KeyByte: Unlock the Potential of your Cloud Databases

OptimusCloud: For your cloud-hosted databases

Achieving cost and performance efficiency for cloud-hosted databases requires exploring a large configuration space, including the parameters exposed by the database along with the variety of VM configurations available in the cloud. Even small deviations from an optimal configuration have significant consequences on performance and cost. Existing systems that automate cloud deployment configuration can select near-optimal instance types for homogeneous clusters of virtual machines and for stateless, recurrent data analytics workloads.
OptimusCloud finds optimal performance-per-$ cloud deployments for NoSQL database applications.

It has the following three distinctive features:(1) It considers heterogeneous cluster configurations,(2) It jointly optimizes database and VM configurations, and(3) It dynamically adjusts configuration as workload behavior changes.
We evaluate OptimusCloud with two clustered NoSQL systems: Cassandra and Redis, using three representative workloads and show that it provides 40% higher throughput/$ and 4.5X lower 99-th percentile latency on average compared to state-of-the-art prior systems, CherryPick, Selecta, and SOPHIA.

Sophia: For your on-premise databases

Reconfiguring NoSQL databases under changing workload patterns is crucial for maximizing database throughput. This is challenging because of the large configuration parameter search space with complex interdependencies among the parameters. While state-of-the-art systems can automatically identify close-to-optimal configurations for static workloads, they suffer for dynamic workloads as they overlook three fundamental challenges:(1) Estimating performance degradation during the reconfiguration process (such as due to database restart).(2) Predicting how transient the new workload pattern will be.(3) Respecting the application’s availability requirements during reconfiguration.
Our solution, SOPHIA, addresses all these shortcomings using an optimization technique that combines workload prediction with a cost-benefit analyzer.

SOPHIA computes the relative cost and benefit of each reconfiguration step, and determines an optimal reconfiguration for a future time window. This plan specifies when to change configurations and to what, to achieve the best performance without degrading data availability. We demonstrate its effectiveness for three different workloads: a multi-tenant, global-scale metagenomics repository (MG-RAST), a bus-tracking application (Tiramisu), and an HPC data-analytics system, all with varying levels of workload complexity and demonstrating dynamic workload changes.
SOPHIA outperforms in throughput and tail-latency various baselines for two popular NoSQL databases, Cassandra and Redis.

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KeyByte: Unlock the Potential of your Cloud Databases

OptimusCloud: For your cloud-hosted databases

Sophia: For your on-premise databases