Lr-Stream: Using latency and resource aware scheduling to improve latency and throughput for streaming applications

Abstract

Low latency and high throughput are two of the most critical performance requirements for big data stream computing systems. As multi-source high-speed data streams arrive in real time, it is essential to study latency-aware and resource-aware scheduling to reduce latency and increase throughput. In this paper, we propose a latency- and resource-aware scheduling framework (Lr-Stream) targeting stream-oriented big data applications. Our key contributions can be summarized as follows: (1) a stream topology model and resource scheduling model Lr-Stream are proposed, aiming at optimizing latency and throughput; (2) a latency-aware scheduling strategy and a resource-aware scheduling strategy are proposed; (3) Lr-Stream together with monitor, calculator, and deployment function modules are implemented, and integrated into Apache Storm; (4) system metrics are thoroughly evaluated from latency- and resource-aware perspective on a typical distributed stream computing platform. Experimental results demonstrate that the proposed Lr-Stream yields significant performance improvements in terms of reducing system latency and increasing system throughput.