Large language models (LLMs) are rapidly transforming the landscape of computing and daily life, demonstrating immense potential across diverse applications like natural language processing, machine translation, and code generation. This talk delves into ...

The high-profile success of Deep Learning (DL) at Big Tech companies, including recent Large Language Models (LLMs) such as the GPT and Llama families, has led to high demand among Web companies, consumer app companies, enterprises, healthcare, domain ...

Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning ...

The growth in sensor technologies, IoT devices, and information systems has opened up new opportunities for managing time series data across various domains. Despite significant progress, existing time series management systems face two crucial ...

This paper presents MLOS (ML Optimized Systems), a flexible framework that bridges the gap between benchmarking, experimentation, and optimization of software systems. It allows users to create one-click benchmarking and experimentation scenarios for ...

Byzantine Fault Tolerant (BFT) protocols serve as a fundamental yet intricate component of distributed data management systems in untrustworthy environments. BFT protocols exhibit different design principles and performance characteristics under varying ...

The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of ...

Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data. Mainstream GNNs employ the message passing scheme that iteratively propagates information between connected nodes through edges. ...

Consensus is a fundamental problem in distributed systems, involving the challenge of achieving agreement among distributed nodes. It plays a critical role in various distributed data management problems. This tutorial aims to provide a comprehensive ...

Native distributed databases, crucial for scalable applications, offer transactional and analytical prowess but face data intricacies and network challenges. Under the CAP theorem's constraints, latency and replication issues necessitate creative ...

Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model performance, but at the cost of increased GPU/CPU/memory usage. Meanwhile, ...

Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. ...

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. ...

In recent years at Tencent, we have observed that the use of multiple NoSQL databases for storing business data with diverse models has led to increased programming and deployment costs, as well as inefficient maintenance and underutilized resources. In ...

Alibaba relies on its robust database infrastructure to facilitate realtime data access and ensure business continuity despite regional disruptions. To address these operational imperatives, Alibaba developed the Data Transmission Service (DTS), which ...

Datastore was one of Google's first cloud databases, launched initially as part of App Engine, and built over Google's internal Megastore database system. Firestore was launched in 2019, both a re-implementation of Datastore over Google's Spanner ...

At internet scale companies like ByteDance, data is generated and consumed at enormously high speed by many different applications. Achieving low latency on such big data jobs is an important problem. However, the naive approach of aggregating all the ...

Forecasting extrapolates the values of a time series into the future, and is crucial to optimize core operations for many businesses and organizations. Building machine learning (ML)-based forecasting applications presents a challenge though, due to non-...

Distributed databases have become indispensable in contemporary computing and data processing, owing to their pivotal role in ensuring high availability and scalability. They effectively cater to the requirements of data management and high-concurrency ...

At ByteDance, where we execute over a million Spark jobs and handle 500PB of shuffled data daily, ensuring resource efficiency is paramount for cost savings. However, achieving optimization of resource efficiency in large-scale production environments ...