Section: New Results

Interactive Analysis and Visualization of Large Distributed Systems

  • In [13] , we review the methodology that we use to visualize information for large-scale data-set. Our approach uses tools from information theory to define a trade-off between the loss of information and the compactness of the representation. This methodology is applied to spatio-temporal representation of traces of execution in [30] , [16] , [17] , [32] . In these papers, we show how to build a concise overview of the trace behavior as the result of a spatio-temporal data aggregation process. The experimental results show that this approach can help the quick and accurate detection of anomalies in traces containing up to two hundred million events.

  • Trace analysis graphical user environments have to provide different views on trace data, to really help provide insights on the traced application behavior. In [22] , [35] , we propose an open and modular software architecture, the FrameSoC workbench, that defines clear principles for view engineering and for view consistency management. The FrameSoC workbench has been successfully applied in real trace analysis use-cases. This work has also been tested on real scenario coming from a collaboration with ST Microelectronic [25] .

  • In [7] , we design a novel prediction method with Bayes model to predict a load fluctuation pattern over a long-term interval, in the context of Google data centers. All of the prediction methods are evaluated using Google trace with 10,000+ heterogeneous hosts. Experiments show that our Bayes method improves the long-term load prediction accuracy by up 5 to 50%, compared to other state-of-the-art methods.