1,598 research outputs found
Rethinking State-Machine Replication for Parallelism
State-machine replication, a fundamental approach to designing fault-tolerant
services, requires commands to be executed in the same order by all replicas.
Moreover, command execution must be deterministic: each replica must produce
the same output upon executing the same sequence of commands. These
requirements usually result in single-threaded replicas, which hinders service
performance. This paper introduces Parallel State-Machine Replication (P-SMR),
a new approach to parallelism in state-machine replication. P-SMR scales better
than previous proposals since no component plays a centralizing role in the
execution of independent commands---those that can be executed concurrently, as
defined by the service. The paper introduces P-SMR, describes a "commodified
architecture" to implement it, and compares its performance to other proposals
using a key-value store and a networked file system
Optimistic Parallel State-Machine Replication
State-machine replication, a fundamental approach to fault tolerance,
requires replicas to execute commands deterministically, which usually results
in sequential execution of commands. Sequential execution limits performance
and underuses servers, which are increasingly parallel (i.e., multicore). To
narrow the gap between state-machine replication requirements and the
characteristics of modern servers, researchers have recently come up with
alternative execution models. This paper surveys existing approaches to
parallel state-machine replication and proposes a novel optimistic protocol
that inherits the scalable features of previous techniques. Using a replicated
B+-tree service, we demonstrate in the paper that our protocol outperforms the
most efficient techniques by a factor of 2.4 times
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
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
The End of Slow Networks: It's Time for a Redesign
Next generation high-performance RDMA-capable networks will require a
fundamental rethinking of the design and architecture of modern distributed
DBMSs. These systems are commonly designed and optimized under the assumption
that the network is the bottleneck: the network is slow and "thin", and thus
needs to be avoided as much as possible. Yet this assumption no longer holds
true. With InfiniBand FDR 4x, the bandwidth available to transfer data across
network is in the same ballpark as the bandwidth of one memory channel, and it
increases even further with the most recent EDR standard. Moreover, with the
increasing advances of RDMA, the latency improves similarly fast. In this
paper, we first argue that the "old" distributed database design is not capable
of taking full advantage of the network. Second, we propose architectural
redesigns for OLTP, OLAP and advanced analytical frameworks to take better
advantage of the improved bandwidth, latency and RDMA capabilities. Finally,
for each of the workload categories, we show that remarkable performance
improvements can be achieved
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