Amazon Aurora Serverless is an on-demand, autoscaling configuration for Amazon Aurora with full MySQL and PostgreSQL compatibility. It automatically offers capacity scale-up/down (i.e., vertical scaling) based on a customer database application's needs. For customers with time-varying workloads, it offers cost savings compared to provisioned Aurora or other alternatives due to its agile and granular scaling and its usage-based charging model. This paper describes the key ideas underlying Aurora Serverless's resource management. To help meet its goals, Aurora Serverless adapts and fine tunes well-established ideas related to resource over-subscription; reactive control informed by recent measurements; distributed & hierarchical decision-making; and innovations in the DB engine, OS, and hypervisor for efficiency. Perhaps the most challenging goal is to offer a consistent resource elasticity experience while operating hosts at high degrees of utilization. Aurora Serverless implements several novel ideas for striking a balance between these opposing needs. Its technique for mapping workloads to hosts ensures that, in the common case, there is adequate spare capacity within a host to support fast scale-up for a workload. In the rare event this is not so, it live migrates workloads to ensure seamless scale-up. Its load distribution strategy is characterized by "unbalancing" of load across hosts to enable agile live migrations. Finally, it employs a token bucket-based rate regulation mechanism to prevent a growing workload from saturating its host faster than live migration-based remedial actions.

Agricultural soil moisture measurement is usually done with extensive in situ sensor deployments. These sensor networks are often difficult to install and maintain. Ground penetrating radars have also been used to do fine-grained moisture measurements in farm fields, but usually require the radar to be in or near contact with the soil. In this paper we propose a hybrid approach that combines an in situ backscatter reflector with an ultra-wideband radar that is small enough to be handheld or mounted to a drone. The underground backscatter reflector allows for accurate time-of-flight measurements. The time-of-flight is determined by the permittivity of the soil, which is influenced primarily by the soil water content for non-saline soils. We performed both laboratory and in situ measurements, achieving an average accuracy within 0.013 cm3/ cm3of the ground truth with a 90th percentile of 0.034 cm3/cm3. This demonstrates the feasibility of our approach.

We present a sensing system that determines soil moisture via RF using backscatter tags paired with a commodity ultra-wideband RF transceiver. Despite decades of research confirming the benefits, soil moisture sensors are still not widely adopted on working farms for three key reasons: the high cost of sensors, the difficulty of deploying and maintaining these sensors, and the lack of reliable internet access in rural areas. We seek to address some of these obstacles by designing a low-cost soil moisture sensing system that uses a hybrid approach of pairing completely wireless backscatter tags with a mobile reader.(p)(/p)We designed and built a backscatter tag prototype and tested our system both in laboratory and in situ at an organic farm field. Our backscatter tag has a projected battery lifetime of up to 15 years on 4xAA batteries, and can operate at a depth of at least 30cm and up to 75cm. It achieves an average accuracy within 0.01-0.03cm3/cm3of the ground truth with a 90th percentile of 0.034cm3/cm3, which is comparable to state-of-the-art commercial soil sensors, at an order of magnitude lower cost.