Mitigate data skew caused stragglers through ImKP partition in MapReduce

Speculative execution is the mechanism adopted by current MapReduce framework when dealing with the straggler problem, and it functions through creating redundant copies for identified stragglers. The result of the quicker task will be adopted to improve the overall job execution performance. Although proved to be effective for contention caused stragglers, speculative execution can easily meet its bottleneck when mitigating data skew caused stragglers due to its replication nature: the identical unbalanced input data will lead to a slow speculative task. The Map inputs are typically even in size according to the HDFS block configuration, therefore the skew caused stragglers happen mainly in the Reduce phase because of the unknown intermediate key distribution. In this paper, we focus on mitigating data skew caused Reduce stragglers, propose ImKP, an Intermediate Key Pre-processing framework that enables the even distributed partition for Reduce inputs. A group based ranking technique has been developed that dramatically decreases the pre-processing time, and ImKP manages to eliminate this timing overhead through parallelizing the pre-processing with the file uploading procedure (from local file system to HDFS). For jobs that take input directly from HDFS, ImKP minimizes the overhead by storing the mapping result on every node within the cluster for reuse. Experiments are conducted on different datasets with various workloads. Results show that, compared to the popular hash partition, ImKP can dramatically decrease Reduce skew, achieving a 99.8% reduction in the coefficient of variation of the input sizes in average, and improve up to 29.37% job response performance

Timely Long Tail Identification through Agent Based Monitoring and Analytics

The increasing complexity and scale of distributed systems has resulted in the manifestation of emergent behavior which substantially affects overall system performance. A significant emergent property is that of the "Long Tail", whereby a small proportion of task stragglers significantly impact job execution completion times. To mitigate such behavior, straggling tasks occurring within the system need to be accurately identified in a timely manner. However, current approaches focus on mitigation rather than identification, which typically identify stragglers too late in the execution lifecycle. This paper presents a method and tool to identify Long Tail behavior within distributed systems in a timely manner, through a combination of online and offline analytics. This is achieved through historical analysis to profile and model task execution patterns, which then inform online analytic agents that monitor task execution at runtime. Furthermore, we provide an empirical analysis of two large-scale production Cloud data enters that demonstrate the challenge of data skew within modern distributed systems, this analysis shows that approximately 5% of task stragglers caused by data skew impact 50% of the total jobs for batch processes. Our results demonstrate that our approach is capable of identifying task stragglers less than 11% into their execution lifecycle with 98% accuracy, signifying significant improvement over current state-of-the-art practice and enables far more effective mitigation strategies in large-scale distributed systems worldwide