Using realistic simulation for performance analysis of mapreduce setups

Abstract

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 access to a large number of compute, storage and networking resources; and second, the development of the MapReduce programming model, which provides a highlevel abstraction for data-intensive computing. However, the design space of these systems has not been explored in detail. Specifically, the impact of various design choices and run-time parameters of a MapReduce system on application performance remains an open question.To this end, we embarked on systematically understanding the performance of MapReduce systems, but soon realized that understanding effects of parameter tweaking in a large-scale setup with many variables was impractical. Consequently, in this paper, we present the design of an accurate MapReduce simulator, MRPerf, for facilitating exploration of MapReduce design space. MRPerf captures various aspects of a MapReduce setup, and uses this information to predict expected application performance. In essence, MRPerf can serve as a design tool for MapReduce infrastructure, and as a planning tool for making MapReduce deployment far easier via reduction in the number of parameters that currently have to be hand-tuned using rules of thumb.Our validation of MRPerf using data from medium-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 28.05% performance improvement.

High Throughput and Low Latency on Hadoop Clusters Using Explicit Congestion Notification: The Untold Truth

“…We replicated the methodology used in recent work [10], using the NS-2 packet-level network simulator [16], so we are able to demonstrate the robustness of our findings. Therefore, the NS-2 simulator has been extend with DCTCP [17] implementation and is driven by the MRPerf MapReduce simulator [18].…”

Energy Efficient Ethernet on MapReduce Clusters: Packet Coalescing To Improve 10GbE Links

“…This simulator has been extended with a model of Energy Efficient Ethernet [32], which has been previously validated [8] and used extensively in previous work [33]. The network simulator is driven by the MRPerf MapReduce simulator [34]. We could not use real hardware because the EEE control algorithm is implemented in NIC and switch firmware, and no hardware was available for which we were able to change the packet coalescing settings.…”

Disk I/O Performance-per-Watt Analysis for Cloud Computing

“…In other words, the performance for such workloads is bounded by disk latency (time) and throughput (operations/ second). Hadoop is based on Google's map-reduce [2] [3] in which a given workload task is broken into small tasks and these tasks are distributed to be processed on different nodes in a cloud cluster. The resource utilization for these benchmarks is categorized into three categories as shown in Table 1.…”