Pareto-Optimal Cloud Bursting

Large-scale Bag-of-Tasks (BoT) applications are characterized by their massively parallel, yet independent operations. The use of resources in public clouds to dynamically expand the capacity of a private computer system might be an appealing alternative to cope with such massive parallelism. To fully realize the benefit of this `cloud bursting´, the performance to cost ratio (or cost efficiency) must be thoroughly studied and incorporated into scheduling and resource allocation strategies. In this paper, we present PANDA, a framework for static scheduling BoT applications across resources in both private and public clouds. The framework at the core incorporates a fully polynomial-time approximation scheme (FPTAS) as a novel scheduling algorithm, which generates schedules with the best trade-off point between cost and performance; hence Pareto-optimality. We have theoretically discussed the complexity and correctness of our algorithms, and experimentally verified their efficacy and practicality using ISOMAP-a widely-used nonlinear manifold method as a real-world BoT application. Our evaluation conducted in a ´multi-cloud´ environment of our 40-core private system and Amazon EC2 public cloud demonstrates the scheduling quality of PANDA is guaranteed to be within a measurable distance from the optimal solution. Results obtained from our experiments show such quality is 8 percent or less from the optimum. We also show the sensitivity and robustness of our scheduling solutions against performance errors in both resources and applications.