2507502
An Analytical Performance Model of IP-SAN
In this paper we propose a component-based analytical performance model for IP-SAN. We model four key components within IP-SAN individually and they are IP network, cache, RAID and disks. We validate these component models and compose them to give an entire IP-SAN performance model. The entire IP-SAN model analyzes the performance of an IP-SAN configuration under multiple synchronous workloads by using closed queueing network model and under asynchronous workload by using open queueing network model. We also validate the entire IP-SAN performance model against a real IP-SAN system. The theoretical results are compared with the results from a real IP-SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. To our knowledge, this is the first exploration into building analytical performance model for entire IP-SAN system and validating against a real system. We also identify areas for future research in the performance analysis of real IP-SAN.
17469824
Extending OBDD graphs for composite event matching in content-based pub/sub systems
Content-based publish/subscribe offers a convenient abstraction for the information producers and consumers, supporting a large-scale system design and evolution by integrating several distributed independent application systems. Its core problem is how to match events by predicates on the content of events. In this paper, we propose three matching models, extend the simple matching to the multi-semantic matching and introduce the temporal constraint variable. The multi-semantic matching allows using different operations in accordance with different semantics; the temporal constraint variable supports processing the discrete events in the temporal sequence. Then, we extend OBDD graphs into hierarchy coloured OBDD graphs and prove the equivalence of the transformation. At last, based on OBDD graphs the composite matching algorithm is presented and analysed. By experiments, we show the proposed algorithm is efficient.
6342666
SOWAC: a service-oriented workflow access control model
Workflow access control is the fundamental issue in workflow security. With the development of enterprise globalization and the constant re-engineering and optimizing of enterprise business, the organization becomes more dynamic and its business process is frequently changing. As a result, workflow access control turns more complicated and entails a comparatively operational mechanism. To solve the problem, in view of decoupling workflow access control model from workflow model, we propose a service-oriented workflow access control (SOWAC) model in this paper. In the SOWAC model, service is the abstraction of a task and the unit for applying access control. We present the elements of the SOWAC model and illustrate the enforcement of SOWAC with an example workflow. Then the dynamic separation of duty for the SOWAC model is proposed based on the authorization history of services. By applying SOWAC in a real workflow management system, we show that the SOWAC model is practical and effective.
7297586
Diversifying and improving business process management systems by service cooperation middleware
Emerging service oriented business process management (BPM) systems require efficient middleware infrastructure. However, current techniques for middleware are not optimal for that because they all do not have service-oriented natures themselves. This paper introduces a novel service cooperation middleware (SCM), which is designed to meet such requirements. It improves the development and integration of loosely coupled service oriented BPM system by applying several concepts such as control flow semantic and projection operation, etc. Furthermore, based on the SCM architecture, the related process integration suit viz. ISProcessIntegration (PI) can be widely applied into all kinds of scenes, for example EAI, B2B, B2C, P2P, cluster, with different topologies.
19948642
Interfacial behavior of complex hydrocarbon fluids at elevated pressures and temperatures
Unlike all the physical properties of the bulk fluid phases, interfacial tension (IFT) is unique in the sense that it relates to the interface between the two immiscible fluid phases. Hence, interfacial tension between the fluid phases can be used to infer a great deal of information about solubility, miscibility and mass transfer interactions between the two bulk fluid phases in contact.In this paper, we examine the utility of interfacial tension to characterize miscibility and mass transfer mechanisms using complex hydrocarbon fluids at elevated pressures and temperatures.For CO2/n-decane system at 37.8 /spl deg/C the minimum miscibility pressure (MMP) determined using the VIT technique (7.9 MPa) matched well with the reported MMPs by slim-tube (8.2-8.6 MPa) and rising-bubble techniques (8.8 MPa). For CO2M-C1+ n-C4+ n-CW techniques system at 71.1 /spl deg/C the VIT technique resulted in an MMP value of 12.2 MPa, which is in good agreement with the published values of slim-tube and phase diagram measurements (11.7 MPa) and analytical model predictions (11.7 MPa). This paper discusses the multiple roles of interfacial tension with supporting experimental data obtained at elevated pressures and temperatures and emphasizes the need to recognize interfacial tension as a good phase behavior indicator in fluid-fluid phase equilibria for more efficient use of this fundamental property in several other applications.
644571
Parametric inference for progressive Type-I hybrid censored data on a simple step-stress accelerated life test model
This paper considers a simple step-stress accelerated life test model under progressive Type-I hybrid censoring scheme. The progressive Type-I hybrid censoring scheme and statistical method in synthetic accelerated stresses are provided so as to decrease the lifetime and reduce the test cost. An exponentially distributed life of test units and a cumulative exposure model are assumed. The maximum likelihood estimates of the model parameters are obtained using a pivotal quantity. Two useful lemmas and a theorem are given to construct the approximate confidence intervals for the model parameters. Finally, simulation results are provided to assess the method of inference developed in this article. The simulation results show that the method does improve for large sample size.
16283183
FEATUREBridging the gaps between enterprise software and end users
The device is to be used with a parallel architecture partial response maximum likelihood (PRML) reading apparatus comprising a variable-gain input amplifier, a low-pass analog filter, a transversal continuous-time analog filter and two distinct and parallel processing channels interposed between the transversal analog filter and an RLL-NRZ decoder. The two processing channels comprise respective analog-digital converters and respective Viterbi detectors and operate according to sampling sequences that alternate with one another. The device for processing the servo signals comprises a rectifier connected to the outputs of the analog-digital converters and an integrator.
7153454
Temporal difference learning to detect unsafe system states
This paper proposes a general framework to detect unsafe states of a system whose basic realtime parameters are captured by multi-sensors. Our approach is to learn a danger level function which can be used to alert the users in advance of dangerous situations. The main challenge to this learning problem is the labelling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points where a penalty can be assigned and at the successful ends where a certain reward can be assigned. In this paper, we treat the danger level as expected future reward (penalty is regarded as negative reward) and use temporal difference (TD) learning [2] to learn a function to approximate the expected future reward. The TD learning obtains the approximation by propagating the penalty/reward observable at collapse points or successful ends to the entire feature space following some constraints. Our approach is applied to, but not limited to, the application of monitoring of driving safety and the experimental results demonstrate the effectiveness of the approach.
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.