Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 360 publications
Federated Variational Inference: Towards Improved Personalization and Generalization
Elahe Vedadi
Josh Dillon
Philip Mansfield
Karan Singhal
Arash Afkanpour
Warren Morningstar
AAAI Federated Learning on the Edge Symposium (2024)
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Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately approximate the predictive process, converge to an optimal state, or generalize to new clients. We study personalization and generalization in stateless cross-device federated learning setups assuming heterogeneity in client data distributions and predictive models. We first propose a hierarchical generative model and formalize it using Bayesian Inference. We then approximate this process using Variational Inference to train our model efficiently. We call this algorithm Federated Variational Inference (FedVI). We use PAC-Bayes analysis to provide generalization bounds for FedVI. We evaluate our model on FEMNIST and CIFAR-100 image classification and show that FedVI beats the state-of-the-art on both tasks.
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How we use GenAI in SRE
CommitConf, Madrid (2024)
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Google services are powered by the largest network of computers in the world. Site Reliabity Engineers (SRE) make sure that the whole stack is cool: datacenters are safe, well provisionedl; we have fallback mechanims, and data integrity; to making sure we design our stack properly, using the right storage, replication and software trade-offs.
Generative AI is a great tool to make us super-effective: having access to tools to generate our most toily configurations, to classify risks and events, to manage large swaths of machines with agents or to automate complex workflows cheaply.
This talk will cover the journey that SRE started years ago to become a truly AI-First discipline and the latest advancements in tooling, practices and workflows.
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Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples
Mangpo Phothilimthana
Soroush Ghodrati
Selene Moon
ASPLOS 2024, Association for Computing Machinery
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Representative modeling of I/O activity is crucial when designing large-scale distributed storage systems. Particularly important use cases are counterfactual “what-if” analyses that assess the impact of anticipated or hypothetical new storage policies or hardware prior to deployment. We propose Thesios, a methodology to accurately synthesize such hypothetical full-resolution I/O traces by carefully combining down-sampled I/O traces collected from multiple disks attached to multiple storage servers. Applying this approach to real-world traces that a real ready routinely sampled at Google, we show that our synthesized traces achieve 95–99.5% accuracy in read/write request numbers, 90–97% accuracy in utilization, and 80–99.8% accuracy in read latency compared to metrics collected from actual disks. We demonstrate how The-sios enables diverse counterfactual I/O trace synthesis and analyses of hypothetical policy, hardware, and server changes through four case studies: (1) studying the effects of changing disk’s utilization, fullness, and capacity, (2) evaluating new data placement policy, (3) analyzing the impact on power and performance of deploying disks with reduced rotations-per-minute (RPM), and (4) understanding the impact of increased buffer cache size on a storage server. Without Thesios, such counterfactual analyses would require costly and potentially risky A/B experiments in production.
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We present Prequal (\emph{Probing to Reduce Queuing and Latency}), a load balancer
for distributed multi-tenant systems. Prequal aims to minimize
real-time request latency in the presence of heterogeneous server
capacities and non-uniform, time-varying antagonist load. It actively probes
server load to leverage the \emph{power of $d$ choices}
paradigm, extending it with asynchronous and reusable probes. Cutting
against received wisdom, Prequal does not balance CPU load, but instead
selects servers according to estimated latency and active requests-in-flight
(RIF). We explore its major design features on a testbed system
and evaluate it on YouTube, where it has been deployed for more than two years. Prequal has dramatically decreased tail latency, error rates, and resource use, enabling YouTube and
other production systems at Google to run at much higher utilization.
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BigLake: BigQuery’s Evolution toward a Multi-Cloud Lakehouse
Justin Levandoski
Garrett Casto
Mingge Deng
Rushabh Desai
Thibaud Hottelier
Amir Hormati
Jeff Johnson
Dawid Kurzyniec
Prem Ramanathan
Gaurav Saxena
Vidya Shanmugam
Yuri Volobuev
SIGMOD (2024)
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BigQuery’s cloud-native disaggregated architecture has allowed Google Cloud to evolve the system to meet several customer needs across the analytics and AI/ML workload spectrum. A key customer requirement for BigQuery centers around the unification of data lake and enterprise data warehousing workloads. This approach combines: (1) the need for core data management primitives, e.g., security, governance, common runtime metadata, performance acceleration, ACID transactions, provided by an enterprise data warehouses coupled with (2) harnessing the flexibility of the open source format and analytics ecosystem along with new workload types such as AI/ML over unstructured data on object storage. In addition, there is a strong requirement to support BigQuery as a multi-cloud offering given cloud customers are opting for a multi-cloud footprint by default.
This paper describes BigLake, an evolution of BigQuery toward a multi-cloud lakehouse to address these customer requirements in novel ways. We describe three main innovations in this space. We first present BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg) first class citizens, providing fine-grained governance enforcement and performance acceleration over these formats to BigQuery and other open-source analytics engines. Next, we cover the design and implementation of BigLake Object tables that allow BigQuery to integrate AI/ML for inferencing and processing over unstructured data. Finally, we present Omni, a platform for deploying BigQuery on non-GCP clouds, focusing on the infrastructure and operational innovations we made to provide an enterprise lakehouse product regardless of the cloud provider hosting the data.
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Vortex is an exabyte scale structured storage system built for streaming and batch analytics. It supports high-throughput batch and stream ingestion. For the user, it supports both batch-oriented and stream-based processing on the ingested data.
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The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS.
In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment.
We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead.
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Distributed Tracing for InterPlanetary File System
Marshall David Miller
Rachel Han
Haorui Guo
2024 International Symposium on Parallel Computing and Distributed Systems (PCDS), IEEE, pp. 1-5
Preview abstract
The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS.
In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment.
We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead.
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Verifying credentials, such as educational degrees, professional licenses, and permits, is a crucial yet challenging task for organizations globally. Traditional verification methods often rely on third-party vendors, introducing vulnerabilities like bias, security breaches, and privacy risks. While blockchain technology offers a promising solution for credential management, existing approaches often store sensitive credential data off-chain in centralized databases or InterPlanetary File System (IPFS), leaving them susceptible to data breaches and loss.
This paper presents a novel, privacy-preserving credential verification system built on a permissioned blockchain network. This system, implemented using the Hyperledger Fabric framework, offers several key advantages over traditional methods, including enhanced security and improved privacy. By leveraging cryptographic techniques, the system ensures the robust and privacypreserving storage of credentials directly on the blockchain. This eliminates the reliance on vulnerable off-chain storage and mitigates associated risks. Furthermore, our analysis of a common credential dataset demonstrates the practical feasibility and cost-effectiveness of our solution, suggesting its widespread adoption. By addressing the limitations of both traditional and existing blockchain-based approaches, our system provides a robust, secure, and efficient solution for credential management in diverse sectors.
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We discuss distributed reset control of bittide systems. In a bittide system, multiple processors communicate over a network. The processors remain in logical synchrony by controlling the timing of frame transmissions. The protocol for doing this relies upon an underlying dynamic control system, where each node makes only local observations and performs no direct coordination with other nodes. In this paper we develop a control algorithm based on the idea of reset control, which allows all nodes to maintain small buffer offsets while also requiring very little state information at each node. This offers the potential for simplified boot processes and failure handling.
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Progressive Partitioning for Parallelized Query Execution in Google’s Napa
Junichi Tatemura
Yanlai Huang
Jim Chen
Yupu Zhang
Kevin Lai
Divyakant Agrawal
Brad Adelberg
Shilpa Kolhar
Indrajit Roy
49th International Conference on Very Large Data Bases, VLDB (2023), pp. 3475-3487
Preview abstract
Napa powers Google's critical data warehouse needs. It utilizes Log-Structured Merge Tree (LSM) for real-time data ingestion and achieves sub-second query latency for billions of queries per day. Napa handles a wide variety of query workloads: from full-table scans, to range scans, and multi-key lookups. Our design challenge is to handle this diverse query workload that runs concurrently. In particular, a large percentage of our query volume consists of external reporting queries characterized by multi-key lookups with strict sub-second query latency targets.
Query parallelization, which is achieved by processing a query in parallel by partitioning the input data (i.e., the SIMD model of computation), is an important technique to meet the low latency targets. Traditionally, the effectiveness of parallelization of a query is highly dependent on the alignment with the data partitioning established at write time. Unfortunately, such a write-time partitioning scheme cannot handle the highly variable parallelization requirements that are needed on a per-query basis.
The key to Napa’s success is its ability to adapt its query parallelization requirements on a per-query basis. This paper describes an index-based approach to perform data partitioning for queries that have sub-second latency requirements. Napa’s approach is progressive in that it can provide good partitioning within the time budgeted for partitioning. Since the end-to-end query time also includes the time to perform partitioning there is a tradeoff in terms of the time spent for partitioning and the resulting evenness of the partitioning. Our approach balances these opposing considerations to provide sub-second querying for billions of queries each day. We use production data to establish the effectiveness of Napa’s approach across easy to handle workloads to the most pathological conditions.
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In the new “gig” economy, a user plays the role of a consumer as well as a service provider. As a service provider, drivers travelling from a source to a destination may opportunistically pickup and drop-off packages along the way if that does not add significantly to their trip distance or time. This gives rise to a new business offering called Package Delivery as a Service (PDaaS) that brokers package pickups and deliveries at one end and connects them to drivers on the other end, thus creating an ecosystem of supply and demand. The dramatic cost savings of such a service model come from the fact that the driver is already en-route to their destination and the package delivery adds a small overhead to an already pre-planned trip. From a technical perspective, this problem introduces new technical challenges that are uncommon in the literature. The driver may want to optimise for distance or time. Furthermore, new packages arrive for delivery all the time and are assigned to various drivers continuously. This means that the algorithm has to work in an environment that is dynamic, thereby precluding most standard road network precomputation efforts. Furthermore, the number of packages that are available for delivery could be in the hundreds of thousands, which has to be quickly pruned down for the algorithm to scale. The paper proposes a variation called dual Dijkstra’s that combines a forward and a backward scan in order to find delivery options that satisfy the constraints specified by the driver. The new dual heuristic improves the standard single Dijkstra’s approach by narrowing down the search space, thus resulting in significant speed-ups over the standard algorithms. Furthermore, a combination of dual Dijkstra’s with a heuristic landmark approach results in a dramatic speed-up compared to the baseline algorithms. Experimental results show that the proposed approach can offer drivers a choice of packages to deliver under specified constraints of time or distance, and providing sub-second response time despite the complexity of the problem involved. As the number of packages in the system increases, the matchmaking process becomes easier resulting in faster response times. The scalability of the PDaaS infrastructure is demonstrated using extensive experimental results.
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We introduce logical synchrony, a framework that allows distributed computing to be coordinated as tightly as with pure synchrony without the distribution of a global clock or any reference to a universal time. We describe and prove the main properties of the framework and point to how processes can be executed on a logically synchronous system.
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CAPA: An Architecture For Operating Cluster Networks With High Availability
Bingzhe Liu
Mukarram Tariq
Omid Alipourfard
Rich Alimi
Deepak Arulkannan
Virginia Beauregard
Patrick Conner
Brighten Godfrey
Xander Lin
Mayur Patel
Joon Ong
Amr Sabaa
Alex Smirnov
Manish Verma
Prerepa Viswanadham
Google, Google, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (2023)
Preview abstract
Management operations are a major source of outages for networks. A number of best practices designed to reduce and mitigate such outages are well known, but their enforcement has been challenging, leaving the network vulnerable to inadvertent mistakes and gaps which repeatedly result in outages. We present our experiences with CAPA, Google’s “containment and prevention architecture” for regulating management operations on our cluster networking fleet. Our goal with CAPA is to limit the systems where strict adherence to best practices is required, so that availability of the network is not dependent on the good intentions of every engineer and operator. We enumerate the features of CAPA which we have found to be necessary to effectively enforce best practices within a thin “regulation“ layer. We evaluate CAPA based on case studies of outages prevented, counterfactual analysis of past incidents, and known limitations. Management-plane-related outages have substantially reduced both in frequency and severity, with a 82% reduction in cumulative duration of incidents normalized to fleet size over five years
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Code Generation for Data-Dependent Stencils
Mohammed Essadki
Bertrand Michel
Bruno Maugars
Oleksandr Zinenko
Nicolas Vasilache
CGO, IEEE (2023)
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Numerical simulation often resorts to iterative in-place stencils such as the Gauss-Seidel or Successive Overrelaxation (SOR) methods. Writing high performance implementations of such stencils requires significant effort and time; it also involves non-local transformations beyond the stencil kernel itself. While automated code generation is a mature technology for image processing stencils, convolutions and out-of place iterative stencils (such as the Jacobi method), the optimization of in-place stencils requires manual craftsmanship. Building on recent advances in tensor compiler construction, we propose the first domain-specific code generator for iterative in-place stencils. Starting from a generic tensor compiler implemented in the MLIR framework, tensor abstractions are incrementally refined and lowered down to parallel, tiled, fused and vectorized code. We used our generator to implement a realistic, implicit solver for structured meshes, and demonstrate results competitive with an industrial computational fluid dynamics framework. We also compare with stand-alone stencil kernels for dense tensors.
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