An Analysis of Load Imbalance in Scale-out Data Serving

Despite the natural parallelism across lookups, performance of distributed key-value stores is often limited due to load imbalance induced by heavy skew in the popularity distribution of the dataset. To avoid violating service level objectives expressed in terms of tail latency, systems tend to keep server utilization low and organize the data in micro-shards, which in turn provides units of migration and replication for the purpose of load balancing. These techniques reduce the skew, but incur additional monitoring, data replication and consistency maintenance overheads. This work shows that the trend towards extreme scale-out will further exacerbate the skew-induced load imbalance, and hence the overhead of migration and replication.

The Case for RackOut: Scalable Data Serving Using Rack-Scale Systems

Edouard Bugnion, Alexandros Daglis, Babak Falsafi, Boris Robert Grot, Stanko Novakovic

To provide low latency and high throughput guarantees, most large key-value stores keep the data in the memory of many servers. Despite the natural parallelism across lookups, the load imbalance, introduced by heavy skew in the popularity distribution of keys, limits performance. To avoid violating tail latency service-level objectives, systems tend to keep server utilization low and organize the data in micro-shards, which provides units of migration and replication for the purpose of load balancing. These techniques reduce the skew, but incur additional monitoring, data replication and consistency maintenance overheads. In this work, we introduce RackOut, a memory pooling technique that leverages the one-sided remote read primitive of emerging rack-scale systems to mitigate load imbalance while respecting service-level objectives. In RackOut, the data is aggregated at rack-scale granularity, with all of the participating servers in the rack jointly servicing all of the rack’s micro-shards. We develop a queuing model to evaluate the impact of RackOut at the datacenter scale. In addition, we implement a RackOut proof-of-concept key-value store, evaluate it on two experimental platforms based on RDMA and Scale-Out NUMA, and use these results to validate the model. Our results show that RackOut can increase throughput up to 6× for RDMA and 8.6× for Scale-Out NUMA compared to a scale-out deployment, while respecting tight tail latency service-level objectives.

2016

Mitigating Load Imbalance in Distributed Data Serving with Rack-Scale Memory Pooling

Edouard Bugnion, Alexandros Daglis, Babak Falsafi, Boris Robert Grot, Stanko Novakovic, Dmitrii Ustiugov

To provide low-latency and high-throughput guarantees, most large key-value stores keep the data in the memory of many servers. Despite the natural parallelism across lookups, the load imbalance, introduced by heavy skew in the popularity distribution of keys, limits performance. To avoid violating tail latency service-level objectives, systems tend to keep server utilization low and organize the data in micro-shards, which provides units of migration and replication for the purpose of load balancing. These techniques reduce the skew but incur additional monitoring, data replication, and consistency maintenance overheads. In this work, we introduce RackOut, a memory pooling technique that leverages the one-sided remote read primitive of emerging rack-scale systems to mitigate load imbalance while respecting service-level objectives. In RackOut, the data are aggregated at rack-scale granularity, with all of the participating servers in the rack jointly servicing all of the rack’s micro-shards. We develop a queuing model to evaluate the impact of RackOut at the datacenter scale. In addition, we implement a RackOut proof-of-concept key value store, evaluate it on two experimental platforms based on RDMA and Scale-Out NUMA, and use these results to validate the model. We devise two distinct approaches to load balancing within a RackOut unit, one based on random selection of nodes—RackOut_static—and another one based on an adaptive load balancing mechanism— RackOut_adaptive. Our results show that RackOut_static increases throughput by up to 6× for RDMA and 8.6× for Scale-Out NUMA compared to a scale-out deployment, while respecting tight tail latency service-level objectives. RackOut_adaptive improves the throughput by 30% for workloads with 20% of writes over RackOut_static.

2019

Mitigating Load Imbalance in Distributed Data Serving with Rack-Scale Memory Pooling

Edouard Bugnion, Alexandros Daglis, Babak Falsafi, Boris Robert Grot, Stanko Novakovic, Dmitrii Ustiugov

2019