SORMSYS: Towards a Resource Management Platform for Self-Organizing Large Scale Distributed Systems

2010 ◽  
Author(s):  
F Pop
Keyword(s):  
Distributed Systems ◽  
Resource Management ◽  
Large Scale ◽  
Management Platform ◽  
Self Organizing

scholarly journals Special Issue on Algorithms for the Resource Management of Large Scale Infrastructures

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 200 ◽  
Author(s):  
Danilo Ardagna ◽  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Energy Consumption ◽  
Distributed Systems ◽  
Resource Management ◽  
Large Scale ◽  
Efficient Solutions ◽  
Special Issue ◽  
Emerging Trends ◽  
Innovative Solutions ◽  
Application Deployment ◽  
Global Energy Consumption

Modern distributed systems are becoming increasingly complex as virtualization is being applied at both the levels of computing and networking. Consequently, the resource management of this infrastructure requires innovative and efficient solutions. This issue is further exacerbated by the unpredictable workload of modern applications and the need to limit the global energy consumption. The purpose of this special issue is to present recent advances and emerging solutions to address the challenge of resource management in the context of modern large-scale infrastructures. We believe that the four papers that we selected present an up-to-date view of the emerging trends, and the papers propose innovative solutions to support efficient and self-managing systems that are able to adapt, manage, and cope with changes derived from continually changing workload and application deployment settings, without the need for human supervision.

A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems

2012 ◽  
Vol 63 (9) ◽  
pp. 1409-1423 ◽  
Author(s):  
Alexandra Olteanu ◽  
Florin Pop ◽  
Ciprian Dobre ◽  
Valentin Cristea
Keyword(s):  
Distributed Systems ◽  
Resource Management ◽  
Large Scale ◽  
Dynamic Rescheduling

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Workshop on large-scale distributed systems for information retrieval

2007 ◽  
Vol 41 (2) ◽  
pp. 83-88
Author(s):  
Flavio P. Junqueira ◽  
Vassilis Plachouras ◽  
Fabrizio Silvestri ◽  
Ivana Podnar
Keyword(s):  
Information Retrieval ◽  
Distributed Systems ◽  
Large Scale

scholarly journals Self-organizing Clustering: Non-hierarchical Clustering for Large Scale DNA Sequence Data

2007 ◽  
Vol 3 ◽  
pp. 193-197 ◽  
Author(s):  
Kou Amano ◽  
Hiroaki Ichikawa ◽  
Hidemitsu Nakamura ◽  
Hisataka Numa ◽  
Kaoru Fukami-Kobayashi ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Sequence Data ◽  
Dna Sequence Data ◽  
Self Organizing
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