A Coflow-based Co-optimization Framework for High-performance Data Analytics

Efficient execution of distributed database operators such as joining and aggregating is critical for the performance of big data analytics. With the increase of the compute speedup of modern CPUs, reducing the networkcommunication time of these operators in large systems is becoming increasingly important, and also challenging current techniques. Significant performance improvements have been achieved by using state-of-the-art methods, such as reducing network traffic designed in the data management domain, and data flow scheduling in the data communications domain.However, the proposed techniques in both fields just view each other as a black box, and performance gains from a co-optimization perspective have not yet been explored. In this paper, based on current research in coflow scheduling,we propose a novel Coflow-based Co-optimization Framework(CCF), which can co-optimize application-level data movementand network-level data communications for distributed operators,and consequently contribute to their performance inlarge distributed environments. We present the detailed designand implementation of CCF, and conduct an experimentalevaluation of CCF using large-scale simulations on large datajoins. Our results demonstrate that CCF can always performfaster than current approaches on network communications inlarge-scale distributed scenarios