6,369 research outputs found
Measuring and Managing Answer Quality for Online Data-Intensive Services
Online data-intensive services parallelize query execution across distributed
software components. Interactive response time is a priority, so online query
executions return answers without waiting for slow running components to
finish. However, data from these slow components could lead to better answers.
We propose Ubora, an approach to measure the effect of slow running components
on the quality of answers. Ubora randomly samples online queries and executes
them twice. The first execution elides data from slow components and provides
fast online answers; the second execution waits for all components to complete.
Ubora uses memoization to speed up mature executions by replaying network
messages exchanged between components. Our systems-level implementation works
for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the
EasyRec Recommendation Engine, and the OpenEphyra question answering system.
Ubora computes answer quality much faster than competing approaches that do not
use memoization. With Ubora, we show that answer quality can and should be used
to guide online admission control. Our adaptive controller processed 37% more
queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor
A Big Data Analyzer for Large Trace Logs
Current generation of Internet-based services are typically hosted on large
data centers that take the form of warehouse-size structures housing tens of
thousands of servers. Continued availability of a modern data center is the
result of a complex orchestration among many internal and external actors
including computing hardware, multiple layers of intricate software, networking
and storage devices, electrical power and cooling plants. During the course of
their operation, many of these components produce large amounts of data in the
form of event and error logs that are essential not only for identifying and
resolving problems but also for improving data center efficiency and
management. Most of these activities would benefit significantly from data
analytics techniques to exploit hidden statistical patterns and correlations
that may be present in the data. The sheer volume of data to be analyzed makes
uncovering these correlations and patterns a challenging task. This paper
presents BiDAl, a prototype Java tool for log-data analysis that incorporates
several Big Data technologies in order to simplify the task of extracting
information from data traces produced by large clusters and server farms. BiDAl
provides the user with several analysis languages (SQL, R and Hadoop MapReduce)
and storage backends (HDFS and SQLite) that can be freely mixed and matched so
that a custom tool for a specific task can be easily constructed. BiDAl has a
modular architecture so that it can be extended with other backends and
analysis languages in the future. In this paper we present the design of BiDAl
and describe our experience using it to analyze publicly-available traces from
Google data clusters, with the goal of building a realistic model of a complex
data center.Comment: 26 pages, 10 figure
Database Workload Management (Dagstuhl Seminar 12282)
This report documents the program and the outcomes of Dagstuhl Seminar 12282 "Database Workload Management". Dagstuhl Seminar 12282 was designed to
provide a venue where researchers can engage in dialogue with industrial
participants for an in-depth exploration of challenging industrial
workloads, where industrial participants can challenge researchers to
apply the lessons-learned from their large-scale experiments to multiple
real systems, and that would facilitate the release of real workloads that can be used to drive future research, and concrete measures to evaluate and compare workload management techniques in the context of these workloads
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