|By Srinivasan Sundara Rajan||
|January 3, 2011 12:00 PM EST||
While most of the case studies and value proposition for the Cloud is seen as a cost-reduction option for the enterprise by moving the fixed capacity infrastructure to a dynamic structure, there is also an element in cloud regarding its ability to perform processing of large amounts of information using highly parallel processes on scalable computing infrastructure.
Some of the major advantages of utilizing the Cloud for processing complex jobs are:
- Enable quicker innovation through access to additional compute resources in minutes instead of months.
- Scale compute resources to the size appropriate for each workload.
Amazon Elastic MapReduce
Amazon Elastic MapReduce is a web service that enables businesses, researchers, data analysts, and developers to easily and cost-effectively process vast amounts of data. It utilizes a hosted Hadoop framework running on the web-scale infrastructure of Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3).
Five Automobile Scenarios for Amazon Elastic MapReduce
Using Amazon Elastic MapReduce, you can instantly provision as much or as little capacity as you like to perform data-intensive tasks for applications such as web indexing, data mining, log file analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics research.
However, the following specific scenarios in the large automobile manufacturing companies will serve as best candidates for adopting high-performance computing using Amazon Elastic MapReduce.
1. Vehicle Model and Option Validation
Processing logic inside vehicle manufacturing, there are complex algorithms related to:
- Building the base vehicle configuration data
- Evaluating multiple models
- Defining and validating which vehicle options can be installed on a vehicle
- Which options to use to ensure compatibility
- There are also restrictions associated with a valid build of a vehicle
While the logic behind the vehicle validation is beyond the scope of this article, this complex algorithm is time-consuming and once tailored properly can be a valid candidate for Amazon Elastic MapReduce.
2. Vehicle Mass Analysis
The automobile industry is facing numerous challenges today. Clearly at the top of the list is the need to reduce vehicle weight. Several advanced algorithms, "What If Analysis," play vital role in mass reduction during the product design and development process.
In the vehicle development process, engineers design, analyze, test and then redesign a product. Design processes that incorporate optimization tools can evaluate hundreds of design concepts under multiple load conditions simultaneously. This ensures that the resulting design is then optimized for mass analysis while meeting all design and manufacturing targets.
Algorithms and processing associated with vehicle mass analysis can be a valid candidate for high-performance computing framework like Amazon Elastic MapReduce.
3. Emission Reporting
Regarding pollution control, to reduce the environment effects due to vehicle emissions, stricter controls and reporting is enforced by the EPA (Environmental Protection Agency) and by other similar agencies on the OEM. This, in turn, forces several advanced calculations to be done by automobile manufacturers.
For example there are complex calculations for:
- Vehicle pollution generated on a given trip (carbon monoxide, NOx, hydrocarbons, CO2)
- Monthly CO2 emissions and carbon footprint
Again these complex calculations on large volumes of data making it ideal candidate.
4. Recyclability and Recoverability
To support the statutory requirements especially in Europe for the end-of-life of a vehicle and related environmental issues, there are again complex calculations involved to:
- Analyze parts, components and vehicles in terms of their recyclability and recoverability (the ease with which parts can be recycled at the end of life)
- Life-cycle analysis studies
- Recyclability calculations
- Recovery weight calculations
Like the candidates above, this is again a candidate for MapReduce algorithms.
5. Warranty Claim Analysis
While several leading OEMs faced crisis due to recalls resulting out from vehicle faults, analyzing customer satisfaction surveys and warranty claims is very important to bring quality into the vehicle design. Several large organizations invest in large processing power to analyze warranty claims; again this will be an ideal candidate for the MapReduce algorithms implemented by Amazon High Performance Computing.
Summary and Challenges
While the automotive scenarios are good candidates for implementing the Amazon Elastic MapReduce functionality, it requires more design and analysis in terms of:
- Develop your data processing application. Amazon Elastic MapReduce enables job flows to be developed in SQL-like languages, such as Hive and Pig, making it easy to write data analytical scripts without in-depth knowledge of the MapReduce development paradigm. If desired, more sophisticated applications can be authored in your choice of Cascading, Java, Ruby, Perl, Python,PHP, R, or C++.
- Upload your data and your processing application into Amazon S3. Amazon S3 provides reliable, scalable, easy-to-use storage for your input and output data.
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