Mazda A. Marvasti

A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance

We adapt the classic cusum change-point detection algorithm to handle non-stationary sequences that are typical with network surveillance applications. The proposed algorithm uses a defined timeslot structure to take into account time varying distributions, and uses historical samples of observations within each timeslot to facilitate a nonparametric methodology. Our proposed solution includes an on-line screening feature that fully automates the implementation of the algorithm and eliminates the need for manual oversight up until the point where root cause analysis begins.

Cusum techniques for timeslot sequences with applications to network surveillance

We develop two cusum change-point detection algorithms for data network monitoring applications where numerous and various performance and reliability metrics are available to aid with the early identification of realized or impending failures. We confront three significant challenges with our cusum algorithms: (1) the need for nonparametric techniques so that a wide variety of metrics can be included in the monitoring process, (2) the need to handle time varying distributions for the metrics that reflect natural cycles in work load and traffic patterns, and (3) the need to be computationally efficient with the massive amounts of data that are available for processing. The only critical assumption we make when developing the algorithms is that suitably transformed observations within a defined timeslot structure are independent and identically distributed under normal operating conditions. To facilitate practical implementations of the algorithms, we present asymptotically valid thresholds. Our research was motivated by a real-world application and we use that context to guide the design of a simulation study that examines the sensitivity of the cusum algorithms.

Abnormality analysis of streamed log data

We examine the determination of abnormality of streamed data using the statistical structure of the meta-data associated with it. The vital need for such a subject within a heterogeneous log based environment in real-time comes from the fact that most cloud based applications will use text-based logging as a means of reporting application behavior. The sheer volume of such logs makes retrospective analysis infeasible due to large processing and storage requirements. Our approach is based on conversion of the original data stream into meta-data (graph) and revealing the dominating (normal) statistical patterns within it. Real-time analysis of the stream compared with the meta-data model determines the degree of anomaly of the current data. The resulting graph also reveals the fundamental structure (“behavioral footprint”) of the data beyond the sources (physical or virtual devices) and processes.

Ranking and Updating Beliefs Based on User Feedback: Industrial Use Cases

Incorporation of user feedback in enterprise management products can greatly enhance our understanding of modern technology challenges and amplify the ability for those products to home in to user environments. In this paper we present an entropy-based confidence determination approach to process user feedback data (direct or indirect) to automatically rank and update the beliefs of any recommender system. Several examples of application of this method are discussed in the context of VMware products. Moreover, an optimization algorithm is demonstrated for adaptive thresholding of monitoring flows based on user ratings of generated alerts effectiveness.

A Nonparametric Cusum Algorithm for Timeslot Sequences with Applications to Network Surveillance

We adapt the classic cusum change-point detection algorithm for applications to data network monitoring where various and numerous performance and reliability metrics are available to aid with early identification of realized or impending failures. Three significant challenges that must be overcome are: 1) the need for a nonparametric technique so that a wide variety of metrics (including discrete metrics) can be included in the monitoring process, 2) the need to handle time varying distributions for the metrics that reflect natural cycles in work load and traffic patterns, and 3) the need to be computationally efficient with data processing of the massive number of metrics that are available from IT environments.Correct navigational behavior of a Web application is essential to its reliability. An effective means to improve our confidence in the correct behavior of a Web application is to test it by exploring the possible navigation among the Web pages at client side: The tester carries out the testing by consecutively clicking the hyperlinks along with some possible search parameters and checking whether the returned Web pages are as expected. Traditional conformance testing techniques based on finite state machines can be adopted in this setting to automatically generate suitable test sequences to traverse among client pages. This paper presents our initial result in improving T-method for test sequence generation to considerably reduce its length by making use of the characteristics provided by the Web browsers. Our experiments show a 29% - 68% saving on the test sequence lengths compared to the direct application of T-method.

Systems management in the age of cloud

Summary form only given. With the growing demand for cloud computing and the need for elastic environments comes new challenges in systems manageability. The abstraction of physical from application to the end-user computing and the need to expand and contract capacity on demand has introduced a new paradigm for systems and application management. Transiency of performance counters and the inability to get appropriate sample sizes renders traditional frequency based statistical analysis techniques ineffective. This talk will look into current research at VMware regarding these topics and the general trends in systems management in the age of the hybrid cloud.