Abstract Data sharing is central to various applications such as fraud detection, ad matching, and improving patient care. However, each solution to data sharing is bespoke and cost-intensive, hampering value generation. We identify the lack of abstractions to control data release as the culprit of the problem. For example, it is common to have constraints on whether to share data that depend on the result of sharing, and evaluating these constraints requires sharing in the first place, leading to a standstill. To help people build solutions to a wide variety of data sharing applications, we propose programmable dataflows , which consist of two components. The first component is an abstraction, the contract , which agents use to communicate the intent of a data sharing action and evaluate its consequences before the dataflow takes place. This helps agents control the release of their data. The second component is a contract programming model (CPM), which allows agents to program data sharing applications catered to each problem’s needs with the contract abstraction. We describe how to deploy those applications on a data escrow to ensure data remains protected from unintended data releases. Our evaluation shows 1) the contract abstraction permits representing a wide range of sharing problems, 2) CPM permits writing programs for complex data sharing problems and 3) quantitatively, our improvements to CPM make sharing programs run efficiently.

As users migrate their analytical workloads to cloud databases, it is becoming just as important to reduce monetary costs as it is to optimize query runtime. In the cloud, a query is billed based on either its compute time or the amount of data it processes. We observe that analytical queries are either compute- or IO-bound and each query type executes cheaper in a different pricing model. We exploit this opportunity and propose methods to build cheaper execution plans across pricing models that complete within user-defined runtime constraints. We implement these methods and produce execution plans spanning multiple pricing models that reduce the monetary cost for workloads by as much as 56%. We reduce individual query costs by as much as 90%. The prices chosen by cloud vendors for cloud services also impact savings opportunities. To study this effect, we simulate our proposed methods with different cloud prices and observe that multi-cloud savings are robust to changes in cloud vendor prices. These results indicate the massive opportunity to save money by executing workloads across multiple pricing models.

Large scale distributed computing setups rely on power management systems to enforce tight power budgets. Existing systems use a central authority that redistributes excess power to power-hungry nodes. This central authority, however, is both a single point of failure and a critical bottleneck—especially at large scale. To address these limitations we propose Penelope, a distributed power management system which shifts power through peer-to-peer transactions, ensuring that it remains robust in faulty environments and at large scale. We implement Penelope and compare its achieved performance to SLURM, a centralized power manager, under a variety of power budgets. We find that under normal conditions SLURM and Penelope achieve almost equivalent performance; however in faulty environments, Penelope achieves 8–15% mean application performance gains over SLURM. At large scale and with increasing frequency of messages, Penelope maintains its performance in contrast to centralized approaches which degrade and become unusable.