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Abstract:

Federated Clouds are infrastructures arranging physical resources from different datacenters. A Cloud broker intermediates between users and datacenters to support the execution of jobs through Virtual Machines (VM). We exploit federated Clouds to run CPU-intensive jobs, in particular, Parameter Sweep Experiments (PSE). Specifically, we study a broker-level scheduler based on Ant Colony Optimization (ACO), which aims to select the datacenters taking into account both the network latencies and the availability of resources. The less the network latency, the lower the influence on makespan. Moreover, when more VMs can be allocated in datacenters with lower latency, more physical resources can be taken advantage of, and hence job execution time decreases. Then, once our broker-level scheduler has selected a datacenter to execute jobs, VMs are allocated in the physical machines of that datacenter by another intra-datacenter scheduler based on ACO. Experiments performed using CloudSim and job data from a real PSE show that our ACO-based broker-level scheduler succeeds in reducing the makespan compared to similar schedulers based on latency-aware greedy and round robin heuristics. © 2016 IEEE.

Registro:

Documento: Conferencia
Título:Broker Scheduler based on ACO for Federated Cloud-based scientific experiments
Autor:Pacini, E.; Mateos, C.; Garino, C.G.
Filiación:ITIC Research Institute, Facultad de Ciencias Exactas y Naturales, UNCuyo and CONICET, Mendoza, Argentina
ISISTAN-CONICET and UNICEN, Tandil, Buenos Aires, Argentina
ITIC Research Institute and Facultad de Ingeniería, UNCuyo, Mendoza, Argentina
Palabras clave:Ant Colony Optimization; Broker; Federated Cloud; Scheduling; Ant colony optimization; Artificial intelligence; Optimization; Routers; Scheduling; Ant Colony Optimization (ACO); Broker; CPU-intensive; Federated clouds; Network latencies; Physical resources; Scientific experiments; Virtual machines; Job shop scheduling
Año:2016
DOI: http://dx.doi.org/10.1109/ARGENCON.2016.7585239
Título revista:2016 IEEE Biennial Congress of Argentina, ARGENCON 2016
Título revista abreviado:IEEE Bienn. Congr. Argent., ARGENCON
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814673_v_n_p_Pacini

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Citas:

---------- APA ----------
Pacini, E., Mateos, C. & Garino, C.G. (2016) . Broker Scheduler based on ACO for Federated Cloud-based scientific experiments. 2016 IEEE Biennial Congress of Argentina, ARGENCON 2016.
http://dx.doi.org/10.1109/ARGENCON.2016.7585239
---------- CHICAGO ----------
Pacini, E., Mateos, C., Garino, C.G. "Broker Scheduler based on ACO for Federated Cloud-based scientific experiments" . 2016 IEEE Biennial Congress of Argentina, ARGENCON 2016 (2016).
http://dx.doi.org/10.1109/ARGENCON.2016.7585239
---------- MLA ----------
Pacini, E., Mateos, C., Garino, C.G. "Broker Scheduler based on ACO for Federated Cloud-based scientific experiments" . 2016 IEEE Biennial Congress of Argentina, ARGENCON 2016, 2016.
http://dx.doi.org/10.1109/ARGENCON.2016.7585239
---------- VANCOUVER ----------
Pacini, E., Mateos, C., Garino, C.G. Broker Scheduler based on ACO for Federated Cloud-based scientific experiments. IEEE Bienn. Congr. Argent., ARGENCON. 2016.
http://dx.doi.org/10.1109/ARGENCON.2016.7585239