FARMS: Efficient mapreduce speculation for failure recovery in short jobs

Abstract

With the ever-increasing size of software and hardware components and the complexity of configurations, large-scale analytics systems face the challenge of frequent transient faults and permanent failures. As the indispensable part for big data analytics, MapReduce programming model is equipped with a speculation mechanism to cope with run-time stragglers and failures. However, we reveal that the existing speculation mechanism has some major drawbacks that hinder its efficiency during failure recovery, which we refer to as the speculation breakdown. We use the representative implementation of MapReduce, i.e., YARN and its speculation mechanism as a case study to demonstrate that the speculation breakdown causes significant performance degradation among MapReduce jobs, especially those with shorter turnaround time. As our experiments show, a single node failure can cause a job slowdown by up to 9.2 times. In order to address the speculation breakdown, we introduce a failure-aware speculation scheme and a refined task scheduling policy. Moreover, we have conducted a comprehensive set of experiments to evaluate the performance of both single component and the whole framework. Our experimental results show that our new framework achieves dramatic performance improvement in handling with node failures compared to the original YARN.

MapReduce Intermediate Data Migration Time Modeling using Machine Learning Technique

“…Similarly, some researches focused on particular phase scheduling [7], [8] and [9]. Some research has been done to improve the fault tolerance [10] and [11]. Some tried to focus on the replication and reliability issues [12] and [13].…”

Replication Effect over Hadoop MapReduce Performance using Regression Analysis

“…Similarly, some researches focused on particular phase scheduling [9][10][11]. Some research has been done to improve the fault tolerance [12][13]. Some tried to focus on the reliability issues [14].…”