10 research outputs found
Storage and aggregation for fast analytics systems
Computing in the last decade has been characterized by the rise of data- intensive scalable computing (DISC) systems. In particular, recent years have wit- nessed a rapid growth in the popularity of fast analytics systems. These systems exemplify a trend where queries that previously involved batch-processing (e.g., run- ning a MapReduce job) on a massive amount of data, are increasingly expected to be answered in near real-time with low latency. This dissertation addresses the problem that existing designs for various components used in the software stack for DISC sys- tems do not meet the requirements demanded by fast analytics applications. In this work, we focus specifically on two components:
1. Key-value storage: Recent work has focused primarily on supporting reads with high throughput and low latency. However, fast analytics applications require that new data entering the system (e.g., new web-pages crawled, currently trend- ing topics) be quickly made available to queries and analysis codes. This means that along with supporting reads efficiently, these systems must also support writes with high throughput, which current systems fail to do. In the first part of this work, we solve this problem by proposing a new key-value storage system – called the WriteBuffer (WB) Tree – that provides up to 30× higher write per- formance and similar read performance compared to current high-performance systems.
2. GroupBy-Aggregate: Fast analytics systems require support for fast, incre- mental aggregation of data for with low-latency access to results. Existing techniques are memory-inefficient and do not support incremental aggregation efficiently when aggregate data overflows to disk. In the second part of this dis- sertation, we propose a new data structure called the Compressed Buffer Tree (CBT) to implement memory-efficient in-memory aggregation. We also show how the WB Tree can be modified to support efficient disk-based aggregation.Ph.D
Towards Optimal Power Management: Estimation of Performance Degradation due to DVFS on Modern Processors
The alarming growth of the power consumption of data centers
coupled with low average utilization of servers suggests
the use of power management strategies. Such actions however
require the understanding of the effects of the power
management actions on the performance of data center applications
running on managed platforms. The goal of our
research is to accurately estimate power savings and consequent
performance degradation from DVFS and thereby
better guide the optimization of a performance/power metric
of a platform. Towards that end, this paper presents
precise performance and power models for DVFS strategies.
Precise models are attained by better modeling the
performance behavior of modern out-of-order processors, by
taking into account, for instance, the effects of cache miss
overlapping. Models are validated using benchmarks from
the SPEC CPU2006 suite, which show that the observed
degradation always falls within the predicted bounds. Also,
the upper bound degradation estimates were up to 43% less
than those due to a linear degradation model which allows
for the aggressive use of DVFS
Robust and Flexible Power-Proportional Storage (CMU-PDL-10-106)
Power-proportional cluster-based storage is an important component of an overall cloud computing infrastructure. With it, substantial subsets of nodes in the storage cluster can be turned off to save power during periods of low utilization. Rabbit is a distributed
file system that arranges its data-layout to provide ideal power-proportionality down to very low minimum number of powered-up
nodes (enough to store a primary replica of available datasets). Rabbit addresses the node failure rates of large-scale clusters with
data layouts that minimize the number of nodes that must be powered-up if a primary fails. Rabbit also allows different datasets to
use different subsets of nodes as a building block for interference avoidance when the infrastructure is shared by multiple tenants.
Experiments with a Rabbit prototype demonstrate its power-proportionality, and simulation experiments demonstrate its properties
at scale