HardBD & Active'21

HardBD & Active'21

Joint International Workshop on Big Data Management on Emerging Hardware
and Data Management on Virtualized Active Systems

To be Sponsored by and Held in Conjunction with ICDE 2021

April 19, 2021 in Chania, Crete, Greece

bullet Description
bullet Topics
bullet Submission
bullet Important Dates
bullet Program
bullet Keynote
bullet Organizers
bullet PC Members
bullet Previous Workshops


HardBD : Data properties and hardware characteristics are two key aspects for efficient data management. A clear trend in the first aspect, data properties, is the increasing demand to manage and process Big Data in both enterprise and consumer applications, characterized by the fast evolution of Big Data Systems, such as Key-Value stores, Document stores, Graph stores, Spark, MapReduce/Hadoop, Graph Computation Systems, Tree-Structured Databases, as well as novel extensions to relational database systems. At the same time, the second aspect, hardware characteristics, is undergoing rapid changes, imposing new challenges for the efficient utilization of hardware resources. Recent trends include massive multi-core processing systems, high performance co-processors, very large main memory systems, persistent main memory, fast networking components, big computing clusters, and large data centers that consume massive amounts of energy. Utilizing new hardware technologies for efficient Big Data management is of urgent importance.

Active : Existing approaches to solve data-intensive problems often require data to be moved near the computing resources for processing. These data movement costs can be prohibitive for large data sets. One promising solution is to bring virtualized computing resources closer to data, whether it is at rest or in motion. The premise of active systems is a new holistic view of the system in which every data medium and every communication channel become compute-enabled. The Active workshop aims to study different aspects of the active systems' stack, understand the impact of active technologies (including but not limited to hardware accelerators such as SSDs, GPUs, FPGAs, and ASICs) on different applications workloads over the lifecycle of data, and revisit the interplay between algorithmic modeling, compiler and programming languages, virtualized runtime systems and environments, and hardware implementations, for effective exploitation of active technologies.

HardBD & Active'21 : Both HardBD and Active are interested in exploiting hardware technologies for data-intensive systems. The aim of this one-day joint workshop is to bring together researchers, practitioners, system administrators, and others interested in this area to share their perspectives on exploiting new hardware technologies for data-intensive workloads and big data systems, and to discuss and identify future directions and challenges in this area. The workshop aims at providing a forum for academia and industry to exchange ideas through research and position papers.

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 Topics of interest include but are not limited to:

  • Systems Architecture on New Hardware
  • Data Management Issues in Software-Hardware-System Co-design
  • Main Memory Data Management (e.g. CPU Cache Behavior, SIMD, Lock-Free Designs, Transactional Memory)
  • Data Management on New Memory Technologies (e.g., SSDs, NVMs)
  • Active Technologies (e.g., GPUs, FPGAs, and ASICs) in Co-design Architectures
  • Distributed Data Management Utilizing New Network Technologies (e.g., RDMA)
  • Novel Applications of New Hardware Technologies in Query Processing, Transaction Processing, or Big Data Systems (e.g., Hadoop, Spark, NoSQL, NewSQL, Document Stores, Graph Platforms etc.)
  • Novel Applications of Low-Power Modern Processors in Data-Intensive Workloads
  • Virtualizing Active Technologies on Cloud (e.g., Scalability and Security)
  • Benchmarking, Performance Models, and/or Tuning of Data Management Workloads on New Hardware Technologies

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  Submission Guidelines

We welcome submissions of original, unpublished research papers that are not being considered for publication in any other forum. Papers should be prepared in the IEEE format and submitted as a single PDF file. The paper length should not exceed 6 pages. The submission site is https://cmt3.research.microsoft.com/HardBDActive2021.

Authors of a selection of accepted papers will be invited to submit an extended version to the Distributed and Parallel Databases (DAPD) journal.

Camera Ready Instructions: ICDE'21 is using IEEE Conference Publishing Services (CPS) to collect camera-ready papers and copyright forms. Please submit your camera-ready papers and copyright forms at the following link: https://ieeecps.org/#!/auth/login?ak=1&pid=7olafLcrkRpJt57rn5a8lW. Papers should be formatted in the IEEE format. The paper length should not exceed 6 pages.

Please also upload your camera-ready papers on the CMT site so that we can provide paper links in the workshop program.

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  Important Dates

Paper submission: January 18, 2021 (Monday) 11:59:00 PM PT
January 25, 2021 (Monday) 11:59:00 PM PT
Notification of acceptance: February 8, 2021 (Monday)
February 15, 2021 (Monday)
Camera-ready copies: March 1, 2021 (Monday)
Workshop: April 19, 2021 (Monday)

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7:00-8:00am PDT Research Session I

8:00-8:10am PDT Break

8:10-9:40am PDT SMDB and HardBD&Active Joint Keynote Session I

9:40-9:50am PDT Break

9:50-11:20am PDT SMDB and HardBD&Active Joint Keynote Session II

11:20-11:30am PDT Break

11:30-12:30pm PDT Research Session II

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  Joint Keynote Talks with SMDB 2021

Sam Lightstone      AI's Enormous Potential for Database Simplification

Sam Lightstone
CTO AI Strategy, IBM Data and AI

Abstract: Research into self-managing databases exploded in the early 2000's with sizeable corporate efforts from each of IBM, Microsoft and Oracle. In 2005 the SMDB Workgroup was founded by Sam Lightstone and Guy Lohman to bring together like minded innovators from industry and academia. Now, as we enter the era of intelligent computing, AI offers itself as a catalyst for quantum gains in database simplification. In this session Sam Lightstone will contrast the state of SMDB technology of 2005 with today's emerging new potential for automation, semantic simplification and handling new workloads.

Bio: Sam Lightstone is IBM Chief Technology Officer for AI, IBM Fellow and a Master Inventor in the IBM Data and AI group. He is also chair of the Data and AI Technical Team, the working group of IBM's technical executives in the division. He has been the founder and co-founder of several large-scale initiatives including AI databases, next generation data warehousing, data virtualization, autonomic computing for data systems, serverless cloud SQL query, and cloud native database services. He co-founded the IEEE Data Engineering Workgroup on Self-Managing Database Systems. Sam has more than 65 patents issued and pending and has authored 4 books and over 30 papers. Sam's books have been translated into Chinese, Japanese and Spanish. In his spare time he is an avid guitar player and fencer. His Twitter handle is "samlightstone".

Andy Pavlo      OtterTune: An Automatic Database Configuration Tuning Service

Andy Pavlo
Associate Professor, Carnegie Mellon University & Co-founder, OtterTune

Abstract: Database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior. Setting these knobs correctly for an application's workload can improve the performance and efficiency of the DBMS. But such tuning requires considerable efforts from experienced administrators, which is not scalable for large DBMS fleets. This problem has led to research on using machine learning (ML) to devise strategies to optimize DBMS knobs for any application automatically. The OtterTune database tuning service from Carnegie Mellon uses ML to generate and install optimized DBMS configurations. OtterTune observes the DBMS's workload through its metrics and then trains recommendation models that select better knob values. It then reuses these models to tune other DBMSs more quickly. In this talk, I will present an overview of OtterTune and discuss the challenges one must overcome to deploy an ML-based service for DBMSs. I will also highlight the insights we learned from real-world installations of OtterTune.

Bio: Andy Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. His research interest is in database management systems, specifically main memory systems, self-driving / autonomous architectures, transaction processing systems, and large-scale data analytics. At CMU, he is a member of the Database Group and the Parallel Data Laboratory. He is also the co-founder and CEO of OtterTune.

Shasank Chavan      Automatic Data Management and Storage Tiering
with Oracle Database In-Memory

Shasank Chavan
Vice President of the Data and In-Memory Technologies group, Oracle

Abstract: Autonomous / Self-Driving Databases utilize machine learning techniques to eliminate the manual labor associated with database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. This talk will focus specifically on how we implement a self-driving database with Oracle's Database In-Memory product to automatically tune for query optimization, memory management, storage management and data tiering. We will first present Oracle's Database In-Memory architecture and various features built for optimizing analytics and mixed workload performance, and then describe in some detail the smarts we have to make it auto-performing in our self-driving database.

Bio: Shasank Chavan is the Vice President of the Data and In-Memory Technologies group at Oracle. He leads an amazing team of brilliant engineers in the Database organization who develop customer-facing, performance-critical features for an In-Memory Columnar Store which, as Larry Ellison proclaimed, "processes data at ungodly speeds". His team implements novel SIMD kernels and hardware acceleration technology for blazing fast columnar data processing, optimized data formats and compression technology for efficient in-memory storage, algorithms and techniques for fast in-memory join and aggregation processing, and optimized in-memory data access and storage solutions in general. His team is currently hyper-focused on leveraging emerging hardware technologies to build Oracle's next-generation, highly distributed, data storage engine that powers the cloud. Shasank earned his BS/MS in Computer Science at the University of California, San Diego. He has accumulated 20+ patents over a span of 22 years working on systems software technology.

Ippokratis Pandis      Architectural Evolution of Amazon Redshift and
Its Practical Usage of Machine Learning

Ippokratis Pandis
Amazon AWS

Abstract: Amazon Redshift is Amazon's Petabyte-scale managed cloud data warehouse. Every day customers use Amazon Redshift to process multiple Exabytes of data. In the first part of this talk, we are going to look a bit under the hood of Amazon Redshift and discuss how the team makes sure that Amazon Redshift maintains its price/performance leadership among Cloud Data Warehouses. Further, we will talk about its architectural evolution, discussing features such as Managed Storage, Elastic Resize, Concurrency Scaling, DataSharing, Spectrum and AQUA. In the second part, we are going to discuss how Amazon Redshift leverages Machine Learning to improve its global operation, to reduce the need for administrative operations by its customers, and to improve its performance.

Bio: Ippokratis Pandis is a senior principal engineer at Amazon Web Services, working in Amazon Redshift. Redshift is Amazon's fully managed, petabyte-scale data warehouse service. Among others, Ippokratis is the architect of the Spectrum, Concurrency Scaling and DataSharing features of Redshift. Previously, Ippokratis has held positions as software engineer at Cloudera where he worked on the Impala SQL-on-Hadoop query engine, and as member of the research staff at the IBM Almaden Research Center, where he worked on the DB2 BLU product. Ippokratis received his PhD from the Electrical and Computer Engineering department at Carnegie Mellon University. He is the recipient of Best Demonstration awards at ICDE 2006 and SIGMOD 2011, and Test-of-Time award at EDBT 2019. He has served as PC chair of DaMoN 2014, DaMoN 2015, CloudDM 2016 and HPTS 2019.

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  PC Members

  • Manos Athanassoulis, Boston University
  • Bingsheng He, National University of Singapore
  • Peiquan Jin, Univerisity of Science and Technology of China
  • Wolfgang Lehner, TU Dresden
  • Yinan Li, Microsoft Research
  • Qiong Luo, Hong Kong University of Science and Technology
  • Ilia Petrov, Reutlingen University
  • Eva Sitaridi, Amazon
  • Tianzheng Wang, Simon Fraser University
  • Xiaodong Zhang, Ohio State University

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