Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria; Argentin