RouteGA: A Grid Load Balancing Algorithm with Genetic Support

Motivated by the first process allocation limitations of the original Route load balancing algorithm, this paper presents RouteGA (Route with Genetic Algorithm support) which considers historical information about parallel application executions in order to optimize the first scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify process occupation at the launch moment. Such occupation is used to parameterize a genetic algorithm responsible for optimizing the process allocation on heterogeneous computing environments such as Grids. Results confirm RouteGA over- performs the original Route, which had previously overper-formed others from literature.

[1]  Rodrigo Fernandes de Mello,et al.  Model for Simulation of Heterogeneous High-Performance Computing Environments , 2006, VECPAR.

[2]  Laurence T. Yang,et al.  A routing load balancing policy for grid computing environments , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[3]  Henri Casanova,et al.  A realistic network/application model for scheduling divisible loads on large-scale platforms , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[4]  Marcos José Santana,et al.  Scheduling based on the impact over process communication of parallel applications , 2005, 19th International Symposium on High Performance Computing Systems and Applications (HPCS'05).

[5]  D. Fogel,et al.  Basic Algorithms and Operators , 1999 .

[6]  Laurence T. Yang,et al.  An On‐Line Approach for Classifying and Extracting Application Behavior on Linux , 2006 .

[7]  Warren Smith,et al.  Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance , 1999, JSSPP.

[8]  Evgueni Dodonov,et al.  GridBox: securing hosts from malicious and greedy applications , 2004, MGC '04.

[9]  Shikharesh Majumdar,et al.  Scheduling in multiprogrammed parallel systems , 1988, SIGMETRICS 1988.

[10]  Roger W. Hockney.  The science of computer benchmarking , 1995, Software, environments, tools.

[11]  Mukesh Singhal,et al.  Load distributing for locally distributed systems , 1992, Computer.

[12]  R. Hinterding,et al.  Representation, mutation and crossover issues in evolutionary computation , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[13]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[14]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[15]  Dror G. Feitelson,et al.  Job Scheduling in Multiprogrammed Parallel Systems , 1997 .

[16]  Arjan J. C. van Gemund,et al.  LLB: A fast and effective scheduling algorithm for distributed-memory systems , 1999, Proceedings 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing. IPPS/SPDP 1999.

[17]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[18]  Rajkumar Buyya,et al.  High Performance Cluster Computing , 1999 .

[19]  Rodrigo Fernandes de Mello,et al.  A new migration model based on the evaluation of processes load and lifetime on heterogeneous computing environments , 2004, 16th Symposium on Computer Architecture and High Performance Computing.

[20]  Luciano José Senger,et al.  A new migration model based on the evaluation of processes load and lifetime on heterogeneous computing environments , 2004 .

[21]  Rajkumar Buyya,et al.  High Performance Cluster Computing: Architectures and Systems , 1999 .

[22]  Henri Casanova,et al.  Practical divisible load scheduling on grid platforms with APST-DV , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[23]  Dror G. Feitelson,et al.  Improved Utilization and Responsiveness with Gang Scheduling , 1997, JSSPP.