Accelerating Big Data Processing with Hadoop, Spark and Memcached on Datacenters with Modern Architectures
When: June 13, 2015 (1:30pm-5:30pm)
Where: Portland, Oregon, USA
Abstract
Apache Hadoop and Spark are gaining prominence in handling Big Data and
analytics. Similarly, Memcached in Web 2.0 environment is becoming important
for large-scale query processing. These middleware are traditionally written
with sockets and do not deliver best performance on datacenters with modern
high performance networks. In this tutorial, we will provide an in-depth
overview of the architecture of Hadoop components (HDFS, MapReduce, RPC, HBase,
etc.), Spark and Memcached. We will examine the challenges in re-designing the
networking and I/O components of these middleware with modern interconnects,
protocols (such as InfiniBand, iWARP, RoCE, and RSocket) with RDMA and storage
architecture. Using the publicly available software packages in the
High-Performance Big Data (HiBD, http://hibd.cse.ohio-state.edu) project,
we will provide case studies of the new designs for several
Hadoop/Spark/Memcached components and their associated benefits. Through these
case studies, we will also examine the interplay between high performance
interconnects, storage systems (HDD and SSD), and multi-core platforms to
achieve the best solutions for these components.
Targeted Audience and Scope
The tutorial content is planned for half-a-day. This tutorial
is targeted for various categories of people working in the areas of Big Data
including high-performance Hadoop/Spark/Memcached, high performance communication and I/O
architecture, storage, networking, middleware, cloud computing and applications.
Specific audience this tutorial is aimed at include: - Scientists,
engineers, researchers, and students engaged in designing next-generation Big
Data systems and applications
- Designers and developers of Big Data,
Hadoop, Spark and Memcached middleware
- Newcomers to the field of Big Data who
are interested in familiarizing themselves with Hadoop, Spark, Memcached, RDMA, and
high-performance networking
- Managers and administrators responsible
for setting-up next generation Big Data environment and high-end systems/facilities
in their organizations/laboratories
The content level will be as follows: 30% beginner, 40%
intermediate, and 30% advanced. There is no fixed pre-requisite. As long as the
attendee has a general knowledge in Big Data, Hadoop, Spark, Memcached, high performance
computing, networking and storage architecture, and related issues, he/she will
be able to understand and appreciate it. The tutorial is designed in such a way
that an attendee gets exposed to the topics in a smooth and progressive manner.
This tutorial is organized as a coherent talk to cover multiple topics.
Outline of the Tutorial
- Introduction to Big Data Applications and Analytics
- Overview of Hadoop MapReduce Programming Model
- Architecture Overview of Apache Hadoop, Spark and Memcached
- MapReduce and YARN
- HDFS
- Spark
- RPC
- HBase
- Memcached
- Overview of Modern Interconnects, Protocols and Storage Architectures for Data Center Systems
- InfiniBand and RDMA
- 10/40 GigE, iWARP and RoCE technologies
- RSocket and SDP protocols
- SSD-based storage
- Challenges in Accelerating Hadoop, Spark and Memcached on Modern Datacenters
- Overview of Benchmarks and Applications using Hadoop, Spark and Memcached
- Acceleration Case Studies and In-Depth Performance Evaluation
- MapReduce over InfiniBand with RDMA and SSD
- HDFS over InfiniBand with RDMA and SSD
- Spark over InfiniBand with RDMA and SSD
- RPC over InfiniBand with RDMA
- HBase over InfiniBand with RDMA and SSD
- Memcached over InfiniBand with RDMA and SSD
- RDMA for Apache Hadoop/Memcached Distributions with Optimizations and Tuning
- Discussion on Open Research Challenges for Accelerating Big Data Analytics
- Ongoing and Future Activities for High-Performance Big Data
- Conclusion and Q&A
Brief Biography of Speakers
Dr. Dhabaleswar
K. (DK) Panda is a Professor and Distinguished Scholar
of Computer Science at the Ohio State
University. He obtained his Ph.D. in computer engineering from the University
of Southern California. His research interests include parallel computer
architecture, high performance computing, communication protocols, files
systems, network-based computing, and Quality of Service. He has published over
350 papers in major journals and international conferences related to these
research areas. Dr. Panda and his research group members have been doing
extensive research on modern networking technologies including InfiniBand, HSE
and RDMA over Converged Enhanced Ethernet (RoCE). His research group is
currently collaborating with National Laboratories and leading InfiniBand and
10GigE/iWARP companies on designing various subsystems of next generation
high-end systems. The MVAPICH2
(High Performance MPI over InfiniBand, iWARP and RoCE) open-source software
package, developed by his research group, are currently being used by more than
2,400 organizations worldwide (in 75 countries). This software has enabled
several InfiniBand clusters (including the 7th one) to get into the latest
TOP500 ranking. More than 266,000 downloads of this
software have taken place from the project's website alone.
These software packages are also available with the Open
Fabrics stack for network vendors (InfiniBand and iWARP), server vendors and
Linux distributors. The new RDMA-enabled Apache Hadoop and Memcached packages, consisting of
acceleration for HDFS, MapReduce, RPC and Memcached, are publicly available from
http://hibd.cse.ohio-state.edu. These packages are
being used my more than 110 organizations worldwide (in 18 countries).
Dr. Panda's research is supported
by funding from US National Science Foundation, US Department of Energy, and
several industry including Intel, Cisco, SUN, Mellanox, QLogic, NVIDIA and
NetApp. He is an IEEE Fellow and a member of ACM. More details about Dr.
Panda, including a comprehensive CV and publications are available here.
Dr. Xiaoyi Lu
is a Senior Research Associate in the Department of Computer Science and
Engineering at the Ohio State University, USA. He obtained his Ph.D. degree in
Computer Science from Institute of Computing Technology, Chinese Academy of
Sciences, Beijing, China. His current research interests include
high-performance interconnects and protocols, Big Data, Hadoop/Spark Ecosystem,
Parallel Computing Models (MPI/PGAS), GPU/MIC, Virtualization and Cloud
Computing. He has published over 40 papers in major journals and international
conferences related to these research areas. He has been actively involved in
various professional activities in academic journals and conferences.
Recently, Dr. Lu is doing research and working on design and development for
the High-Performance Big Data project
(http://hibd.cse.ohio-state.edu). He is a member of IEEE. More
details about Dr. Lu are available here.
Last Updated: June 13, 2015