QuAD: A quorum protocol for adaptive data management in the cloud

Abstract

More and more companies move their data to the Cloud which is able to cope with the high scalability and availability demands due to its pay-as-you-go cost model. For this, databases in the Cloud are distributed and replicated across different data centers. According to the CAP theorem, distributed data management is governed by a trade-off between consistency and availability. In addition, the stronger the provided consistency level, the higher is the generated coordination overhead and thus the impact on system performance. Nevertheless, many OLTP applications demand strong consistency and use ROWA(A) for replica synchronization. ROWA(A) protocols eagerly update all (or all available) replicas and thus generate a high overhead for update transactions. In contrast, quorum-based protocols consider only a subset of sites for eager commit. This reduces the overhead for update transactions at the cost of reads, as the latter also need to access several sites. Existing quorum-based protocols do not consider the load of sites when determining the quorums; hence, they are not able to adapt at run-time to load changes. In this paper, we present QuAD, an adaptive quorum-based replication protocol that constructs quorums by dynamically selecting the optimal quorum configuration w.r.t. load and network latency. Our evaluation of QuAD based on Amazon EC2 shows that it considerably outperforms both static quorum protocols and dynamic protocols that neglect site properties in the quorum construction process. Index Terms-distributed data management; replication.

LABAREDA: A Predictive and Elastic Load Balancing Service for Cloud-Replicated Databases

“…Instead, the proposed strategy builds on the present workload of the system to balance it. Fetai, Stiemer and Schuldt (2017) developed QuAD, an adaptive quorum protocol for providing strong data consistency to a fully replicated database system with homogeneous nodes. One possible event that causes quorum readjustment is changes in node properties, such as workload.…”

“…Several works [Xiong et al 2011] [Fetai and Schuldt 2013] [Moreira et al 2014] [Fetai et al 2017] have used prediction models to observe the future behavior of database workloads in order to fulfill some quality of service requirement. The use of predictive models can provide support in solving some of the challenges, such as allowing a database service to determine if certain features are required to meet certain SLAs and to adjust, according to model estimation, appropriate database configurations [Mozafari et al 2013].…”

“…• 101 are recursively based on the past and need the predicted load for the last period [Fetai et al 2017]. Using EMA, the first value is initialized to the arithmetic mean of the first n measures, which can be observed in the 3 equation.…”

“…Some solutions [Xiong et al 2011] [Sakr andLiu 2012] [Moon et al 2013] [Fetai and Schuldt 2013] [Pippal et al 2015] [Fetai et al 2017] that utilize database replication use load balancing strategies to distribute workloads among the replicas. However, a small amount of solutions found in our research use elasticity combined with predictive aspects in order to observe the future impact of the workload in relation to the use of existing cloud resources and SLA compliance.…”