Amount Of Monitoring Data Research Articles

A recommender system architecture for predictive telecom network management

Current telecom networks generate massive amounts of monitoring data consisting of observations on network faults, configuration, accounting, performance, and security. Due to the ever increasing degree of complexity of networks, coupled with specific constraints (legal, regulatory, increasing scale of management in heterogeneous networks), the traditional reactive management approaches are increasingly stretched beyond their capabilities. A new network management paradigm is required that takes a preemptive rather than reactive approach to network management. This work presents the design and specification of E-Stream, a predictive recommendationbased solution to automated network management. The architecture of E-Stream illustrates the challenges of leveraging vast volumes of management data to identify preemptive corrective actions. Such design challenges are mitigated by the components of E-Stream, which together form a single functional system. The EStream approach starts by abstracting trace information to extract sequences of events relevant to interesting incidents in the network. After observing event sequences in incoming event streams, specific appropriate actions are selected, ranked, and recommended to preempt the predicted incidents.

Scalable and efficient workload hotspot detection in virtualized environment

Workload hotspot detection is a key component of virtual machine (VM) management in virtualized environment. One of its challenges is how to effectively collect the resource usage of VMs. Also, since data centers usually have hundreds or even thousands of nodes, workload hotspot detection must be able to handle a large amount of monitoring data. In this paper, we address these two challenges. We first present a novel approach to VM memory monitoring. This approach collects memory usage data by walking through the page tables of VMs and by checking the present bit of page table entry. Second, we present a MapReduce-based approach to efficiently analyze a large amount of resource usage data of VMs and nodes. Leveraging the power of parallelism and robustness of MapReduce can significantly accelerate the detection of hotspots. Extensive simulations have been performed to evaluate the proposed approaches. The simulation results show that our approach can achieve effective estimation of memory usage with low overhead and can quickly detect workload hotspots.

Diagnosis Rule Discovery Based on Causality in the Context of Fault Diagnosis in Rotating Machinery

The rotating machineries in a factory usually have the characteristics of complex structure and highly automated logic, which generated a large amounts of monitoring data. It is an infeasible task for uses to deal with the massive data and locate fault timely. In this paper, we explore the causality between symptom and fault in the context of fault diagnosis in rotating machinery. We introduce data mining into fault diagnosis and provide a formal definition of causal diagnosis rule based on statistic test. A general framework for diagnosis rule discovery based on causality is provided and a simple implementation is explored with the purpose of providing some enlightenment to the application of causality discovery in fault diagnosis of rotating machinery.

Intelligent Pearl Disease Diagnosis Based on Rough Set - Neural Network

Abstract In view of large amount of monitoring data for Pearl disease, complexity of network structure of the traditional diagnostic neural network method, validity of disease data issues and slow training, this paper introduces the rough set theory to intelligent Pearl disease diagnosis. A method for disease diagnostics is proposed based on rough set -neural network. The rough set is used to remove the redundant attributes of decision table in order to reduce the number of input neurons and optimize neural network topology. The experimental simulation shows that the proposed algorithm can effectively improve the diagnostic rate and diagnostic accuracy. The proposed algorithm is a new way of methods for the diagnosis aquaculture technology

Research on Mathematic Model and Measuring Method of Moving Beam Attitude in Large Forging Hydraulic Press

The moving beam of large forging hydraulic press is taken as a research object. A mathematic model of moving beam’s six freedom degrees attitude is established. According to the mechanical feature and special working conditions of large forging hydraulic press, a non-contacted method for moving beam attitude measuring which based on machine vision is proposed. The basic principle and composition of the system are introduced. The system is applied in 300MN large forging hydraulic press, and large amounts of monitoring data are collected which confirm that the measuring method is effective. The research result is helpful for the safety operation and maintenance of large forging hydraulic press.

A Monitoring Sensor Management System for Grid Environments

Large distributed systems such as Computational Grids require a large amount of monitoring data be collected for a variety of tasks such as fault detection, performance analysis, performance tuning, performance prediction, and scheduling. Ensuring that all necessary monitoring is turned on and that data is being collected can be a very tedious and error-prone task. We have developed an agent-based system to automate the execution of monitoring sensors and the collection of event data.

A statistical description of the occurrence of premature ventricular contractions based on the poisson process

A stochastic model characterizing the occurrence of premature ventricular contractions (PVCs) is described and tested for consistency with clinical data. Single-lead EKG signals from 21 patients hospitalized in the CCU were recorded and the intervals between ectopic beats were stored in the computer for a subsequent time series analysis. The PVC occurrence could be adequately described by locally stationary renewal processes for 86% of the patients, and for 52% consistency with the more specific Poisson process was established. Adopting the Poisson process as a pertinent model, a statistically efficient procedure for the on-line detection of PVC rate changes was developed. The proposed detection scheme performed without significant degradation for the much wider class of data consistent with nonspecific renewal processes. The detection procedure provides for the partition of the record of PVC occurrence into a sequence of segments with constant rates, thereby greatly reducing the amount of data for monitoring.