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Map-Reduce Synchronized and Comparative Queue Capacity Scheduler in Hadoop fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Performance evaluation of Map-reduce jar pig hive and spark with machine lear...IJECEIAES
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.
A comparative survey based on processing network traffic data using hadoop pi...ijcses
Big data analysis has now become an integral part of many computational and statistical departments.
Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data
manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every
minute by different websites related to e-commerce, shopping carts and online banking. Here comes the
need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can
efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
Map Reduce Workloads: A Dynamic Job Ordering and Slot Configurationsdbpublications
MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks. Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a MapReduce workload have significantly different performance and system utilization. This survey proposes two classes of algorithms to minimize the make span and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 - 80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.
Map-Reduce Synchronized and Comparative Queue Capacity Scheduler in Hadoop fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Performance evaluation of Map-reduce jar pig hive and spark with machine lear...IJECEIAES
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.
A comparative survey based on processing network traffic data using hadoop pi...ijcses
Big data analysis has now become an integral part of many computational and statistical departments.
Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data
manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every
minute by different websites related to e-commerce, shopping carts and online banking. Here comes the
need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can
efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
Map Reduce Workloads: A Dynamic Job Ordering and Slot Configurationsdbpublications
MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks. Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a MapReduce workload have significantly different performance and system utilization. This survey proposes two classes of algorithms to minimize the make span and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 - 80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.
Cloud has been a computational and storage solution for many data centric organizations. The
problem today those organizations are facing from the cloud is in data searching in an efficient
manner. A framework is required to distribute the work of searching and fetching from
thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The
major idea is to design a web server in the map phase using the jetty web server which will give
a fast and efficient way of searching data in MapReduce paradigm. For real time processing on
Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in
web server with multi-level index keys. The web server uses to handle traffic throughput. By web
clustering technology we can improve the application performance. To keep the work down, the
load balancer should automatically be able to distribute load to the newly added nodes in the
server.
Automatic Parameter Tuning for Databases and Big Data Systems Jiaheng Lu
Database and big data analytics systems such as Hadoop and Spark have a large number of configuration parameters that control memory distribution, I/O optimization, parallelism, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators struggle to understand and tune them to achieve good performance. In this tutorial, we review existing approaches on automatic parameter tuning for databases, Hadoop, and Spark, which we classify into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We describe the foundations of different automatic parameter tuning algorithms and present pros and cons of each approach. We also highlight real-world applications and systems and identify research challenges for handling cloud services, resource heterogeneity, and real-time analytics
Capacity Management and BigData/Hadoop - Hitchhiker's guide for the Capacity ...Renato Bonomini
Hadoop is a zoo of different types of workloads; even if most companies are simply using Hadoop to store information (HDFS), there are many other applications, to name a few hdfs, hive, pig, impala, spark, solr, flume.
Each animal in this zoo behaves differently and, for example, there are significant differences in the two most common workloads “MapReduce” and “HBase”
This leads to mainly three point of views for analysis to make sure service levels are achieved:
- Interest in response time for “interactive workload” CPU, Memory, Network and IO utilization levels to respond to queries in a quick and effective way
- Interest in high throughput for “batch workloads”: Maximize the utilization levels, not interested in response time
- Interest in planning storage capacity (filesystem and HDFS)
This speech focuses on providing guidelines for the capacity planner to understand how to translate existing techniques and framework and to adapt them to these new technologies: in most cases “what’s old is new again”
Game Changed – How Hadoop is Reinventing Enterprise ThinkingInside Analysis
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast on April 8, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=cfa1bffdd62dc6677fa225bdffe4a0b9
The innovation curve often arcs slowly before picking up speed. Companies that harness a major transformation early in the game can make serious headway before challengers enter the picture. The world of Hadoop features several of these upstarts, each of which uses the open-source foundation as an engine to drive vastly greater performance to a wide range of services, and even create new ones.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain how the Hadoop engine is being used to architect a new generation of enterprise applications. He’ll be briefed by George Corugedo, RedPoint Global CTO and Co-founder, who will showcase how enterprises can cost-effectively take advantage of the scalability, processing power and lower costs that Hadoop 2.0/YARN applications offer by eliminating the long-term expense of hiring MapReduce programmers.
Visit InsideAnlaysis.com for more information.
How pig and hadoop fit in data processing architectureKovid Academy
Pig, developed by Yahoo research in 2006, enables programmers to write data transformation programs for Hadoop quickly and easily without the cost and complexity of map-reduce programs.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
Rfhoc a random forest approach to auto-tuning hadoop’s configurationLeMeniz Infotech
Rfhoc a random forest approach to auto-tuning hadoop’s configuration
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
About Streaming Data Solutions for HadoopLynn Langit
Whitepaper comparing capabilities for various streaming data solutions for Hadoop - includes Apache Storm, Apache Spark Streaming and commercial solutions, such as Data Torrent
Performance evaluation and estimation model using regression method for hadoo...redpel dot com
Performance evaluation and estimation model using regression method for hadoop word count.
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Cloud has been a computational and storage solution for many data centric organizations. The
problem today those organizations are facing from the cloud is in data searching in an efficient
manner. A framework is required to distribute the work of searching and fetching from
thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The
major idea is to design a web server in the map phase using the jetty web server which will give
a fast and efficient way of searching data in MapReduce paradigm. For real time processing on
Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in
web server with multi-level index keys. The web server uses to handle traffic throughput. By web
clustering technology we can improve the application performance. To keep the work down, the
load balancer should automatically be able to distribute load to the newly added nodes in the
server.
Automatic Parameter Tuning for Databases and Big Data Systems Jiaheng Lu
Database and big data analytics systems such as Hadoop and Spark have a large number of configuration parameters that control memory distribution, I/O optimization, parallelism, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators struggle to understand and tune them to achieve good performance. In this tutorial, we review existing approaches on automatic parameter tuning for databases, Hadoop, and Spark, which we classify into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We describe the foundations of different automatic parameter tuning algorithms and present pros and cons of each approach. We also highlight real-world applications and systems and identify research challenges for handling cloud services, resource heterogeneity, and real-time analytics
Capacity Management and BigData/Hadoop - Hitchhiker's guide for the Capacity ...Renato Bonomini
Hadoop is a zoo of different types of workloads; even if most companies are simply using Hadoop to store information (HDFS), there are many other applications, to name a few hdfs, hive, pig, impala, spark, solr, flume.
Each animal in this zoo behaves differently and, for example, there are significant differences in the two most common workloads “MapReduce” and “HBase”
This leads to mainly three point of views for analysis to make sure service levels are achieved:
- Interest in response time for “interactive workload” CPU, Memory, Network and IO utilization levels to respond to queries in a quick and effective way
- Interest in high throughput for “batch workloads”: Maximize the utilization levels, not interested in response time
- Interest in planning storage capacity (filesystem and HDFS)
This speech focuses on providing guidelines for the capacity planner to understand how to translate existing techniques and framework and to adapt them to these new technologies: in most cases “what’s old is new again”
Game Changed – How Hadoop is Reinventing Enterprise ThinkingInside Analysis
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast on April 8, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=cfa1bffdd62dc6677fa225bdffe4a0b9
The innovation curve often arcs slowly before picking up speed. Companies that harness a major transformation early in the game can make serious headway before challengers enter the picture. The world of Hadoop features several of these upstarts, each of which uses the open-source foundation as an engine to drive vastly greater performance to a wide range of services, and even create new ones.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain how the Hadoop engine is being used to architect a new generation of enterprise applications. He’ll be briefed by George Corugedo, RedPoint Global CTO and Co-founder, who will showcase how enterprises can cost-effectively take advantage of the scalability, processing power and lower costs that Hadoop 2.0/YARN applications offer by eliminating the long-term expense of hiring MapReduce programmers.
Visit InsideAnlaysis.com for more information.
How pig and hadoop fit in data processing architectureKovid Academy
Pig, developed by Yahoo research in 2006, enables programmers to write data transformation programs for Hadoop quickly and easily without the cost and complexity of map-reduce programs.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
Rfhoc a random forest approach to auto-tuning hadoop’s configurationLeMeniz Infotech
Rfhoc a random forest approach to auto-tuning hadoop’s configuration
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
About Streaming Data Solutions for HadoopLynn Langit
Whitepaper comparing capabilities for various streaming data solutions for Hadoop - includes Apache Storm, Apache Spark Streaming and commercial solutions, such as Data Torrent
Performance evaluation and estimation model using regression method for hadoo...redpel dot com
Performance evaluation and estimation model using regression method for hadoop word count.
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Self adjusting slot configurations for homogeneous and heterogeneous hadoop c...LeMeniz Infotech
Self adjusting slot configurations for homogeneous and heterogeneous hadoop clusters
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
Hadoop performance modeling for job estimation and resource provisioningLeMeniz Infotech
Hadoop performance modeling for job estimation and resource provisioning
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
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Hourglass: a Library for Incremental Processing on HadoopMatthew Hayes
Hadoop enables processing of large data sets through its relatively easy-to-use semantics. However, jobs are often written inefficiently for tasks that could be computed incrementally due to the burdensome incremental state management for the programmer. This paper introduces Hourglass, a library for developing incremental monoid computations on Hadoop. It runs on unmodified Hadoop and provides an accumulator-based interface for programmers to store and use state across successive runs; the framework ensures that only the necessary subcomputations are performed. It is successfully used at LinkedIn, one of the largest online social networks, for many use cases in dashboarding and machine learning. Hourglass is open source and freely available.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
1. Hadoop MapReduce Performance Prediction
www.kellytechno.com Page 1
A Hadoop MapReduce Performance Prediction
Method
Abstract—More and more Internet companies rely on large
scale data analysis as part of their core services for tasks such
as log analysis, feature extraction or data filtering. Map-Reduce,
through its Hadoop implementation, has proved to be an efficient
model for dealing with such data. One important challenge when
performing such analysis is to predict the performance of individual
jobs. In this paper, we propose a simple framework to predict
the performance of Hadoop jobs. It is composed of a dynamic
light-weight Hadoop job analyzer, and a prediction module using
locally weighted regression methods. Our framework makes some
theoretical cost models more practical, and also well fits for the
diversification of the jobs and clusters. It can also help those
users who want to predict the cost when applying for an ondemand
cloud service. At the end, we do some experiments to
verify our framework.
Index Terms—Locally Weighted Regression; Job Analyzer;
Performance Prediction; Hadoop; MapReduce;
I. INTRODUCTION
It has been widely accepted that we are facing an information
booming era. The amount of data becomes very huge, and
traditional ways to manage and to process no longer work. In
such a situation, MapReduce[1] has been proved as an efficient
way to deal with ”Big Data”. Hadoop[2] is an open source
2. Hadoop MapReduce Performance Prediction
www.kellytechno.com Page 2
implementation of MapReduce. A lot of internet companies
have deployed many Hadoop clusters to provide core services,
such as log analytic, data mining, feature extraction, etc. But
usually the efficiency of these clusters is not very high. After
studying the performance of some Hadoop clusters, we find
some interesting problems as the following:
• How to design an efficient scheduling policy? There are
many works done on scheduling policies for Hadoop
clusters. But some of the advanced policies (such as
[3] [4] [5]) require high level performance estimation in
advance to decide scheduling strategies.
• How to tune the parameters? Hadoop provides more
than 200 parameters both for the clusters and also for
the jobs. But in most of the time, users just choose
to use the default values or tune the parameters rely
on some empirical values. But if we can estimate the
job performance before execution, we can give more
reasonable values.
• How to optimize the job performance? More and more
companies pay attention on the ROI, which refers to
”return of investment”. They not only need to solve the
problems but also want to optimize the job performance,
so that they could use less time and less resources to
solve more problems.
• How to balance between the cost and the performance?
3. Hadoop MapReduce Performance Prediction
www.kellytechno.com Page 3
As IaaS becomes more and more popular, some users
prefer to use an on-demand service rather than to deploy
their own clusters. In such a situation, they need to
precisely decide how long and how many nodes will they
use.
To well settle these problems, the main point is to estimate
the job performance in advance. That inspires us to create the
framework described in this paper to predict the performance
of a hadoop job. In order to provide plentiful predictions, our
work is based on the following 2 main parts:
• A job analyzer: which is used to analyse the jobs submitted
by the users to collect the features related with the
jobs, it can also collect the parameters related with the
clusters.
• A prediction module: which is used for estimating the
performance in using a local weighted linear regretion
method.
The main contributions of this paper are as follows:
1) We design a light-weight hadoop job analyzer. It can be
used not only as a job performance analyzer but also a
parameter collecter.
2) We propose a prediction module, which combines two
kinds of information given by the job analyzer and the
history traces to predict job performance.
II. RELATED WORK
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A. Hadoop MapReduce
The MapReduce programming model was initially developed
by [1] for processing large scale of data. Hadoop is
an open source framework made of a MapReduce framework
and a distributed file system called HDFS. It is very popular
not only among the academic institution but also in many
real industries such as web search, social network, economic
computation and so on. A lot of research work committed to
optimize the Hadoop jobs performance and the efficiency of
the Hadoop clusters in many different aspects [6] [7] [8].
When running a Hadoop job, the large amount of input
data will be first divided into some splits (64M by default).
Then each split will be executed by a user-defined map task.
Take Word Count as an example, each input split contains
several lines of an article, each line is read as a record and
then will be wrapped as key objects and value objects. Then
the map function will consume a key and a value object and
emit a key and value object as well. During this process, all the
records (each line) will be executed by the same map function.
After all the map tasks finish, the reduce tasks will pull the
corresponding partitions from the output of the map tasks.
Then all these data will be sorted and mergered in the reduce
tasks to make sure that all the values with the same key will
be put together. Finally reduce tasks will run and produce the
output data.
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B. Hadoop Simulator
To the best of our knowledge, there are two kinds of
Hadoop simulators. One is trying to ”replace” the Hadoop
framework and usually focuses on the scheduling policy design
[9] or resource allocation [10]. To simplify their problem and
evaluation, they use some simulator to help them analyze
the jobs. Usually these simulators are just simply an analog
of the MapReduce framework without any complex node
communication or process communication. Another one [11] is
trying to analyze the application performance on MapReduce
cluster through studying the language syntax, logical data-
flow, data storage and its implementations. It provides a
vivid MapReduce environment and can be used to test the
scheduling algorithms. The framework in [11] is designed
based on Grid Sim. It will not sample the input data and
does not care about the complexity of the jobs. Instead, it will
record the performance after executing the whole job in the
environment of their simulator. In other words, this simulator
is not light weight, and can not meet a quasi-realtime need.
All these works are not well suited for our prediction need.
As described before, our job analyzer can not only support real
Hadoop jobs but also profile usefull information about jobs and
give them to the prediction moduler. Most of the others works
do not focus on job analyzing, and cannot provide enough
information, such as the complexity of the Map functions and
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Reduce functions, the conversion rate of the data and so on,
for a further module to make prediction. At the same time,
our job analyser is light-weight, it will consume only a little
additional cost to provide plenty of information. And it is not
only a simulator, it is designed to collect information and to
analyze job performance, but it could also be used as a Map
and Reduce function debugger tool.
C. Predictor
Some predictors are statistic based black box model , while
others are cost model based white box model. In [12], the
author classify the job into several categories by collecting
the history trace of a given cluster. And inside each categories,
they use a statistic model to predict job execution time. The
authors also compare some clustering technics and feature
elimination technics, then propose to use Kernel Canonical
Correlation Analysis (KCCA) statistic model to find out the
correlation between the features (e.x. inputSize, shuffleInputRatio,
outputShuffleRatio, etc.) and the job execution times.
Fig. 1. System Overview
The biggest difference between our work and this kind of
work is that we focus on predicting detailed information about
jobs. Meanwhile, their features can not be obtained before job
execution, and we can use our job analyser to get our features.
Another kind of predictor is based on cost-model. The whatif
engine discribed in [13] is focusing on optimizing Hadoop
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job performance by predicting job performance. This engine
is actually a predictor. It gives a corresponding prediction by
tuning Hadoop job configurations. But as Hadoop has more
than 200 configuration parameters, it’s not easy to control the
work under a low overhead. And in our work, we only care
about a few main features (5 for Map and 6 for Reduce) which
can accurately react the performance, so that we can give the
prediction of the performance within a reasonable time and
help to guide the scheduling policy or any other tuning needs.
III. SYSTEM DESIGN
In this section, we introduce the design of our system. The
purpose of this system is to predict the job performance, hence,
i.e. the execution time of Map task and Reduce task. Our work
can also help some ideal cost models such as [14] to calculate
the CPU, Disk I/O and Network I/O cost as well. Meanwhile, it
can also be used to help other optimizing works about Hadoop
such as [15] to estimate their cost. All the parts in our system
are loosely coupled. Figure 1 presents an overview.
A. Job Analyzer
The purpose of the Job Analyzer is to extract the following
information from the use submitted job. First, it measures the
data input size and the number of records. Second, it tries to
estimate the complexity of the Map and Reduce functions.
Finally, it estimates the data conversion rate, i.e. the ratio
between the input and output data of a mapper.
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And these information should be achieved in a reansonable
time within a low latency, because any additional time cost is
not welcomed by users. To achieve these goals, we first get
a sample of the input data instead of processing the whole
data set. Then we extract and modify the procedure code only
for Map and Reduce functions, eliminate all the costs for
transfering data, initiate the cluster, and so on. Finally, we use
the reflection mechanism of Java to instantiate the processing
class of Map and Reduce in users’ jobs.
1) Feasibility Analysis: We argue that our method is feasible
from the following 2 aspects:
The introduction about Hadoop in sectio I shows out that
a Hadoop MapReduce job has some special characteristics as
shown below,