# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
###### Abstract  
A surge of interest in data-intensive computing has led to a drastic increase in the demand for data centers. Given this growing popularity, data centers are becoming a primary contributor to the increased consumption of energy worldwide. To mitigate this problem, this paper revisits DVFS (Dynamic Voltage Frequency Scaling), a well-known technique to reduce the energy usage of processors, from the viewpoint of distributed systems. Distributed data systems typically adopt a replication facility to provide high availability and short latency. In this type of architecture, the replicas are maintained in an asynchronous manner, while the master synchronously operates via user requests. Based on this relaxation constraint of replica, we present a novel DVFS technique called Concerto, which intentionally scales down the frequency of processors operating for the replicas. This mechanism can achieve considerable energy savings without an increase in the user-perceived latency. We implemented Concerto on Redis 6.0.1, a commercial-level distributed key-value store, demonstrating that all associated performance issues were resolved. To prevent a delay in read queries assigned to the replicas, we offload the independent part of the read operation to the fast-running thread. We also empirically demonstrate that the decreased performance of the replica does not cause an increase of the replication lag because the inherent load unbalance between the master and replica hides the increased latency of the replica. Performance evaluations with micro and real-world benchmarks show that Redis saves 32% on average and up to 51% of energy with Concerto under various workloads, with minor performance losses in the replicas. Despite numerous studies of the energy saving in data centers, to the best of our best knowledge, Concerto is the first approach that considers clock-speed scaling at the aggregate level, exploiting heterogeneous performance constraints across data nodes. distributed systems; database systems; energy efficiency; power management Article  
In line with above arguments, this paper takes advantage of an opportunity to reduce the energy used by data centers by perspicaciously applying the DVFS (Dynamic Voltage and Frequency Scaling) technique in distributed data systems. DVFS scales down the frequency and voltage of the processors when the performance constraints are not stringent, instilling a trade-off the performance and savings. State-of-the-art distributed data systems, such as Redis [6], MongoDB [7], Cassandra [8], and Ceph [9], typically provide a replication feature. By maintaining multiple copies of data with the data systems, they provide high reliability, good availability, and rapid responsiveness to end users. Table 1 summarizes the replication model employed in popular distributed data systems. The most popular replication model is the single-master and multiple-replica model, while a few distributed data systems, such as Dynamo [12] and Cassandra [8], allow multiple masters, preferring high availability to data consistency. In the single-master and multiple-replica model, all writes go through a master; the master reflects data changes to its dataset first and then propagates these changes to multiple replicas asynchronously. This mechanism makes it easy to coordinate the concurrent writes occurring with regard to the data without violating the consistency requirement. We focus on the fact that the performance requirements are disparate across the data nodes of the system due to its asymmetric structure with replication. For master instances, rapid processing capability is expected because these instances are synchronous with user requests. In contrast, replica instances have a relaxed performance requirement in terms of latency because they have little impact on the user-facing performance. This property presents a possibility to gain a benefit by sacrificing the performance up to tolerable limits.