D-tunes: self tuning datastores for geo-distributed interactive applications

Modern internet applications have resulted in users sharing data with each other in an interactive fashion. These applications have very stringent service level agreements (SLAs) which place tight constraints on the performance of the underlying geo-distributed datastores. Deploying these systems in the cloud to meet such constraints is a challenging task, as application architects have to strike an optimal balance among different contrasting objectives such as maintaining consistency between multiple replicas, minimizing access latency and ensuring high availability. Achieving these objectives requires carefully configuring a number of low-level parameters of the datastores, such as the number of replicas, which DCs contain which data, and the underlying consistency protocol parameters. In this work, we adopt a systematic approach where we develop analytical models that capture the performance of a datastore based on application workload and build a system that can automatically configure the datastore for optimal performance.