5,135 research outputs found
Dynamic Physiological Partitioning on a Shared-nothing Database Cluster
Traditional DBMS servers are usually over-provisioned for most of their daily
workloads and, because they do not show good-enough energy proportionality,
waste a lot of energy while underutilized. A cluster of small (wimpy) servers,
where its size can be dynamically adjusted to the current workload, offers
better energy characteristics for these workloads. Yet, data migration,
necessary to balance utilization among the nodes, is a non-trivial and
time-consuming task that may consume the energy saved. For this reason, a
sophisticated and easy to adjust partitioning scheme fostering dynamic
reorganization is needed. In this paper, we adapt a technique originally
created for SMP systems, called physiological partitioning, to distribute data
among nodes, that allows to easily repartition data without interrupting
transactions. We dynamically partition DB tables based on the nodes'
utilization and given energy constraints and compare our approach with physical
partitioning and logical partitioning methods. To quantify possible energy
saving and its conceivable drawback on query runtimes, we evaluate our
implementation on an experimental cluster and compare the results w.r.t.
performance and energy consumption. Depending on the workload, we can
substantially save energy without sacrificing too much performance
LogBase: A Scalable Log-structured Database System in the Cloud
Numerous applications such as financial transactions (e.g., stock trading)
are write-heavy in nature. The shift from reads to writes in web applications
has also been accelerating in recent years. Write-ahead-logging is a common
approach for providing recovery capability while improving performance in most
storage systems. However, the separation of log and application data incurs
write overheads observed in write-heavy environments and hence adversely
affects the write throughput and recovery time in the system. In this paper, we
introduce LogBase - a scalable log-structured database system that adopts
log-only storage for removing the write bottleneck and supporting fast system
recovery. LogBase is designed to be dynamically deployed on commodity clusters
to take advantage of elastic scaling property of cloud environments. LogBase
provides in-memory multiversion indexes for supporting efficient access to data
maintained in the log. LogBase also supports transactions that bundle read and
write operations spanning across multiple records. We implemented the proposed
system and compared it with HBase and a disk-based log-structured
record-oriented system modeled after RAMCloud. The experimental results show
that LogBase is able to provide sustained write throughput, efficient data
access out of the cache, and effective system recovery.Comment: VLDB201
GraphH: High Performance Big Graph Analytics in Small Clusters
It is common for real-world applications to analyze big graphs using
distributed graph processing systems. Popular in-memory systems require an
enormous amount of resources to handle big graphs. While several out-of-core
approaches have been proposed for processing big graphs on disk, the high disk
I/O overhead could significantly reduce performance. In this paper, we propose
GraphH to enable high-performance big graph analytics in small clusters.
Specifically, we design a two-stage graph partition scheme to evenly divide the
input graph into partitions, and propose a GAB (Gather-Apply-Broadcast)
computation model to make each worker process a partition in memory at a time.
We use an edge cache mechanism to reduce the disk I/O overhead, and design a
hybrid strategy to improve the communication performance. GraphH can
efficiently process big graphs in small clusters or even a single commodity
server. Extensive evaluations have shown that GraphH could be up to 7.8x faster
compared to popular in-memory systems, such as Pregel+ and PowerGraph when
processing generic graphs, and more than 100x faster than recently proposed
out-of-core systems, such as GraphD and Chaos when processing big graphs
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