups is proposed in this paper, which include application of synthetic neural network. Compared the measured values with these normal values, the operating condition of steam turbine flow passage can be evaluated and the detailed reasons of fault can be diagnosed.
11893705
View Synthesis for Virtual Walk through in Real Scene Based on Catadioptric Omnidirectional Images
This paper proposes an approach to more conveniently and efficiently create virtual walk through in real scene from catadioptric omni directional images via view synthesis. Acquisition and unwarping process of omnidirectional image are discussed firstly, then, according to the speciality of cylindrical panoramic image, we utilize the advantage of epiline-sampling method for cylindrical image, which samples reference images along epilines as much as possible grounding on epipolar geometry. As to novel view generation after image rectification, a corresponding interpolation algorithm based on rectified images is developed. Results of proposed approach applied to both synthetic and real scene are given at the end of this paper. Experiments show that our method can reduce distortion and pixel information losing from image rectification obviously meanwhile maintain scene information well.
1723065
An approach for realizing privacy-preserving web-based services
The past decade has witnessed a phenomenal growth in the number of users who routinely use the web to obtain information, conduct research, or carry out financial transactions. While the ability of the web to provide customized information and financial services has boosted personal and business productivity, it has raised significant concerns regarding an individual’s privacy. The privacy concerns are exacerbated further with the advent of more complex services that involve multiple service providers, as it raises the potential of personal information being shared across these providers in ways that weren’t intended by the owner of the information. To illustrate the increased privacy concerns raised by composite web-based services, consider a web-based service that allows an individual to research and order over-the-counter medication (or health supplements) at a low cost, while ensuring that there are no adverse interactions based on the individuals current prescription. Such a service will require access to the individual’s personal medical history, and will also require interaction with other entities such as web-based medical references, discount pharmacies and creditcard companies. Users may not want their personal health histories to be shared with discount pharmacies or credit card companies. Similarly, they may not want their credit card information to be shared with providers of free on-line information. Web-based service providers have long recognized the importance of addressing user privacy concerns, and taken steps to state their policies regarding the storage and use of personal user information. Unfortunately, since these policies are stated in English, they are imprecise and ambiguous, posing problems in accountability and enforceability, as well as in checking compatibility between consumer and provider policies. Indeed, the verbose nature of these policies discourage most users from even reading them! To address the above problem, we present a new approach where the consumers as well as providers can express their privacy concerns in a formal way. Specifically, consumers express their requirements in the form of policies, while providers specify their use of consumer data using models. Our approach automates compatibility checking between policies and models. If there is an incompatibility, the consumer is informed how she can refine her policies in order to use the service. If she does not want to change her policies in any way, the approach passes on additional privacy requirements to the provider. Service access can continue in case