Dynamic Scheduling based on Particle Swarm Optimization for Cloud-based Scientific Experiments

ELINA PACINI; CRISTIAN MATEOS; CARLOS GARCÍA GARINO

Mendoza

Simposio; VI Latin American Symposium on High Performance Computing (HPCLatAm 2013); 2013

ITIC, ICB, UNCuyo

Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with dierent input data, which results in many CPU-intensive jobs and thus computing environments such as Clouds must be used. Our goal 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 ecient scheduling strategies to appropriately allocate VMs to physical resources. Here, scheduling is however challenging due to its inherent NP-completeness. We describe and evaluate a Cloud scheduler based on Particle Swarm Optimization (PSO). The main performance metrics to study are the number of Cloud users that the scheduler is able to successfully serve, 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 a 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.