Autonomous FPGA Fault Handling through Competitive Runtime Reconfiguration

2006 ◽  
Author(s):  
R.F. DeMara ◽  
Kening Zhang
Keyword(s):  
Fault Handling ◽  
Runtime Reconfiguration

scholarly journals Self-healing trans-cloud applications

Computing ◽  
2021 ◽  
Author(s):  
Antonio Brogi ◽  
Jose Carrasco ◽  
Francisco Durán ◽  
Ernesto Pimentel ◽  
Jacopo Soldani
Keyword(s):  
Open Source ◽  
Cloud Services ◽  
Self Healing ◽  
Controlled Experiments ◽  
Fault Handling ◽  
Extensive Evaluation ◽  
Cloud Providers ◽  
Heterogeneous Cloud ◽  
Cloud Applications ◽  
Unstable States

AbstractTrans-cloud applications consist of multiple interacting components deployed across different cloud providers and at different service layers (IaaS and PaaS). In such complex deployment scenarios, fault handling and recovery need to deal with heterogeneous cloud offerings and to take into account inter-component dependencies. We propose a methodology for self-healing trans-cloud applications from failures occurring in application components or in the cloud services hosting them, both during deployment and while they are being operated. The proposed methodology enables reducing the time application components rely on faulted services, hence residing in “unstable” states where they can suddenly fail in cascade or exhibit erroneous behaviour. We also present an open-source prototype illustrating the feasibility of our proposal, which we have exploited to carry out an extensive evaluation based on controlled experiments and monkey testing.

scholarly journals A Fault Handling Process for Faults in District Heating Customer Installations

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3169
Author(s):  
Sara Månsson ◽  
Marcus Thern ◽  
Per-Olof Johansson Kallioniemi ◽  
Kerstin Sernhed
Keyword(s):  
Data Analysis ◽  
Fault Detection ◽  
Negative Impact ◽  
District Heating ◽  
Customer Data ◽  
Heating Systems ◽  
Fault Handling ◽  
District Heating Systems ◽  
High Return ◽  
Using Data

Faults in district heating (DH) customer installations cause high return temperatures, which have a negative impact on both current and future district heating systems. Thus, there is a need to detect and correct these faults soon after they occur to minimize their impact on the system. This paper, therefore, suggests a fault handling process for the detection and elimination of faults in DH customer installations. The fault handling process is based on customer data analysis since many faults manifest in customer data. The fault handling process was based on an analysis of the results from the previous fault handling studies, as well as conducting a workshop with experts from the DH industry. During the workshop, different organizational and technical challenges related to fault handling were discussed. The results include a presentation of how the utilities are currently working with fault handling. The results also present an analysis of different organizational aspects that would have to be improved to succeed in fault handling. The paper also includes a suggestion for how a fault handling process based on fault detection using data analysis may be designed. This process may be implemented by utilities in both current and future DH systems that interested in working more actively with faults in their customer installations.

scholarly journals Modular fault handling in a network-transparent programming language

2012 ◽  
Author(s):  
Gery Debongnie ◽  
Raphael Collet ◽  
Sebastien Doeraene ◽  
Peter Van Roy
Keyword(s):  
Programming Language ◽  
Fault Handling

Clustering error messages produced by distributed computing infrastructure during the processing of high energy physics data

2021 ◽  
Vol 36 (10) ◽  
pp. 2150070
Author(s):  
Maria Grigorieva ◽  
Dmitry Grin
Keyword(s):  
Distributed Computing ◽  
Large Scale ◽  
High Energy Physics ◽  
High Energy ◽  
Machine Learning Algorithms ◽  
Service Failures ◽  
Fault Handling ◽  
Scientific Experiments ◽  
Computing Centers ◽  
Distributed Computing Infrastructures

Large-scale distributed computing infrastructures ensure the operation and maintenance of scientific experiments at the LHC: more than 160 computing centers all over the world execute tens of millions of computing jobs per day. ATLAS — the largest experiment at the LHC — creates an enormous flow of data which has to be recorded and analyzed by a complex heterogeneous and distributed computing environment. Statistically, about 10–12% of computing jobs end with a failure: network faults, service failures, authorization failures, and other error conditions trigger error messages which provide detailed information about the issue, which can be used for diagnosis and proactive fault handling. However, this analysis is complicated by the sheer scale of textual log data, and often exacerbated by the lack of a well-defined structure: human experts have to interpret the detected messages and create parsing rules manually, which is time-consuming and does not allow identifying previously unknown error conditions without further human intervention. This paper is dedicated to the description of a pipeline of methods for the unsupervised clustering of multi-source error messages. The pipeline is data-driven, based on machine learning algorithms, and executed fully automatically, allowing categorizing error messages according to textual patterns and meaning.

Our Knowledge Management Hubble May Need Glasses

2018 ◽  
Vol 14 (1) ◽  
pp. 30-50 ◽  
Author(s):  
William H. Money ◽  
Stephen J. Cohen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Real Time ◽  
Complex Data ◽  
Data Systems ◽  
Fault Handling ◽  
Architectural Framework ◽  
Big Data Systems ◽  
And Performance ◽  
Streaming Big Data

This article analyzes the properties of unknown faults in knowledge management and Big Data systems processing Big Data in real-time. These faults introduce risks and threaten the knowledge pyramid and decisions based on knowledge gleaned from volumes of complex data. The authors hypothesize that not yet encountered faults may require fault handling, an analytic model, and an architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of the data. New architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time will contribute to organizational knowledge and performance. System designs must mitigate faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This article concludes that with improved designs, real-time Big Data systems may continuously deliver the value of streaming Big Data.

Optimal runtime reconfiguration strategies for systolic arrays

2009 ◽  
Author(s):  
Arpith C. Jacob ◽  
Jeremy D. Buhler ◽  
Roger D. Chamberlain
Keyword(s):  
Systolic Arrays ◽  
Runtime Reconfiguration
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