MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Proceedings of the 5th European conference on Computer systems
Evaluating I/O Scheduler in Virtual Machines for Mapreduce Application
GCC '10 Proceedings of the 2010 Ninth International Conference on Grid and Cloud Computing
Scheduling Hadoop Jobs to Meet Deadlines
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Towards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Exploiting Spatio-temporal Tradeoffs for Energy-Aware MapReduce in the Cloud
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Purlieus: locality-aware resource allocation for MapReduce in a cloud
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Location-Aware MapReduce in Virtual Cloud
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Adaptive Disk I/O Scheduling for MapReduce in Virtualized Environment
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Matchmaking: A New MapReduce Scheduling Technique
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
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As Cloud computing provides Anything as a Service (XaaS), many applications can be developed and run on the Cloud without concerns of platforms. Data-incentive applications are also easily developed on virtual machines provided by the Cloud. In this work, we investigate cost-effective resource provisioning for MapReduce applications with deadline constraints, as the MapReduce programming model is useful and powerful in developing data-incentive applications. When users want to run MapReduce applications, they submit jobs to a Cloud resource broker which allocates appropriate virtual machines with consideration of SLAs (Service-Level Agreements). The goal of resource provisioning in this paper is to minimize the cost of virtual machines for executing MapReduce applications without violating their deadlines to be finished by. We propose two resource provisioning approaches: one based on listed pricing policies and the other based on deadline-aware tasks packing. Throughout simulations, we evaluate and analyze them in various ways.