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Distributed key-value stores today require frequent key-value shard migration between nodes to react to dynamic workload changes for load balancing, data locality, and service elasticity. In this paper, we propose NetMigrate, a live migration approach for in-memory key-value stores based on programmable network data planes. NetMigrate migrates shards between nodes with zero service interruption and minimal performance impact. During migration, the switch data plane monitors the migration process in a fine-grained manner and directs client queries to the right server in real time, eliminating the overhead of pulling data between nodes. We implement a NetMigrate prototype on a testbed consisting of a programmable switch and several commodity servers running Redis and evaluate it under YCSB workloads. Our experiments demonstrate that NetMigrate improves the query throughput from 6.5% to 416% and maintains low access latency during migration, compared to the state-of-the-art migration approaches. 

Network operators need to run diverse measurement tasks on programmable switches to support management decisions (e.g., traffic engineering or anomaly detection). While prior work has shown the viability of running a single sketch instance, they largely ignore the problem of running an ensemble of sketch instances for a collection of measurement tasks. As such, existing efforts fall short of efficiently supporting a general ensemble of sketch instances. In this work, we present the design and implementation of Sketchovsky, a novel cross-sketch optimization and composition framework. We identify five new cross-sketch optimization building blocks to reduce critical switch hardware resources. We design efficient heuristics to select and apply these building blocks for arbitrary ensembles. To simplify developer effort, Sketchovsky automatically generates the composed code to be input to the hardware compiler. Our evaluation shows that Sketchovsky makes ensembles with up to 18 sketch instances become feasible and can reduce up to 45% of the critical hardware resources. 

Today’s large-scale services (e.g., video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a “sketch of sketches” to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.