117,152 research outputs found
DISTRIBUTED MULTIDIMENSIONAL INDEXING FOR SCIENTIFIC DATA ANALYSIS APPLICATIONS
Scientific data analysis applications require large scale computing power to
effectively service client queries and also require large storage repositories
for datasets that are generated continually from sensors and simulations.
These scientific datasets are growing in size every day, and are becoming truly
enormous. The goal of this dissertation is to provide efficient multidimensional
indexing techniques that aid in navigating distributed scientific datasets.
In this dissertation, we show significant improvements in accessing
distributed large scientific datasets.
The first approach we took to improve access to subsets of large
multidimensional scientific datasets, was data chunking. The contents of
scientific data files typically are a collection of multidimensional arrays,
along with the corresponding metadata. Data chunking groups data elements into
small chunks of a fixed, but data-specific, size to take advantage of
spatio-temporal locality since it is not efficient to index individual data
elements of large scientific datasets.
The second approach was the design of an efficient multidimensional index for
scientific datasets. This work investigates how existing multidimensional
indexing structures perform on chunked scientific datasets, and compares their
performance with that of our own indexing structure, SH-trees. Since R-trees
were proposed, various multidimensional indexing structures have been proposed.
However, there are a relatively small number of studies focused on improving
the performance of indexing geographically distributed datasets, especially
across heterogeneous machines. As a third approach, in an attempt to
accelerate indexing performance for distributed datasets, we proposed several
distributed multidimensional indexing schemes: replicated centralized indexing,
hierarchical two level indexing, and decentralized two level indexing.
Our experimental results show that great performance improvements
are gained from distribution of multidimensional index. However, the design
choices for distributed indexing, such as replication, partitioning, and
decentralization, must be carefully considered since they may decrease the overall
performance in certain situations. Therefore, this work provides performance
guidelines to aid in selecting the best distributed multidimensional indexing
scheme for various systems and applications. Finally, we describe how a
distributed multidimensional indexing scheme can be used by a distributed
multiple query optimization middleware as a case-study application to
generate better query plans by leveraging information about the contents of
remote caches
Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme
This paper addresses the problem of efficiently storing and accessing massive
data blocks in a large-scale distributed environment, while providing efficient
fine-grain access to data subsets. This issue is crucial in the context of
applications in the field of databases, data mining and multimedia. We propose
a data sharing service based on distributed, RAM-based storage of data, while
leveraging a DHT-based, natively parallel metadata management scheme. As
opposed to the most commonly used grid storage infrastructures that provide
mechanisms for explicit data localization and transfer, we provide a
transparent access model, where data are accessed through global identifiers.
Our proposal has been validated through a prototype implementation whose
preliminary evaluation provides promising results
Simplified Distributed Programming with Micro Objects
Developing large-scale distributed applications can be a daunting task.
object-based environments have attempted to alleviate problems by providing
distributed objects that look like local objects. We advocate that this
approach has actually only made matters worse, as the developer needs to be
aware of many intricate internal details in order to adequately handle partial
failures. The result is an increase of application complexity. We present an
alternative in which distribution transparency is lessened in favor of clearer
semantics. In particular, we argue that a developer should always be offered
the unambiguous semantics of local objects, and that distribution comes from
copying those objects to where they are needed. We claim that it is often
sufficient to provide only small, immutable objects, along with facilities to
group objects into clusters.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499
Incremental Consistency Guarantees for Replicated Objects
Programming with replicated objects is difficult. Developers must face the
fundamental trade-off between consistency and performance head on, while
struggling with the complexity of distributed storage stacks. We introduce
Correctables, a novel abstraction that hides most of this complexity, allowing
developers to focus on the task of balancing consistency and performance. To
aid developers with this task, Correctables provide incremental consistency
guarantees, which capture successive refinements on the result of an ongoing
operation on a replicated object. In short, applications receive both a
preliminary---fast, possibly inconsistent---result, as well as a
final---consistent---result that arrives later.
We show how to leverage incremental consistency guarantees by speculating on
preliminary values, trading throughput and bandwidth for improved latency. We
experiment with two popular storage systems (Cassandra and ZooKeeper) and three
applications: a Twissandra-based microblogging service, an ad serving system,
and a ticket selling system. Our evaluation on the Amazon EC2 platform with
YCSB workloads A, B, and C shows that we can reduce the latency of strongly
consistent operations by up to 40% (from 100ms to 60ms) at little cost (10%
bandwidth increase, 6% throughput drop) in the ad system. Even if the
preliminary result is frequently inconsistent (25% of accesses), incremental
consistency incurs a bandwidth overhead of only 27%.Comment: 16 total pages, 12 figures. OSDI'16 (to appear
Partout: A Distributed Engine for Efficient RDF Processing
The increasing interest in Semantic Web technologies has led not only to a
rapid growth of semantic data on the Web but also to an increasing number of
backend applications with already more than a trillion triples in some cases.
Confronted with such huge amounts of data and the future growth, existing
state-of-the-art systems for storing RDF and processing SPARQL queries are no
longer sufficient. In this paper, we introduce Partout, a distributed engine
for efficient RDF processing in a cluster of machines. We propose an effective
approach for fragmenting RDF data sets based on a query log, allocating the
fragments to nodes in a cluster, and finding the optimal configuration. Partout
can efficiently handle updates and its query optimizer produces efficient query
execution plans for ad-hoc SPARQL queries. Our experiments show the superiority
of our approach to state-of-the-art approaches for partitioning and distributed
SPARQL query processing
- …