Visualizing and Modeling Categorical Time Series Data

Categorical time series data can not be effectively visualized and modeled using methods developed for ordinary data. The arbitrary mapping of categorical data to ordinal values can have a number of undesirable consequences. New techniques for visualizing and modeling categorical time series data are described, and examples are presented using computer and communications network traces

Two Performance Tool Design Issues and CHITRA's Solutions

Two issues arising in the design of trace-file based performance analysis tools are discussed: handling categorical rather than just numeric data and correctly inferring system behavior from trace data by using not one but multiple trace files. The issues are illustrated using the problem of determining whether damaging oscillations occur in the all points shortest path algorithm when used for routing messages between processors. Solutions used by the Chitra trace analysis tool are discussed

CHITRA94: A Tool to Dynamically Charaterize Ensembles of Traces for Input Data Modeling and Output Analsis

CHITRA94 is the third generation of a comprehensive system to visualize, transform, statistically analyze, and model ensembles of trace data and validate the resultant models. The tool is useful for deriving input data models from trace data as well as for analysis of trace data output by a simulation. The tool uses an stochastic (possibly no-Markovian) process as its fundamental modeling component. The tool is unique in several respects. First, it focuses on the dynamic characterization of systems. Consequently it includes tests for homogeneity of ensembles, stationarity of individual traces, and the ability to partition ensembles into transient and stationary pieces. Second, CHITRA94 is a scalable performance tool: its methods are designed to work with an arbitrary number of traces in an ensemble so that a user can examine the variability of system behaviors across different traces (representing different observation periods, different system configurations, etc.). To analyze multiple traces, the user can either (1) map the traces to a model that characterizes the dynamic behavior or (2) use a novel visualization, the mass evolution graph, that shows the probability mass distribution evolution of one or more traces. A mass evolution graph is easily constructed from trace data, and the derivative of the paths it contains yield an estimate of probability mass as a function of time. Third, it analyzes categorical, not just numerical, time series data. Categorical data arises naturally in many computer and communication system modeling problems. Fourth, it includes a library of transforms that reduce the state space of the stochastic process generated. Fifth, CHITRA94 is implemented as a user extensible collection of small programs, organized as a library, which allows the user to write new library modules that use existing modules to automate analysis procedures. Instead, CHITRA94 may be invoked from command line, under a GUI, or integrated with another tool (e.g., a simulation model development or CASE tool). The use of CHITRA94 is illustrated on a variety of trace data, and the extensibility is illustrated on the problem of partitioning an ensemble of 60 traces of compressed entertainment video into mutually exclusive, exhaustive, and homogeneous subsets from which a hierarchical workload model is derived

Chitra94: A Tool to Dynamically Characterize Ensembles of Traces for Input Data Modeling and Output Analysis

Chitra94 is the third generation of a comprehensive system to visualize, transform, statistically analyze, and model ensembles of trace data and validate the resultant models. The tool is useful for deriving input data models from trace data as well as for analysis of trace data output by a simulation. The tool uses a stochastic (possibly non-Markovian) process as its fundamental modeling component. The tool is unique in several respects. First, it focuses on the dynamic characterization of systems. Consequently it includes tests for homogeneity of ensembles, stationarity of individual traces, and the ability to partition ensembles into transient and stationary pieces. Second, Chitra94 is a scalable performance tool: its methods are designed to work with an arbitrary number of traces in an ensemble so that a user can examine the variability of system behaviors across different traces (representing different observation periods, different system configurations, etc.) To analyze multipl..

Multimedia Traffic Analysis Using CHITRA95

We describe how to investigate collections of trace data representing network delivery of multimedia information with CHITRA95, a tool that allows a user to visualize, query, statistically analyze and test, transform, and model collections of trace data. CHITRA95 is applied to characterize World Wide Web (WWW) traffic from three workloads: students in a classroom of network-connected workstations, graduate students browsing the Web, undergraduates browsing educational and other materials, as well as traffic on a courseware repository server. We explore the inter-access time of files on a server (i.e., recency), the hit rate from a proxy server cache, and the distributions of file sizes and media types requested. The traffic study also yields statistics on the effectiveness of caching to improve transfer rates. In contrast to past WWW traffic studies, we analyze client as well as server traffic; we compare three workloads rather than drawing conclusions from one workload; and we analyze tcpdump logs to calculate the performance improvement in throughput that an end user sees due to caching

Queue Layouts and Staircase Covers of Matrices

A connection between a queue layout of an undirected graph and a staircase cover of its adjacency matrix is established. The connection is exploited to establish a number of combinatorial results relating the number of vertices, the number of edges, and the queue number of a queue layout. The staircase notion is generalized to that of an (h,w)- staircase, and an efficient algorithm to optimally cover a matrix with (h,w)- staircases is presented. The algorithm is applied to problems of monotonic subsequences and to the maxdominance problem of Atallah and Kosaraju