Fulva: Efficient Live Migration for In-Memory Key-Value Stores with Zero Downtime

A key-value store live migration approach migrates key-value tuples and their client requests from an overloaded machine (source) to an idle machine (destination), while still serving client requests. Existing migration approaches fall into two categories. First, a source-driven approach (e.g., DrTM-B) executes all client requests on the source and incrementally propagates the updated key-value tuples to the destination. This approach has an inevitable downtime to completely propagate the updated tuples at the end of a migration. Second, a destination-driven approach (e.g., RockSteady) executes all read and write requests on the destination, and pulls tuples from source for read requests on-demand. This approach has zero downtime, but incurs extra network round-trips due to the on-demand pull, greatly increasing request latency. Overall, a live migration approach that has zero downtime and no performance degradation during the migration is highly desirable but missing. The key observation of our Fulva system is that the source and destination can cooperatively drive the migration and serve requests, and we need only to design an efficient protocol to ensure linearizability (i.e., reads see the updates from the latest writes). To this end, when a migration starts, Fulva works by three steps. First, all write requests are redirected to the destination. Second, each client program uses a Fulva's RPC library to track the migration progress. For read requests accessing the already-migrated tuples, Fulva RPC library sends the requests to the destination. Third, for read requests accessing not-yet-migrated tuples, Fulva sends to both machines. The first step avoids downtime because all updated tuples are already on the destination. The second and third steps avoid the on-demand pull and ensure linearizability, making Fulva efficient. We implemented Fulva using DPDK and integrated it with RAMCloud, a popular in-memory key-value store. We compared Fulva with two notable systems, RockSteady (destination-driven approach) and RAMCloud's default source-driven approach. Extensive evaluation shows that Fulva had much higher throughput and lower latency than the two systems, and Fulva's network bandwidth usage is comparable with RockSteady. All Fulva's source code and raw evaluation results are released on github.com/hku-systems/fulva.