1,263 research outputs found
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
Parallel Deferred Update Replication
Deferred update replication (DUR) is an established approach to implementing
highly efficient and available storage. While the throughput of read-only
transactions scales linearly with the number of deployed replicas in DUR, the
throughput of update transactions experiences limited improvements as replicas
are added. This paper presents Parallel Deferred Update Replication (P-DUR), a
variation of classical DUR that scales both read-only and update transactions
with the number of cores available in a replica. In addition to introducing the
new approach, we describe its full implementation and compare its performance
to classical DUR and to Berkeley DB, a well-known standalone database
Just-in-time Data Distribution for Analytical Query Processing
Distributed processing commonly requires data spread across machines using a
priori static or hash-based data allocation. In this paper, we explore
an alternative approach that starts from a master node in control of the
complete database, and a variable number of worker nodes for delegated
query processing. Data is shipped just-in-time to the worker nodes using
a need to know policy, and is being reused, if possible, in subsequent
queries. A bidding mechanism among the workers yields a scheduling with
the most efficient reuse of previously shipped data, minimizing the data
transfer costs.
Just-in-time data shipment allows our system to benefit from locally
available idle resources to boost overall performance. The system is
maintenance-free and allocation is fully transparent to users. Our
experiments show that the proposed adaptive distributed architecture is a
viable and flexible alternative for small scale MapReduce-type of
settings
Distributed Database Management Techniques for Wireless Sensor Networks
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Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9
Designing and Implementing a Distributed Database for a Small Multi-Outlet Business
Data is a fundamental and necessary element for businesses. During their operations they generate a certain amount of data that they need to capture, store, and later on retrieve when required. Databases provide the means to store and effectively retrieve data. Such a database can help a business improve its services, be more competitive, and ultimately increase its profits. In this paper, the system requirements of a distributed database are researched for a movie rental and sale store that has at least two outlets in different locations besides the main one. This project investigates the different stages of such a database, namely, the planning, analysis, decision, implementation and testing
A PROGRAMMING FRAMEWORK TO EASE DEVELOPMENT OF TIGHTLY-COUPLED CLOUD APPLICATIONS
Cloud application development is currently for professionals only. To make the cloud more accessible, cloud applications should ideally be easy to develop so that virtually anyone can develop their own cloud applications. However, they are difficult to develop, because they are essentially distributed systems, where the concurrent operations may take place, and reasoning about the behavior of concurrent operations to ensure correctness is not trivial. Additionally, programmers must consider failure handling, scalability, consistency, modularity, elasticity
A development framework for artificial intelligence based distributed operations support systems
Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself
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