Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service

An increasing number of Analytics-as-a-Service solutions has recently seen the light, in the landscape of cloud-based services. These services allow flexible composition of compute and storage components, that create powerful data ingestion and processing pipelines. This work is a first attempt at an experimental evaluation of analytic application performance executed using a wide range of storage service configurations. We present an intuitive notion of data locality, that we use as a proxy to rank different service compositions in terms of expected performance. Through an empirical analysis, we dissect the performance achieved by analytic workloads and unveil problems due to the impedance mismatch that arise in some configurations. Our work paves the way to a better understanding of modern cloud-based analytic services and their performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1

Scientific Computing Meets Big Data Technology: An Astronomy Use Case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark -- a modern big data platform -- to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications

AdaptDB: Adaptive Partitioning for Distributed Joins

Big data analytics often involves complex join queries over two or more tables. Such join processing is expensive in a distributed setting both because large amounts of data must be read from disk, and because of data shuffling across the network. Many techniques based on data partitioning have been proposed to reduce the amount of data that must be accessed, often focusing on finding the best partitioning scheme for a particular workload, rather than adapting to changes in the workload over time. In this paper, we present AdaptDB, an adaptive storage manager for analytical database workloads in a distributed setting. It works by partitioning datasets across a cluster and incrementally refining data partitioning as queries are run. AdaptDB introduces a novel hyper-join that avoids expensive data shuffling by identifying storage blocks of the joining tables that overlap on the join attribute, and only joining those blocks. Hyper-join performs well when each block in one table overlaps with few blocks in the other table, since that will minimize the number of blocks that have to be accessed. To minimize the number of overlapping blocks for common join queries, AdaptDB users smooth repartitioning to repartition small portions of the tables on join attributes as queries run. A prototype of AdaptDB running on top of Spark improves query performance by 2-3x on TPC-H as well as real-world dataset, versus a system that employs scans and shuffle-joins

Why Let Resources Idle? Aggressive Cloning of Jobs with Dolly

Abstract Despite prior research on outlier mitigation, our analysis of jobs from the Facebook cluster shows that outliers still occur, especially in small jobs. Small jobs are particularly sensitive to long-running outlier tasks because of their interactive nature. Outlier mitigation strategies rely on comparing different tasks of the same job and launching speculative copies for the slower tasks. However, small jobs execute all their tasks simultaneously, thereby not providing sufficient time to observe and compare tasks. Building on the observation that clusters are underutilized, we take speculation to its logical extreme-run full clones of jobs to mitigate the effect of outliers. The heavy-tail distribution of job sizes implies that we can impact most jobs without using much resources. Trace-driven simulations show that average completion time of all the small jobs improves by 47% using cloning, at the cost of just 3% extra resources

Fine-grained Memory Locality Aware Scheduling Techniques in a Virtualized Cluster

Department of Computer Science and EngineeringBig-data processing platforms can be used in a virtualized cluster. But, the well-known big data processing system does not take account into the virtualized cluster. In this research, we designed and implemented Cache-Affinity and Virtualization-Aware (CAVA) resource manager which schedules tasks considering both in-memory cache and virtualized environment. CAVA uses the properties of a virtualized cluster such that transferring data between two co-resident Virtual Machines is performed very fast because it does not use physical network IO. CAVA also tries to read input data from in-memory cache prior to disk regardless of whether input data is in the same or different physical machine. This methodology is very effective in the virtualized cluster because it exploits the chances to reuse cached input data in the cluster and mitigate disk I/O of physical machines. We implemented CAVA on the top of the Hadoop and we showed that CAVA improves overall performance by 34.2% on average over multiple workloads compared to the original Hadoop.ope