Raparo: Resource-Level Angle-Based Parallel Routing for FPGAs

2019 ◽  
Author(s):  
Minghua Shen ◽  
Nong Xiao
Keyword(s):  
Resource Level

scholarly journals Calcein staining of calcified structures in pearl oyster Pinctada margaritifera and the effect of food resource level on shell growth

Aquaculture ◽  
2011 ◽  
Vol 313 (1-4) ◽  
pp. 149-155 ◽  
Author(s):  
Clémentine Linard ◽  
Yannick Gueguen ◽  
Jacques Moriceau ◽  
Claude Soyez ◽  
Bélinda Hui ◽  
...  
Keyword(s):  
Shell Growth ◽  
Food Resource ◽  
Pearl Oyster ◽  
Pinctada Margaritifera ◽  
Resource Level ◽  
Effect Of Food

Abnormal behavior detection using resource level to service level metrics mapping in virtualized systems

2020 ◽  
Vol 102 ◽  
pp. 680-700 ◽  
Author(s):  
Souhila Benmakrelouf ◽  
Cédric St-Onge ◽  
Nadjia Kara ◽  
Hanine Tout ◽  
Claes Edstrom ◽  
...  
Keyword(s):  
Service Level ◽  
Abnormal Behavior ◽  
Behavior Detection ◽  
Resource Level ◽  
Abnormal Behavior Detection

Supporting CPU-based guarantees in cloud SLAs via resource-level QoS metrics

2012 ◽  
Vol 28 (8) ◽  
pp. 1295-1302 ◽  
Author(s):  
Íñigo Goiri ◽  
Ferran Julià ◽  
J. Oriol Fitó ◽  
Mario Macías ◽  
Jordi Guitart
Keyword(s):  
Resource Level ◽  
Qos Metrics

A PARALLEL ARCHITECTURE FOR AI NONLINEAR PLANNING

1990 ◽  
Vol 04 (02) ◽  
pp. 215-232 ◽  
Author(s):  
SUKHAN LEE ◽  
KYUSIK CHUNG
Keyword(s):  
Conflict Resolution ◽  
Distributed Processing ◽  
Parallel Architecture ◽  
State Level ◽  
Conflict Detection ◽  
Parallel And Distributed Processing ◽  
Resource Level ◽  
Planning Algorithm ◽  
Conflict Detection And Resolution ◽  
Nonlinear Planning

This paper presents a resource-level conflict detection and conflict resolution scheme which is combined with a state-level backward planning algorithm and provides efficient conflict detection and global conflict resolution for nonlinear planning. The scheme keeps track of the usage of individual resources during planning, and constructs a Resource-Usage Flow (RUF) structure (based on which conflict detection and resolution is accomplished). The RUF structure allows the system to perform minimal and nonredundant operations for conflict detection and resolution. Furthermore, resource-level conflict detection and resolution facilitates problem decomposition in terms of resources, thereby providing easy implementation in a parallel and distributed processing environment. Performance analysis indicates that the proposed architecture has a speed-up factor of the average depth of a plan network, D(Na), compared to the distributed NOAH, where Na (the total number of action nodes at the completion of planning) and D(Na) are considerably larger than the number of resources involved in planning as well as the number of initial goal states.

scholarly journals Ensuring Resource-Level Quality for Services on Grids

2010 ◽  
Author(s):  
André Lage Freitas ◽  
Jean-Louis Pazat ◽  
Nikos Parlavantzas
Keyword(s):  
Resource Level
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