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171
Improving MapReduce Performance in Heterogeneous Environments
, 2008
"... MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an open-source implementation of MapReduce enjoying wide adoption and is often used for short jobs where low response time is cri ..."
Abstract
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Cited by 350 (19 self)
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MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an open-source implementation of MapReduce enjoying wide adoption and is often used for short jobs where low response time
Towards Optimizing Hadoop Provisioning in the Cloud
"... Data analytics is becoming increasingly prominent in a variety of application areas ranging from extracting business intelligence to processing data from scientific studies. MapReduce programming paradigm lends itself well to these data-intensive analytics jobs, given its ability to scale-out and le ..."
Abstract
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Cited by 32 (4 self)
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-out and leverage several machines to parallely process data. In this work we argue that such MapReduce-based analytics are particularly synergistic with the pay-as-you-go model of a cloud platform. However, a key challenge facing end-users in this environment is the ability to provision MapReduce applications
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
, 2010
"... Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services ” to endusers under a usage-based payment model. They can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application se ..."
Abstract
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Cited by 199 (23 self)
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services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user
Slo-driven right-sizing and resource provisioning of mapreduce jobs
- In Workshop on Large Scale Distributed Systems and Middleware (LADIS) in conjunction with VLDB
, 2011
"... There is an increasing number of MapReduce applications, e.g., personalized advertising, spam detection, real-time event log analysis, that require completion time guarantees or need to be completed within a given time window. Currently, there is a lack of performance models and workload analy-sis t ..."
Abstract
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Cited by 4 (0 self)
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There is an increasing number of MapReduce applications, e.g., personalized advertising, spam detection, real-time event log analysis, that require completion time guarantees or need to be completed within a given time window. Currently, there is a lack of performance models and workload analy
STEAMEngine: Driving MapReduce Provisioning in the Cloud
, 2010
"... MapReduce has gained in popularity as a distributed data analysis paradigm, particularly in the cloud, where MapReduce jobs are run on virtual clusters. The provisioning of MapReduce jobs in the cloud is an important problem for optimizing several user as well as provider-side metrics, such as runti ..."
Abstract
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Cited by 1 (1 self)
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provisioning: along with their resource requirements (spatial component), a MapReduce job runtime (temporal component) is a critical element for any resource provisioning algorithm. We also describe two novel provisioning algorithms—a user-driven performance optimization and a provider-driven energy
HOG: Distributed Hadoop MapReduce on the Grid
"... Abstract—MapReduce is a powerful data processing platform for commercial and academic applications. In this paper, we build a novel Hadoop MapReduce framework executed on the Open Science Grid which spans multiple institutions across the United States – Hadoop On the Grid (HOG). It is different from ..."
Abstract
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Cited by 3 (1 self)
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across the U.S. to virtual racks and creating multi-institution failure domains. Our modifications to the Hadoop framework are transparent to existing Hadoop MapReduce applications. In the evaluation, we successfully extend HOG to 1100 nodes on the grid. Additionally, we evaluate HOG with a simulated
Using realistic simulation for performance analysis of mapreduce setups
- In Proceedings of the 1st ACM workshop on LargeScale System and Application Performance
, 2009
"... Recently, there has been a huge growth in the amount of data processed by enterprises and the scientific computing community. Two promising trends ensure that applications will be able to deal with ever increasing data volumes: First, the emergence of cloud computing, which provides transparent acce ..."
Abstract
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Cited by 16 (1 self)
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-scale production clusters shows that it is able to predict application performance accurately, and thus can be a useful tool in enabling cloud computing. Moreover, an initial application of MRPerf to our test clusters running Hadoop, revealed a performance bottleneck, fixing which resulted in up to 28
Toward Optimal Resource Provisioning for Cloud MapReduce and Hybrid Cloud Applications
"... Abstract-Given the variety of resources available in public clouds and locally (hybrid clouds), it can be very difficult to determine the best number and type of resources to allocate (and where) for a given activity. In order to solve this problem we first define the requested computation in terms ..."
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solutions that optimize speed or cost or a certain combination of both (e.g. cheapest solution that meets a certain deadline). We evaluate our approach with two classes of cloud applications: MapReduce applications, and Monte Carlo simulations. A significant advantage in our approach is that our solution
MapReduce Jobs ∗
"... MapReduce; Hadoop; performance models; completion time prediction; resource allocation There is an increasing number of MapReduce applications, e.g., personalized advertising, spam detection, real-time event log analysis, that need to be completed within a given time window. Currently, there is a la ..."
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MapReduce; Hadoop; performance models; completion time prediction; resource allocation There is an increasing number of MapReduce applications, e.g., personalized advertising, spam detection, real-time event log analysis, that need to be completed within a given time window. Currently, there is a
Benchmarking Joyent SmartDataCenter for Hadoop MapReduce and MPI Operations
"... Abstract—Cloud Computing is an ever-growing paradigm shift in computing allowing users commodity access to compute and storage services. As such cloud computing is an emerging promising approach for High Performance Computing (HPC) application development. Automation of resource provision offered by ..."
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Abstract—Cloud Computing is an ever-growing paradigm shift in computing allowing users commodity access to compute and storage services. As such cloud computing is an emerging promising approach for High Performance Computing (HPC) application development. Automation of resource provision offered
Results 1 - 10
of
171
