Network Performance Aware Optimizations on IaaS Clouds

Abstract

Network performance aware optimizations have long been a hot research topic to optimize distributed applications on traditional network environments. However, those optimization techniques rely on a few measurements on pair-wise network performance, and such direct use of network measurements is no longer valid on Infrastructure-as-a-service (IaaS) clouds. First, the direct calibration is ineffective. Network performance measurements may not represent the long-term performance (informally the stable component inside network performance) because of virtualization and network performance interference in the cloud. Second, the direct calibration is inefficient because the measurement overhead of all pair-wise link performance in a cluster becomes prohibitively high as the number of instances increases. To effectively and efficiently utilize existing network performance aware optimizations on IaaS clouds, we propose to reduce the measurement overhead and decouple the constant component from the dynamic network performance while minimizing the difference between the network performance and the constant component. For effectiveness, we use the constant component to guide the network performance aware optimizations. For efficiency, we exploit a non-negative matrix factorization (NMF) method to reduce the calibration overhead. Furthermore, we observe a tradeoff between effectiveness and efficiency, and develop an adaptive approach to capture this tradeoff. We demonstrate effectiveness and efficiency of our approach by adopting network performance aware optimizations on two kinds of basic applications, collective communications of MPI and generic topology mapping, and two real-world applications, namely N-body and conjugate gradient (CG). Our experiments on Amazon EC2 and simulations demonstrate significant calibration overhead reduction and performance improvement on guiding network performance aware optimizations, when comparing our approach to other state-of-the-art approaches.