Performance Model for MapReduce Naï ve Bayesian
Model Driven Performance Simulation of Cloud Provisioned Hadoop MapReduce Applications
... the performance characteristics of a Hadoop job can help achieve an optimal balance between resource usage (cost) and job latency on a cloud-based ...the performance of a MapReduce application in a ...
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MapReduce for Bayesian Network Parameter Learning using the EM Algorithm
... 2: Performance of MREM on dif- ferent networks for varying ...and Performance It is important to find the Hadoop configuration that gives a good trade-off between cost and per- ...
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Accelerating Bayesian Network Parameter Learning Using Hadoop and MapReduce
... 3.4 Design Choices for MREM We use hard-coded combiner and optimal resource utiliza- tion to improve the performance of MREM. In a naive map-reduced implementation, each mapper emits a key-value pair per family ...
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A Hadoop MapReduce Performance Prediction Method
... † Ecole Centrale de Paris, France ‡ Beihang University, Beijing China 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 ...
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A Hadoop MapReduce Performance Prediction Method
... † Ecole Centrale de Paris, France ‡ Beihang University, Beijing China 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 ...
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An Optimized Model for MapReduce Based on Hadoop
... the MapReduce, a distributed and parallel programming model, greatly simplifies the design of parallel programming, and the design concept of migrating computation instead of data greatly alleviated the I/O ...
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An Enhanced DACHE Model for the MapReduce Environment
... the performance of the MapReduce framework, making the searching process more easy and efficient than the existing technique and eliminating the duplicate task that co-exists with the incremental ...
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A Model-driven Approach for Price/Performance Tradeos in Cloud-based MapReduce Application Deployment
... executing MapReduce jobs at various CSPs, selecting the best option and deploying and executing the job on the selected CSP ...a model-driven engineering (MDE) framework that shields the users from the ...
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Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce
... First we investigate the performance of ALEM versus MEM. A set of 20,000 ALEM and MEM trials were performed on the BNs Asia and Alarm to measure differences in likelihood and iterations until population ...
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A Bayesian model for predicting face recognition performance using image quality
... a Bayesian ap- proach to model the relation between image quality (like pose, illumination, noise, sharpness, etc) and correspond- ing face recognition ...our model can accu- rately aggregate ...
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Enhancing building performance : a Bayesian network model to support facility management
... The evaluation of building performance with the proposed BN model combines the perspective of different stakeholders, namely: facility managers, occupants, and owners. The first perspective relates to the ...
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THE ANATOMY OF MAPREDUCE JOBS, SCHEDULING, AND PERFORMANCE CHALLENGES
... 5 MapReduce Performance Challenges Hundereds of thousands of jobs of varying demands on CPU, I/O and network are executed on Hadoop clusters consisting of several hundred ...Hadoop MapReduce jobs is ...
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Enhance HADOOP Performance by Tuning Mapreduce Configuration
... support. MapReduce is emerging as an important programming model for large- scale data-parallel applications such as web indexing, data mining, and scientific ...simulation. MapReduce is a widely ...
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PERFORMANCE MODELING AND RESOURCE MANAGEMENT FOR MAPREDUCE APPLICATIONS
... On the other hand, the advent of cloud computing provides a new delivery model with virtually unlimited resources. It allows the customers to deploy their Hadoop clusters by leasing computing and storage resources ...
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Benchmarking and performance modelling of MapReduce communication pattern
... Cost Model For The Entire Process The models presented in the previous section represent models for a single ...task. MapReduce runs several tasks in ...previous performance models using this ...
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MapReduce performance models for Hadoop 2.x
... There has also been an attempt of evaluating the impact of task scheduling on system performance. However, current schedulers neither pack tasks nor consider all their relevant resource demands. This results in ...
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Title: A Survey on MapReduce Performance and Hadoop Acceleration
... As soon as the last MOF is available, completed PQs are constructed. The full pipeline of shuffle, merges, and reduces then starts. One may notice that there is still a serialization between the availability of the last ...
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Performance Modeling and Resource Management for Mapreduce Applications
... On the other hand, the advent of cloud computing provides a new delivery model with virtually unlimited resources. It allows the customers to deploy their Hadoop clusters by leasing computing and storage resources ...
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Algorithm Rebuilding and Performance Optimization of MapReduce in Hadoop
... Abstract-—As a core component of Hadoop that is a cloud open platform, MapReduce is a distributed and parallel computing model based on mapping function for processing and generating lar[r] ...
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Enhhanced and Efficient Memory Model For Hadoop Mapreduce
... such model a Parallel Hadoop MapReduce makespan model is ...makespan model is designed considering minimizing unutilized/null slot of virtual computing node ...optimization model for ...
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