This document presents JVM-Bypass Shuffling (JBS) for Hadoop, which aims to improve the performance of Hadoop's data shuffling process. It discusses how Hadoop currently relies on Java-based networking that introduces overhead from the Java Virtual Machine. JBS seeks to avoid this overhead by removing the JVM from the critical path of data shuffling. It presents a portable library that can leverage both conventional TCP/IP and high-performance RDMA networking protocols. The goal of JBS is to reduce execution time and CPU utilization for Hadoop jobs. It was shown to reduce job execution time by up to 66.3% and lower CPU utilization by 48.1% compared to the standard Hadoop shuffling approach.

1. Presented By Under the Guidance of
PRATHVIRAJ Mrs. SHANTHI
1CR11CS401 Assistant professor
Dept of CSE Dept of CSE
CMRIT CMRIT
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2.  Abstract
 Introduction
 Existing system
 proposed system
 System Architecture
 Implementation
 Conclusion
 References
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3.  Hadoop employs Java-based network
transport stack on top of the Java Virtual
Machine (JVM) for its data shuffling and
merging purposes.
 JVM-Bypass Shuffling (JBS) for Hadoop
 JBS helps Hadoop data shuffling by avoiding
Java based transport protocols, removing the
overhead and limitations of the JVM.
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4.  MapReduce is a popular programming model
that provides a simple and scalable parallel
data processing framework.
 Hadoop as an open-source implementation
of Map Reduce adopted by Yahoo,Facebook
 data shuffling
 But data shuffling results in great volumes of
network traffic, reduces efficiency of data
analytics application
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5.  JVM introduces significant overhead in
managing Java objects results in shrinkage in
memory available to hadoop
 High speed networks, such as InfiniBand
provide Remote Direct Memory Access(RDMA)
that is capable of up to 56Gbps bandwidth,
and low CPU utilization.
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6.  Hadoop exposes two simple interfaces:
 map
 reduce
 Its runtime system consists of four major
components: JobTracker, TaskTracker,
MapTask, and ReduceTask.
 Dominant source of network traffic:5% of
large jobs can consume more than
98%network bandwidth
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8.  Execution time
 Cpu utilization
 Network traffic
 Overhead in managing java objects
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9.  No change to existing user interface (map
and reduce functions)
 Bypass the JVM from the critical path
of intermediate data shuffling
 Portable layer on top of any network
transport protocol
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11.  Asynchronous network operations
 Increases locality of disk access
 Reduces average delay of requests
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12.  Consolidates network fetching requests from
all Reduce Tasks on a single node
 Number of network connections, which is no
longer the total amount of MOF Copiers from
all Reduce Tasks
 Reduces the resource requirements
 creating and sustaining many network channels
 associated memory to buffer data
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13.  Connection Establishment for RDMA and
RoCE
 TCP/IP-Based Communication
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15.  Currently only Reliable Connection
(RC)service provided by RDMA-capable
interconnects is supported
 512 active connections at a time
 connections are torn down based on the LRU
when exceeded after threshold
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16.  Event-driven model and multiple threads
 On the client side,
◦ one dedicated thread to prepare connection requests
◦ data threads request connection to remote servers
 On the server side
◦ one thread is listening for client connection requests
andaccepts them
 Both client and server
◦ Use epoll interface to monitor and detect events from
concurrent connections
◦ rely on their data threads to perform the network
communication for data transfer.
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17.  Simply switching to the high-performance
interconnects cannot effectively boost
Hadoop’s performance
 Overhead imposed by JVM on Hadoop
intermediate data shuffling identified
 JVM-Bypass Shuffling (JBS)
 avoids JVM in the critical path of Hadoop data shuffling
 portable library to leverage both conventional TCP/IP
protocol and high-performance RDMA protocol
 reduce the execution time of Hadoop jobs by up
to66.3% a
 lower the CPU utilization by 48.1%
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18.  [1] Apache Hadoop Project.
http://hadoop.apache.org/.
 [2] Plugin for Generic Shuffle Service.
https://issues.apache.org/jira/browse/MAPREDUCE
-4049.
 [3] F. Ahmad, S. T. Chakradhar, A. Raghunathan,
and T. N. Vijaykumar. Tarazu: optimizing map
reduce on heterogeneous clusters. In Proceedings
of the seventeenth international conference on
Architectural Support for Programming Languages
and Operating Systems, ASPLOS’12, pages 61–74,
New York, NY, USA, 2012. ACM.
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19. THANK YOU
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