Finding Constant from Change: Revisiting Network Performance Aware Optimizations on IaaS Clouds

摘要

Network performance aware optimizations have long been an effective approach to optimizing distributed applications on traditional network environments. However, the assumptions of network topology or direct use of several measurements of pair-wise network performance for optimizations are no longer valid on IaaS clouds. Virtualization hides network topology from users, and direct use of network performance measurements may not represent long-term performance. To enable existing network performance aware optimizations on IaaS clouds, we propose to decouple constant component from dynamic network performance while minimizing the difference by a mathematical method called RPCA (Robust Principal Component Analysis). We use the constant component to guide network performance aware optimizations and demonstrate the efficiency of our approach by adopting network aware optimizations for collective communications of MPI and generic topology mapping as well as two real-world applications, N-body and conjugate gradient (CG). Our experiments on Amazon EC2 and simulations demonstrate significant performance improvement on guiding the optimizations.