Search for: All records

Low-latency inference for machine learning models is increasingly becoming a necessary requirement, as these models are used in mission-critical applications such as autonomous driving, military defense (e.g., target recognition), and network traffic analysis. A widely studied and used technique to overcome this challenge is to offload some or all parts of the inference tasks onto specialized hardware such as graphic processing units. More recently, offloading machine learning inference onto programmable network devices, such as programmable network interface cards or a programmable switch, is gaining interest from both industry and academia, especially due to the latency reduction and computational benefits of performing inference directly on the data plane where the network packets are processed. Yet, current approaches are relatively limited in scope, and there is a need to develop more general approaches for mapping offloading machine learning models onto programmable network devices. To fulfill such a need, this work introduces a novel framework, called ML-NIC, for deploying trained machine learning models onto programmable network devices' data planes. ML-NIC deploys models directly into the computational cores of the devices to efficiently leverage the inherent parallelism capabilities of network devices, thus providing huge latency and throughput gains. Our experiments show that ML-NIC reduced inference latency by at least 6 × on average and in the 99th percentile and increased throughput by at least 16xwith little to no degradation in model effectiveness compared to the existing CPU solutions. In addition, ML-NIC can provide tighter guaranteed latency bounds in the presence of other network traffic with shorter tail latencies. Furthermore, ML-NIC reduces CPU and host server RAM utilization by 6.65% and 320.80 MB. Finally, ML-NIC can handle machine learning models that are 2.25 × larger than the current state-of-the-art network device offloading approaches. 

A well-established technique for capturing database provenance as annotations on data is to instrument queries to propagate such annotations. However, even sophisticated query optimizers often fail to produce efficient execution plans for instrumented queries. We develop provenance-aware optimization techniques to address this problem. Specifically, we study algebraic equivalences targeted at instrumented queries and alternative ways of instrumenting queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. Our experiments confirm that these optimizations are highly effective, improving performance by several orders of magnitude for diverse provenance tasks.