Reliability-Oriented Strategies for Multichip Module Based Mission Critical Industry Applications

The availability is defined as the portion of time the system remains operational to serve its purpose. In mission critical applications (MCA), the availability of power converters are determinant to ensure continue productivity and avoid financial losses. Multichip Modules (MCM) are widely adopted in such applications due to the high power density and reduced price; however, the high number of dies inside a compact package results in critical thermal deviations among them. Moreover, uneven power flow, inhomogeneous cooling and accumulated degradation, potentially result in thermal deviation among modules, thereby increasing the temperature differences and resulting in extra temperature in specific subset of devices. High temperatures influences multiple failure mechanisms in power modules, especially in highly dynamic load profiles. Therefore, the higher failure probability of the hottest dies drastically reduces the reliability of mission critical power converters. Therefore, this work investigate reliability-oriented solutions for the design and thermal management of MCM-based power converters applied in mission critical applications. The first contribution, is the integration of a die-level thermal and probabilistic analysis on the design for reliability (DFR) procedure, whereby the temperature and failure probability of each die are taken into account during the reliability modeling. It is demonstrated that the dielevel analysis can obtain more realistic system-level reliability of MCM-based power converters. Thereafter, three novel die-level thermal balancing strategies, based on a modified MCM - with more gate-emitter connections - are proposed and investigated. It is proven that the temperatures inside the MCM can be overcame, and the maximum temperate reduced in up to 8 %

Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

Adaptive and reliable multipath provisioning for media transfer in SDN-based overlay networks

Traditional routing in the Internet is best-effort which makes it challenging for video streaming since no throughput, jitter, delay or loss rate is guaranteed. As different paths have different characteristics, path differentiation such as multipath routing is a promising technique to be used for meeting QoS requirements of media-intensive applications. Using overlay networks different paths are offered which enable more flexibility in QoS and congestion control while the reliability of the connections is enhanced. Software Defined Networking (SDN) is known to be a promising solution to the problems of routing as it provides fine-grained control over packet handling. Relying on SDN, we propose an adaptive multipath provisioning scheme ensuring maximal bandwidth and resiliency of media transfer in overlay networks. The scheme is a time slot-based approach which dynamically finds multipaths. It relies on both active probing and traffic prediction. The experimental results confirm that a more accurate prediction together with more frequent probing lead to fewer number of path re-calculation and also indicate that the proposed scheme enhances the reliability of connections while a more balanced load is achieved in the network compared to the shortest path-based scheme. (C) 2017 Elsevier B.V. All rights reserved