Hasnain Ahmed

Data center networks employ multipath topologies for delivering high bisection bandwidth. Load balancing through these multiple paths reduces latency and improves throughput. Network fabric-based schemes need customized hardware and cannot be implemented on commodity hardware. The existing end-host-based solutions have shown performance better than equal-cost multi-path (ECMP) but they use active probing and uneven congestion signal (e.g. coarse-grained RTT or ECN) that degrades performance especially in asymmetric topologies. Therefore, most of these end-host solutions cannot accurately sense congestion. End-to-end latency can effectively provide accurate path congestion information and smart probing with the help of congestion window can reduce network overhead. In this paper, we propose an edge-based congestion-aware load-balancing solution with smart probing. We combine ECN and end-to-end latency to get more accurate congestion metric and with probabilistic approach we avoid congestion at the best available path. Furthermore, we employ congestion window trend to perform smart probing that will reduce network overhead by performing probing only when required. The proposed scheme is implemented in network simulator ns-3 and flow completion time is taken as the key performance metric. The results show that the proposed scheme not only outperformed ECMP, Clove and LetFlow but also improved the performance over Hermes and CONGA at least 10–30% by employing (i) an accurate congestion signal that effectively detects congestion and asymmetries, and (ii) smart probing to reduce network overhead.

Summary: Traffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds.This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.

Today’s data centers host a variety of different applications that impose specific requirements for their flows. Applications that generate short flows are usually latency sensitive; they require their flows to be completed as fast as possible. Short flows suffer to quickly increase their sending rate due to the existing long flows occupying most of the available capacity. This problem is caused due to the slow convergence of the current data center transport protocols. In this paper, we present a buffer occupancy-based transport protocol (BOTCP) to reduce flow completion time of short flows. BOTCP consists of two parts: (i) A novel buffer occupancy-based congestion signal, and (ii) a congestion control scheme that uses the congestion signal to reduce flow completion time of short flows. The proposed buffer occupancy-based congestion signal gives a precise measure of congestion. The congestion control scheme makes a differentiated treatment of short and long flows to reduce flow completion time of short flows. The results show that BOTCP significantly improves flow completion time of short flows over the existing transport protocols in data center networks.