When network links were slow, cloud and distributed database systems could rely on generic kernel abstractions and treat network communication as a black box. With today's fast cloud networks, this approach breaks down: database performance becomes limited by the CPU overhead of the kernel TCP stack. Replacing TCP with user-space UDP can reduce this overhead, but it requires reimplementing essential guarantees, such as reliability and ordering. To solve this conundrum, database systems should no longer treat networking as a black box but co-design it with database operations. We propose the bi-channel paradigm for database systems, which separates communication into two channels: A high-performance data path for latency- and bandwidth-sensitive operations, and a reliable control path for coordination and recovery. We implement the paradigm by combining user-space UDP and kernel-based TCP, though other stack combinations are possible. This design exploits modern NIC capabilities while preserving TCP's reliability. We demonstrate the paradigm's efficiency and simplicity in two representative settings: a distributed shuffle saturating 200 Gbit/s with three CPU cores, and a replicated key-value store processing millions of messages per second.

Recent Trusted Execution Environments based on Intel SGXv2 enable fast and confidential in-memory processing for DBMSs. However, secure persistence remains a major performance and security challenge. While native SGX-based storage mechanisms provide confidentiality and integrity out of the box, they incur high overheads for DBMSs. In this paper, we analyze the overheads and introduce novel techniques for secure and fast DBMS storage. As we show, these techniques reduce the high storage overheads of native SGX storage mechanisms to negligible overheads, enabling practical, high-performance, secure storage for cloud databases.

Modern hardware and economic trends are driving the adoption of distributed storage engines that expose a transparent, shared-cache abstraction: any node can access both the cluster’s aggregate DRAM and its NVMe storage over a fast (RDMA) network. To sustain performance under changing workloads, these systems must continuously evict and re-cache pages at very high rates. However, most designs still rely on node-local eviction algorithms such as LRU, which waste aggregate DRAM by retaining redundant page copies. We propose ScaleEvict, an altruistic eviction strategy implemented in the state-of-the-art ScaleStore engine. ScaleEvict efficiently coordinates eviction decisions across nodes to reduce redundant replication and prioritize globally valuable pages. ScaleEvict matches ScaleStore’s throughput using only two-thirds of the DRAM. Alternatively, at equal DRAM capacity, ScaleEvict improves throughput by up to 2 × while reducing 99th percentile latency by up to 3 ×.

Kernel-bypass technologies eliminate the overhead of traditional OS stacks, offering direct access to high-speed I/O devices such as network and storage.This paper argues that kernel-bypass is no longer an optional optimization but a necessary architectural strategy for I/O-heavy applications like database systems.The motivation stems from two trends: stagnating CPU performance and rapid advances in I/O hardware, such as 800 Gbit/s NICs and SSDs exceeding 12M IOPS.In our evaluation, we show that, given these trends, it is no longer possible for DBMSs to saturate modern NICs or SSDs with traditional kernel stacks.We thus urge the research community to prioritize kernel-bypass technologies to fully harness the potential of emerging hardware in database systems.

Cloud databases have become prevalent, as evidenced by the rapid growth of systems such as BigQuery, Snowflake, and Databricks.Concurrently, there has been a significant increase in the requirements for secure data processing when outsourcing databases to the cloud.For this, Trusted Execution Environments (TEEs) have emerged as a key technology in the cloud, which is witnessed by the fact that all cloud providers offer TEEs in their service portfolios.However, Amazon Web Services' (AWS) approach to TEEs based on Nitro Enclaves fundamentally differs from that of other cloud providers like Microsoft and Google or standard technologies such as Intel SGX.In this paper, we thus set out the goal to understand the implications of using AWS Nitro Enclaves for cloud databases.Although Nitro Enclaves initially appear to be a promising platform for pure TEE performance, they come with significant limitations regarding communication with the Nitro Enclave.Our benchmark results provide insight into the performance and practical challenges of deploying database workloads in AWS Nitro Enclaves, offering valuable guidance for practitioners and researchers.

In recent years, trusted execution environments (TEEs) such as Intel Software Guard Extensions (SGX) have gained a lot of attention in the database community. This is because TEEs provide an interesting platform for building trusted databases in the cloud. However, until recently SGX was only available on low-end single socket servers built on the Intel Xeon E3 processor generation and came with many restrictions for building DBMSs. With the availability of the new Ice Lake processors, Intel provides a new implementation of the SGX technology that supports high-end multi-socket servers. With this new implementation, which we refer to as SGXv2 in this paper, Intel promises to address several limitations of SGX enclaves. This raises the question whether previous efforts to overcome the limitations of SGX for DBMSs are still applicable and if the new generation of SGX can truly deliver on the promise to secure data without compromising on performance. To answer this question, in this paper we conduct a first systematic performance study of Intel SGXv2 and compare it to the previous generation of SGX.

Federated learning aims to protect the privacy of data owners in a collaborative machine learning setup since training data does not need to be revealed to any other participant involved in the training process. This is achieved by only requiring participants to share locally computed model updates (i.e., gradients), instead of the training data, with a centralized parameter server. However, recent papers have shown that privacy attacks exist which allow this server to reconstruct the training data of individual data owners only from the received gradients. To mitigate this attack, in this paper, we propose a new federated learning framework that decentralizes the parameter server. As part of this contribution, we investigate the configuration space of such a decentralized federated learning framework. Moreover, we propose three promising privacy-preserving techniques, namely model sharding, asynchronous updates and polling intervals for stale parameters. In our evaluation, we observe on different data sets that these techniques can effectively thwart the gradient-based reconstruction attacks on deep learning models, both from the client side and the server side, by reducing the attack results close to random noise.

In this demo we present BlockchainDB, which leverages blockchains as storage layer and introduces a database layer on top that extends blockchains by classical data management techniques (e.g., sharding). Further, BlockchainDB provides a standardized key/value-based query interface to facilitate the adoption of blockchains for data sharing use cases. With BlockchainDB we can thus not only improve the performance and scalability of blockchains for data sharing but also decrease the complexity for organizations intending to use blockchains for this use case.

In this paper we present BlockchainDB , which leverages blockchains as a storage layer and introduces a database layer on top that extends blockchains by classical data management techniques (e.g., sharding) as well as a standardized query interface to facilitate the adoption of blockchains for data sharing use cases. We show that by introducing the additional database layer, we are able to improve the performance and scalability when using blockchains for data sharing and also massively decrease the complexity for organizations intending to use blockchains for data sharing.