In this paper, we propose Adam-Hash: an adaptive and dynamic multi-resolution hashing data-structure for fast pairwise summation estimation. Given a data-set X ⊂ ℝd, a binary function f : ℝd× ℝd→ ℝ, and a point y ∈ ℝd, the Pairwise Summation Estimate $PS{E_X}(y): = \frac{1}{{\left| X \right|}}\sum\nolimits_{x \in X} {f(x,y)} $. For any given data-set X, we need to design a data-structure such that given any query point y ∈ ℝd, the data-structure approximately estimates PSEX(y) in time that is sub-linear in |X|. Prior works on this problem have focused exclusively on the case where the data-set is static, and the queries are independent. In this paper, we design a hashing-based PSE data-structure which works for the more practical dynamic setting in which insertions, deletions, and replacements of points are allowed. Moreover, our proposed Adam-Hash is also robust to adaptive PSE queries, where an adversary can choose query qj∈ ℝddepending on the output from previous queries q1, q2, …, qj–1.

There has been a recent effort in applying differential privacy on memory access patterns to enhance data privacy. This is called differential obliviousness. Differential obliviousness is a promising direction because it provides a principled trade-off between performance and desired level of privacy. To date, it is still an open question whether differential obliviousness can speed up database processing with respect to full obliviousness. In this paper, we present the design and implementation of Adore: A set of D ifferentially O blivious RE lational database operators. Adore includes selection with projection, grouping with aggregation, and foreign key join. We prove that they satisfy the notion of differential obliviousness. Our differentially oblivious operators have reduced cache complexity, runtime complexity, and output size compared to their state-of-the-art fully oblivious counterparts. We also demonstrate that our implementation of these differentially oblivious operators can outperform their state-of-the-art fully oblivious counterparts by up to 7.4X.

With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that includes edge devices, local hubs, edge datacenters, and cloud datacenters. On the other hand, recent AutoML efforts have provided viable solutions for model compression, pruning and quantization for heterogeneous environments; for a machine learning model, now we may easily find or even generate a series of model variants with different tradeoffs between accuracy and efficiency. We design and implement JellyBean, a system for serving and optimizing machine learning inference workflows on heterogeneous infrastructures. Given service-level objectives (e.g., throughput, accuracy), JellyBean picks the most cost-efficient models that meet the accuracy target and decides how to deploy them across different tiers of infrastructures. Evaluations show that JellyBean reduces the total serving cost of visual question answering by up to 58% and vehicle tracking from the NVIDIA AI City Challenge by up to 36%, compared with state-of-the-art model selection and worker assignment solutions. JellyBean also outperforms prior ML serving systems (e.g., Spark on the cloud) up to 5x in serving costs.

Low latency is increasingly critical for modern workloads, to the extent that compute functions are explicitly scheduled to be co-located with their in-memory object stores for faster access. However, the traditional object store architecture mandates that clients interact with the server via inter-process communication (IPC). This poses a significant performance bottleneck for low-latency workloads. Meanwhile, in many important emerging AI workloads, such as parallel tree search and reinforcement learning, all the worker processes accessing the object store belong to a single user. We design Lightning, an in-memory object store rearchitected for modern, low-latency workloads in a single-user, multi-process setting. Lightning departs from the traditional design by adopting a shared memory model, enabling clients to directly access the object store without IPC boundary. Instead, client isolation is achieved by a novel integration of Intel Memory Protect Keys (MPK) hardware, transaction logging, and formal verification. Our evaluations show that Lightning outperforms state-of-the-art in-memory object stores by up to 9.0x on five standard NoSQL workloads and up to 4.5x in scaling up a Python tree search program. Lightning improves the throughput of a popular reinforcement learning framework that uses an in-memory object store for data sharing by up to 40%.