Task Allocation for Maximizing Reliability of Distributed Computer Systems
IEEE Transactions on Computers
Replicated Process Allocation for Load Distribution in Fault-Tolerant Multicomputers
IEEE Transactions on Computers
Replication management using the state-machine approach
Distributed systems (2nd Ed.)
Distributed systems (2nd Ed.)
The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
The costs and limits of availability for replicated services
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Dynamic Multi-Resource Load Balancing in Parallel Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Multimedia Systems
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DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
UNIT: User-centric Transaction Management in Web-Database Systems
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Query optimization in distributed networks of autonomous database systems
ACM Transactions on Database Systems (TODS)
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A New Era of Performance Evaluation
Computer
Query processing over incomplete autonomous databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fault-tolerance in the borealis distributed stream processing system
ACM Transactions on Database Systems (TODS)
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The VLDB Journal — The International Journal on Very Large Data Bases
A probabilistic approach for fault tolerant multiprocessor real-time scheduling
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
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In large-scale Internet-based distributed systems, participants (consumers and providers) are typically autonomous, i.e. they may have special interests towards queries and other participants. In this context, a way to avoid a participant to voluntarily leave the system is satisfying its interests when allocating queries. However, participants satisfaction may also be negatively affected by the failures of other participants. Query replication is a solution to deal with providers failures, but, it is challenging because of autonomy: it cannot only quickly overload the system, but also it can dissatisfy participants with uninteresting queries. Thus, a natural question arises: should queries be replicated? If so, which ones? and how many times? In this paper, we answer these questions by revisiting query replication from a satisfaction and probabilistic point of view. We propose a new algorithm, called S b QR, that decides on-the-fly whether a query should be replicated and at which rate. As replicating a large number of queries might overload the system, we propose a variant of our algorithm, called S b QR+. The idea is to voluntarily fail to allocate as many replicas as required by consumers for low critical queries so as to keep resources for high critical queries during query-intensive periods. Our experimental results demonstrate that our algorithms significantly outperform the baseline algorithms from both the performance and satisfaction points of view. We also show that our algorithms automatically adapt to the criticality of queries and different rates of participant failures.