Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
Building a high-level dataflow system on top of Map-Reduce: the Pig experience
Proceedings of the VLDB Endowment
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
ParaTimer: a progress indicator for MapReduce DAGs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Towards optimizing hadoop provisioning in the cloud
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
ARIA: automatic resource inference and allocation for mapreduce environments
Proceedings of the 8th ACM international conference on Autonomic computing
FLEX: a slot allocation scheduling optimizer for MapReduce workloads
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
CoScan: cooperative scan sharing in the cloud
Proceedings of the 2nd ACM Symposium on Cloud Computing
Towards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
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Many applications associated with live business intelligence are written as complex data analysis programs defined by directed acyclic graphs of MapReduce jobs, for example, using Pig, Hive, or Scope frameworks. An increasing number of these applications have additional requirements for completion time guarantees. In this article, we consider the popular Pig framework that provides a high-level SQL-like abstraction on top of MapReduce engine for processing large data sets. There is a lack of performance models and analysis tools for automated performance management of such MapReduce jobs. We offer a performance modeling environment for Pig programs that automatically profiles jobs from the past runs and aims to solve the following inter-related problems: (i) estimating the completion time of a Pig program as a function of allocated resources; (ii) estimating the amount of resources (a number of map and reduce slots) required for completing a Pig program with a given (soft) deadline. First, we design a basic performance model that accurately predicts completion time and required resource allocation for a Pig program that is defined as a sequence of MapReduce jobs: predicted completion times are within 10% of the measured ones. Second, we optimize a Pig program execution by enforcing the optimal schedule of its concurrent jobs. For DAGs with concurrent jobs, this optimization helps reducing the program completion time: 10%--27% in our experiments. Moreover, it eliminates possible nondeterminism of concurrent jobs’ execution in the Pig program, and therefore, enables a more accurate performance model for Pig programs. Third, based on these optimizations, we propose a refined performance model for Pig programs with concurrent jobs. The proposed approach leads to significant resource savings (20%--60% in our experiments) compared with the original, unoptimized solution. We validate our solution using a 66-node Hadoop cluster and a diverse set of workloads: PigMix benchmark, TPC-H queries, and customized queries mining a collection of HP Labs’ web proxy logs.