Section: New Results

Autonomous Computing

Participants : Cécile Germain-Renaud, Michèle Sebag, Balázs Kégl, Yusik Kim, Julien Nauroy, Dawei Feng.

Within the classical objectives of Autonomics (self-*), two transversal lines of research have emerged.

Coping with non-stationarity

Most existing work on modeling the dynamics of grid behavior assumes a steady-state system and concludes to some form of long-range dependence (slowly decaying correlation) in the associated time-series. But the physical (economic and sociologic) processes governing the grid behavior dispel the stationarity hypothesis. [68] proposes a categorization of the methods integrate non-stationarity into grid modeling. [9] considers a specific class of models: a sequence of stationary processes separated by breakpoints. The model selection question is now defined as identifying the breakpoints and fitting the processes in each segment, together with a validation methodology that empirically addresses the current lack of theoretical results concerning the quality of the estimated model parameters. Even when stationarity is acceptable, the markovian assumption might be too bold. [54] integrate Echo State Network-based regression into a reinforcement learning in continuous state space for fitting the Q function, with application to reactive grid scheduling.

Traces mining

In order for an autonomic system to continuously infer knowledge from its monitoring (the so-called MAPE-K, Monitor-Analyze-Plan-Execute-Knowledge) loop, heterogeneous sources of data have to be integrated. [37] exemplifies two use cases of the Grid Observatory data on evaluating the perfomance of the major EGI scheduler, and blackhole detection.

The Green Computing Observatory [38] data include the detailed monitoring of the processors and motherboards, as well as the global site information, such as overall consumption and overall cooling. The data schema for publication is grounded in an ontology of measurements developed in collaboration with the MIS (Modélisation, Information et Systèmes) laboratory of University Picardie Jules Verne.

[42] proposes a new approach for analyzing behavioral traces: as most of them are indeed text documents, state of the art techniques in text mining, including Latent Dirichlet Allocation, can be exploited . The advantages are twofold: providing some level of explanation inferred from the data; and a relatively scalable way to capture the temporal variability of the behavior of interest, while retaining the full dimensionality of the problem at hand. A promising perspective for combining this approach and inferred segmentation has been identified and is currently explored.