SI-based scheduling of parameter sweep experiments on federated clouds

Abstract

Scientists and engineers often require huge amounts of computing power to execute their experiments. This work focuses on the federated Cloud model, where custom virtual machines (VM) are launched in appropriate hosts belonging to different providers to execute scientific experiments and minimize response time. Here, scheduling is performed at three levels. First, at the broker level, datacenters are selected by their network latencies via three policies –Lowest-Latency-Time-First, First-Latency-Time-First, and Latency-Time- In-Round–. Second, at the infrastructure level, two Cloud VM schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for mapping VMs to appropriate datacenter hosts are implemented. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. Simulated experiments show that the combination of policies at the broker level with ACO and PSO succeed in reducing the response time compared to using the broker level policies combined with Genetic Algorithms.