logo biblio digital exactas
logo twitter logo instagram logo whatsapp logo mail

Navegar

Colección

Datos Estadísticas

Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

Computational Mechanics (CM) concerns the use of computational methods to study phenomena under the principles of mechanics. A representative CM application is parameter sweep experiments (PSEs), which involves the execution of many CPU-intensive jobs and thus computing environments such as Clouds must be used. We focus on federated Clouds, where PSEs are processed via virtual machines (VM) that are lauched in hosts belonging to different datacenters, minimizing both the makespan and flowtime. Scheduling is performed at three levels: a) broker, where datacenters are selected based on their network latencies via three policies, b) infrastructure, where two bio-inspired schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for VM-host mapping in a datacenter are implemented, and c)VM, where jobs are assigned into the preallocated VMs based on job priorities. Simulated experiments performed with job data from two real PSEs show that our scheduling approach allows for a more agile job handling while reducing PSE makespan and flowtime.

Registro:

Documento: Artículo
Título:A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
Autor:Pacini, E.; Mateos, C.; Garino, C.G.; Careglio, C.; Mirasso, A.
Filiación:ITIC Research Institute-UNCuyo University, Mendoza, Argentina
Facultad de Ciencias Exactas y Naturales-UNCuyo University, Mendoza, Argentina
ISISTAN Research Institute-UNICEN University, Tandil, Buenos Aires, Argentina
Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
Facultad de Ingeniería-UNCuyo University, Mendoza, Argentina
Palabras clave:Ant colony optimization; Cloud computing; Computational mechanics; Particle swarm optimization; Scheduling; Ant colony optimization; Artificial intelligence; Cloud computing; Computational mechanics; Mechanics; Scheduling; Ant Colony Optimization (ACO); Computing environments; CPU-intensive; Federated clouds; Minimizing makespan; Network latencies; Simulated experiments; Virtual machines; Particle swarm optimization (PSO)
Año:2016
Volumen:31
Número:3
Página de inicio:1731
Página de fin:1743
DOI: http://dx.doi.org/10.3233/JIFS-152094
Título revista:Journal of Intelligent and Fuzzy Systems
Título revista abreviado:J. Intelligent Fuzzy Syst.
ISSN:10641246
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10641246_v31_n3_p1731_Pacini

Referencias:

  • Agostinho, L., Feliciano, G., Olivi, L., Cardozo, E., Guimaraes, E., A Bio-inspired approach to provisioning of virtual resources in federated Clouds (2011) 9thDASC, IEEE, pp. 598-604
  • Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., Buyya, R., Cloudsim: A toolkit for modeling and simulation of Cloud Computing environments and evaluation of resource provisioning algorithms (2011) Software: Practice & Experience, 41 (1), pp. 23-50
  • Careglio, C., Mirasso, A., Garcá Garino, C., Estudio numérico de una columna crucifome en grandes deformaciones (2007) Mecánica Computacional, 24, pp. 129-143
  • Coutinho, R., Drummond, L., Frota, Y., De Oliveira, D., Optimizing virtual machine allocation for parallel scientific workflows in federated clouds (2015) Future Generation Computer Systems, 46, pp. 51-68
  • De Oliveira, G., Ribeiro, E., Ferreira, D., Aráujo, A., Holanda, M., Walter, M., ACOsched: A scheduling algorithm in a federated cloud infrastructure for bioinformatics applications (2013) 2013 BIBM, IEEE, pp. 8-14
  • Deevena Raju, B., Pandarinath, P., Prasad, G., An image reconstruction technique based on ipso-dwt under varying crack (2015) Journal of Intelligent & Fuzzy Systems, 29 (4), pp. 1643-1652
  • Feller, E., Rilling, L., Morin, C., Energy-Aware Ant Colony based workload placement in Clouds (2011) 12th GCA, IEEE, pp. 26-33
  • Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L., A multi-objective ant colony system algorithm for virtual machine placement in cloud computing (2013) Journal of Computer and System Sciences, 79 (8), pp. 1230-1242
  • Garcá Garino, C., Gabaldón, F., Goicolea, J.M., Finite element simulation of the simple tension test in metals (2006) Finite Elements in Analysis and Design, 42 (13), pp. 1187-1197
  • Garcá Garino, C., Ribero Vairo, M., Andá Fagés, S., Mirasso, A., Ponthot, J.-P., Numerical simulation of finite strain viscoplastic problems (2013) Journal of Computational and Applied Mathematics, 246, pp. 174-184
  • Ghanbari, S., Othman, M., A priority based job scheduling algorithm in cloud computing (2012) Procedia Engineering, 50, pp. 778-785
  • Jeyarani, R., Nagaveni, N., Vasanth Ram, R., Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence (2012) Future Generation Computer Systems, 28 (5), pp. 811-821
  • Jung, J., Jung, S., Kim, T., Chung, T., A study on the Cloud simulation with a network topology generator (2012) World Academy of Science, Engineering&Technology, 6 (11), pp. 303-306
  • Kennedy, J., Swarm intelligence (2006) Handbook of Nature-Inspired and Innovative Computing, pp. 187-219
  • Kessaci, Y., Melab, N., Talbi, E.-G., A pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation (2013) Cluster Computing, 16 (3), pp. 451-468
  • Lucas-Simarro, J., Moreno-Vozmediano, R., Montero, R., Llorente, I., Scheduling strategies for optimal service deployment across multiple clouds (2013) Future Generation Computer Systems, 29 (6), pp. 1431-1441
  • Mahdiyeh, E., Hussain, S., Mohammad, K., Azah, M., A survey of the state of the art in particle swarm optimization (2012) Research Journal of Applied Sciences, Engineering and Technology, 4 (9), pp. 1181-1197
  • Makris, N., Plastic torsional buckling of cruciform compression members (2003) Journal of Engineering Mechanics, 129 (6), pp. 689-696
  • Malik, S., Huet, F., Caromel, D., Latency based group discovery algorithm for network aware Cloud scheduling (2014) Future Generation Computer Systems, 31, pp. 28-39
  • Mateos, C., Pacini, E., Garcá, C., Garino,AnACO-inspired algorithm for minimizing weighted flowtime in Cloud-based parameter sweep experiments (2013) Advances in Engineering Software, 56, pp. 38-50
  • Pacini, E., Mateos, C., Garcá Garino, C., Dynamic scheduling of scientific experiments on clouds using ant colony optimization (2013) 3rd PARENG
  • Pacini, E., Mateos, C., Garcá Garino, C., Distributed job scheduling based on Swarm Intelligence: A survey (2014) Computers & Electrical Engineering, 40 (1), pp. 252-269
  • Pacini, E., Mateos, C., Garcá Garino, C., Multi-objective swarm intelligence schedulers for online scientific clouds (2014) Computing, , Press
  • Pacini, E., Mateos, C., Garcá Garino, C., SI-based scheduling of parameter sweep experiments on federated clouds (2014) HighPerformance Computing vol.845 of CCIS, pp. 28-42
  • Pacini, E., Mateos, C., Garcá, C., Garino, Balancing throughput and response time in online scientific clouds via ant colony optimization (2015) Advances in Engineering Software, 84, pp. 31-47
  • Sedeño Noda, A., Raith, A., A dijkstra-like method computing all extreme supported non-dominated solutions of the biobjective shortest path problem (2015) Computers & Operations Research, 57, pp. 83-94
  • Somasundaram, T., Govindarajan, K., CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science Cloud (2014) Future Generation Computer Systems, 34, pp. 47-65
  • Tavares Neto, R., Godinho Filho, M., Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research (2013) Engineering Applications of Artificial Intelligence, 26 (1), pp. 150-161
  • Tchana, A., Son, G., Broto, L., DePalma, N., Hagimont, D., Two levels autonomic resource management in virtualized IaaS (2013) Future Generation Computer Systems, 29 (6), pp. 1319-1332
  • Tordsson, J., Montero, R., Moreno Vozmediano, R., Llorente, I., Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers (2012) Future Generation Computer Systems, 28 (2), pp. 358-367
  • Zhan, Z., Liu, X., Gong, Y., Zhang, J., Chung, H., Li, Y., Cloud computing resource scheduling and a survey of its evolutionary approaches (2015) ACM Computing Surveys, 47 (4), pp. 1-63

Citas:

---------- APA ----------
Pacini, E., Mateos, C., Garino, C.G., Careglio, C. & Mirasso, A. (2016) . A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds. Journal of Intelligent and Fuzzy Systems, 31(3), 1731-1743.
http://dx.doi.org/10.3233/JIFS-152094
---------- CHICAGO ----------
Pacini, E., Mateos, C., Garino, C.G., Careglio, C., Mirasso, A. "A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds" . Journal of Intelligent and Fuzzy Systems 31, no. 3 (2016) : 1731-1743.
http://dx.doi.org/10.3233/JIFS-152094
---------- MLA ----------
Pacini, E., Mateos, C., Garino, C.G., Careglio, C., Mirasso, A. "A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds" . Journal of Intelligent and Fuzzy Systems, vol. 31, no. 3, 2016, pp. 1731-1743.
http://dx.doi.org/10.3233/JIFS-152094
---------- VANCOUVER ----------
Pacini, E., Mateos, C., Garino, C.G., Careglio, C., Mirasso, A. A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds. J. Intelligent Fuzzy Syst. 2016;31(3):1731-1743.
http://dx.doi.org/10.3233/JIFS-152094