MRBS: A Comprehensive MapReduce Benchmark Suite

2014

Proceedings of the second international workshop on MapReduce and its applications - MapReduce '11, 2011

MapReduce is a highly-popular paradigm for high-performance computing over large data sets in large-scale platforms. However, when the source data is widely distributed and the computing platform is also distributed, e.g. data is collected in separate data center locations, the most efficient architecture for running Hadoop jobs over the entire data set becomes non-trivial. In this paper, we show the traditional single-cluster MapReduce setup may not be suitable for situations when data and compute resources are widely distributed. Further, we provide recommendations for alternative (and even hierarchical) distributed MapReduce setup configurations, depending on the workload and data set.

International Journal of Parallel Programming, 2012

MapReduce is a currently popular programming model to support parallel computations on large datasets. Among the several existing MapReduce implementations, Hadoop has attracted a lot of attention from both industry and research. In a Hadoop job, map and reduce tasks coordinate to produce a solution to the input problem, exhibiting precedence constraints and synchronization delays that are characteristic of a pipeline communication between maps (producers) and reduces (consumers). We here address the challenge of designing analytical models to estimate the performance of MapReduce workloads, notably Hadoop workloads, focusing particularly on the intra-job pipeline parallelism between map and reduce tasks belonging to the E. Vianna (B) · G. Comarela · T. Pontes · J.

2013 IEEE Sixth International Conference on Cloud Computing, 2013

MapReduce is now a pervasive analytics engine on the cloud. Hadoop is an open source implementation of MapReduce and is currently enjoying wide popularity. Hadoop offers a high-dimensional space of configuration parameters, which makes it difficult for practitioners to set for efficient and cost-effective execution. In this work we observe that MapReduce application performance is highly influenced by map concurrency. Map concurrency is defined in terms of two configurable parameters, the number of available map slots and the number of map tasks running over the slots. We show that some inherent MapReduce characteristics enable well-informed prediction of map concurrency. We propose Map Concurrency Characterization (M C 2), a standalone utility program that can predict the best map concurrency for any given MapReduce application. By leveraging the generated predicted information, M C 2 can judiciously guide Map phase configuration and, consequently, improve Hadoop performance. Unlike many of relevant schemes, M C 2 does not employ simulation, dynamic instrumentation, and/or static analysis of unmodified job code to predict map concurrency. In contrast, M C 2 utilizes a simple, yet effective mathematical model, which exploits the MapReduce characteristics that impact map concurrency. We implemented M C 2 and conducted comprehensive experiments on a private cloud and on Amazon EC2 using Hadoop 0.20.2. Our results show that M C 2 can correctly predict the best map concurrencies for the tested benchmarks and provide up to 2.2X speedup in runtime.

International Conference on Extending Database Technology, 2017

MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that may provide reasonably accurate job response time estimation at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance model for Hadoop 2.x. While there are several efficient approaches for modeling the performance of MapReduce workloads in Hadoop 1.x, they could not be applied to Hadoop 2.x due to fundamental architectural changes and dynamic resource allocation in Hadoop 2.x. Thus, the proposed solution is based on an existing performance model for Hadoop 1.x, but taking into consideration architectural changes and capturing the execution flow of a MapReduce job by using queuing network model. This way, the cost model reflects the intra-job synchronization constraints that occur due the contention at shared resources. The accuracy of our solution is validated via comparison of our model estimates against measurements in a real Hadoop 2.x setup.

The Journal of Supercomputing, 2019

In the current decade, doing the search on massive data to find "hidden" and valuable information within it is growing. This search can result in heavy processing on considerable data, leading to the development of solutions to process such huge information based on distributed and parallel processing. Among all the parallel programming models, one that gains a lot of popularity is MapReduce. The goal of this paper is to survey researches conducted on the MapReduce framework in the context of its open-source implementation, Hadoop, in order to summarize and report the wide topic area at the infrastructure level. We managed to do a systematic review based on the prevalent topics dealing with MapReduce in seven areas: (1) performance; (2) job/task scheduling; (3) load balancing; (4) resource provisioning; (5) fault tolerance in terms of availability and reliability; (6) security; and (7) energy efficiency. We run our study by doing a quantitative and qualitative evaluation of the research publications' trend which is published between January 1, 2014, and November 1, 2017. Since the MapReduce is a challenge-prone area for researchers who fall off to work and extend with, this work is a useful guideline for getting feedback and starting research.

2009

MapReduce has emerged as a model of choice for supporting modern data-intensive applications. The model is easy-to-use and promising in reducing time-to-solution. It is also a key enabler for cloud computing, which provides transparent and flexible access to a large number of compute, storage and networking resources.

2012 IEEE 31st Symposium on Reliable Distributed Systems, 2012

MapReduce is a popular programming model for distributed data processing. Extensive research has been conducted on the reliability of MapReduce, ranging from adaptive and on-demand fault-tolerance to new fault-tolerance models. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce fault-tolerance solutions have been evaluated using microbenchmarks in an ad-hoc and overly simplified setting, which may not be representative of real-world applications. This paper presents MRBS, a comprehensive benchmark suite for evaluating the dependability of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to inject various types of faults at different rates. It also considers different application workloads and dataloads, and produces extensive reliability, availability and performance statistics. We illustrate the use of MRBS with Hadoop clusters running on Amazon EC2, and on a private cloud.

Concurrency and Computation: Practice and Experience, 2015

Efficiently scheduling MapReduce tasks is considered as one of the major challenges that face MapReduce frameworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing on the data locality property for tasks scheduling. The data locality may cause less physical resources utilization in non-virtualized clusters and more power consumption. Virtualized clusters provide a viable solution to support both data locality and better cluster resources utilization. In this paper, we evaluate the major MapReduce scheduling algorithms such as FIFO, Matchmaking, Delay, and multithreading locality (MTL) on virtualized infrastructure. Two major factors are used to test the evaluated algorithms: the simulation time and the energy consumption. The evaluated schedulers are compared, and the results show the superiority and the preference of the MTL scheduler over the other existing schedulers. Also, we present a comparison study between virtualized and non-virtualized clusters for MapReduce tasks scheduling. Q. ALTHEBYAN ET AL.

MapReduce and Spark are two very popular open source cluster computing frameworks for large scale data analytics. These frameworks hide the complexity of task parallelism and fault-tolerance, by exposing a simple programming API to users. In this paper, we evaluate the major architectural components in MapReduce and Spark frameworks including: shuffle, execution model, and caching, by using a set of important analytic workloads. To conduct a detailed analysis, we developed two profiling tools: (1) We correlate the task execution plan with the resource utilization for both MapReduce and Spark, and visually present this correlation; (2) We provide a breakdown of the task execution time for in-depth analysis. Through detailed experiments, we quantify the performance differences between MapReduce and Spark. Furthermore, we attribute these performance differences to different components which are architected differently in the two frameworks. We further expose the source of these performance differences by using a set of micro-benchmark experiments. Overall, our experiments show that Spark is about 2.5x, 5x, and 5x faster than MapReduce, for Word Count, k-means, and PageRank, respectively. The main causes of these speedups are the efficiency of the hash-based aggre-gation component for combine, as well as reduced CPU and disk overheads due to RDD caching in Spark. An exception to this is the Sort workload, for which MapReduce is 2x faster than Spark. We show that MapReduce's execution model is more efficient for shuffling data than Spark, thus making Sort run faster on MapRe-duce.