Dynamic Work Load Balancing for Compute Intensive Application Using Parallel and Hybrid Programming Models on CPU-GPU Cluster

2018 ◽  
Vol 15 (6) ◽  
pp. 2336-2340 ◽  
Author(s):  
B. N Chandrashekhar ◽  
H. A Sanjay
Keyword(s):  
Load Balancing ◽  
Work Load ◽  
Programming Models ◽  
Gpu Cluster ◽  
Hybrid Programming ◽  
Dynamic Work

Investigation of Next Machine Selection Methods for Real-time Work Load Balancing

2002 ◽  
Vol 2002 (0) ◽  
pp. 23-24
Author(s):  
Toru EGUCHI ◽  
Fuminori OBA ◽  
Satoru TOYOOKA ◽  
Yasuhiro SATO
Keyword(s):  
Load Balancing ◽  
Real Time ◽  
Work Load ◽  
Selection Methods ◽  
Machine Selection ◽  
Time Work

Linear programming models for load balancing

1991 ◽  
Vol 18 (1) ◽  
pp. 59-64 ◽  
Author(s):  
Manlio Gaudioso ◽  
Pasquale Legato
Keyword(s):  
Linear Programming ◽  
Load Balancing ◽  
Programming Models

scholarly journals Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

2020 ◽  
Vol 9 (4) ◽  
pp. 45-51
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Work Load ◽  
Np Hard Problem ◽  
Multi Agent ◽  
Time And Energy ◽  
User Priority

Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.

Optimizing leaf sweeping and collection in the Argentine city of Trenque Lauquen

2020 ◽  
pp. 0734242X2092259 ◽  
Author(s):  
Diego Delle Donne ◽  
Valeria Di Tomaso ◽  
Guillermo Duran
Keyword(s):  
Linear Programming ◽  
Load Distribution ◽  
Travelling Salesman Problem ◽  
Work Load ◽  
Programming Models ◽  
Solution Strategy ◽  
Travelling Salesman ◽  
Working Day ◽  
The City ◽  
Asymmetric Travelling Salesman Problem

A solution strategy based on integer linear programming models has been developed for leaf sweeping operations in the Argentine city of Trenque Lauquen. The aim is to achieve efficiency in the assignment of sweepers to city blocks, the identification of leaf bag deposit points and the routes to be followed by collection trucks for leaf bag pickup. Previous to this strategy, sweeper assignments were improvised and inefficient, with blocks often left unswept. Furthermore, no method was available for accurately determining the number of sweepers needed to ensure either full coverage of all city zones within the working day or a balanced work load distribution across all sweepers. Application of the solution strategy by the city has resulted in efficient definitions of sweeper requirements while optimizing sweeper assignments such that all blocks are covered. Once the strategy is fully implemented, the number of bag deposit points under the manual definitions should be reduced by roughly one-half and the total travel distance of the truck routes, modelled as an asymmetric travelling salesman problem, should be cut by 10–15% with the consequent savings in time, vehicle use and fuel consumption.

Performance of Hybrid Programming Models for Multiscale Cardiac Simulations: Preparing for Petascale Computation

2011 ◽  
Vol 58 (10) ◽  
pp. 2965-2969 ◽  
Author(s):  
B. J. Pope ◽  
B. G. Fitch ◽  
M. C. Pitman ◽  
J. J. Rice ◽  
M. Reumann
Keyword(s):  
Programming Models ◽  
Hybrid Programming

scholarly journals Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

2013 ◽  
Vol 24 (1) ◽  
pp. 144-157 ◽  
Author(s):  
Dong Li ◽  
Bronis R. de Supinski ◽  
Martin Schulz ◽  
Dimitrios S. Nikolopoulos ◽  
Kirk W. Cameron
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Programming Models ◽  
Hybrid Programming ◽  
Efficient Resource
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