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The protection of sensitive information is of utmost importance for organizations. The complexity and dynamism of modern businesses are forcing a re-think of traditional protection mechanisms. In particular, a priori policy en-forcement mechanisms are often complemented with auditing mechanisms that rely on an a posteriori analysis of logs recording users' activities to prove confor-mity to policies and detect policy violations when a valid explanation of confor-mity does not exist. However, existing auditing solutions require that the infor-mation necessary to assess policy compliance is available for the analysis. This assumption is not realistic. Indeed, a good deal of users' activities may not be under the control of the IT system and thus they cannot be logged. In this paper we tackle the problem of accessing policy compliance in presence of incomplete logs. In particular, we present an auditing framework to assist analysts in find-ing a valid explanation for the events recorded in the logs and to pinpoint policy violations if such an explanation does not exist, when logs are incomplete. We also introduce two strategies for the refinement of plausible explanations of con-formity to drive analysts along the auditing process. Our framework has been implemented on top of CIFF, an abductive proof procedure, and the efficiency and effectiveness of the refinement strategies evaluated.

Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. From the HPC domain, the Periscope Tuning Framework (PTF) is extended to perform dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach. ; © The Author(s) 2017. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. From the HPC domain, the Periscope Tuning Framework (PTF) is extended to perform dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach. ; Web of Science ; 99 ; 8 ; 745 ; 727