Load balancing for heterogeneous traffic in datacenter networks

Abstract

In modern datacenter networks (DCNs), the overwhelming heterogeneous flows have various stringent demands, ranging from delay-sensitive short flows, throughput-sensitive long flows to best-effort flows without deadline. Recently, many load balancing schemes are proposed to deliver good performance for datacenter applications. However, the existing solutions cannot meet all the above requirements simultaneously. Especially, the short flows experience head-of-line blocking due to queued behind the long and best-effort flows. The long flows often suffer from throughput degradation due to bursty congestion. To solve these issues, we present LBT, a traffic-differentiated load balancer. The key design point of LBT is to adaptively adjust the switching granularity of long flows and best-effort flows according to the two calculated switching thresholds based on the traffic strength. Specifically, under heavy load, the switching granularity is increased to guarantee required bandwidth capacity for delay-sensitive short flows to finish quickly. In contrary, the switching granularity is reduced to enable the long flows to make full of parallel equal-cost paths. Moreover, we adopt different routing strategies for the three different categories flows. We conduct NS-2 simulations to evaluate the effectiveness of LBT. The experimental results show that LBT significantly reduces the average flow completion time of short flows by up to 55.9% ∼ 65.4% compared to the state-of-the-art solutions and achieves high throughput for long flows concurrently.