N. Krishnakumar and A. Bernstein. Bounded ignorance: A technique for increasing concurrency in replicated system. ACM Transaction on Data Base Systems, 19(4):586 -- 625, Feb. 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and start from scratch; The algorithm takes (practically) infinite amount of time to finish after a certain number of download cycles. In contrast, the complexity of our samplingbased algorithms stay the same over time. A lot of work has been done to maintain the consistency of replicated data [3, 1, 12, 16, 17]. This work studies the tradeoff between data consistency and read write performance. In most of the existing work, however, researchers have assumed a push model, where the sources notify the replicated data of the updates. For example, Olston et al. 18] proposed a new architecture in which data ....

N. Krishnakumar and A. Bernstein. Bounded ignorance: A technique for increasing concurrency in a replicated system. ACM TODS, 19(4), December 1994.

....replicated database systems, several approaches that relax the strict serializability guarantee have been proposed. The goal of these e orts has been to decrease the contention between user transactions, and hence to increase the concurrency and decrease the abort rate. Krishnakumar and Bernstein [23] suggest N bounded ignorance, a relaxed consistency condition that permits N 1 con icting transactions to be performed concurrently. Pu and Le [38] propose epsilon serializability, another relaxed consistency condition, that allows querytransactions (containing read operations only) to overlap ....

N. Krishnakumar and A. J. Bernstein. Bounded ignorance: A technique for increasing concurrency in a replicated system. ACM Transactions on Database Systems, 19(4):586-625, December 1994.

....the number or severity of conflicting updates. Thus, application developers are forced to make a binary decision between strong and optimistic consistency models. Ateach of these two extremes, there is an associated tradeoff between consistency, performance, and availability. Anumber of efforts [1, 22, 32, 36] argue for the benefits of a continuous consistency model. Here, optimistic and strong consistency are two extremes of a more general spectrum enabling applications to dynamically specify their availability consistency requirements based on changing client, network, and service characteristics. A ....

....between consistency and availability in the context of a clusterbased Internet service. Relative to their efforts, we focus on wide area service availability and are able to quantify service availability as a function of consistency. Anumber of other efforts explore continuous consistency models [1, 22, 32]. We believe our methodology and results are generally applicable to a broad range of consistency models [38] Availability of a replication system is also related to the availability of distributed consensus protocols. Relative to protocols [25] for qualitatively increasing the availability of ....

N. Krishnakumar and A. Bernstein. Bounded Ignorance: A Technique for Increasing Concurrency in a Replicated System. ACM Transactions on Database Systems, 19(4), December 1994.

....Protocols Natural Easy to Use API Weight Spec. Conit based Continuous Consistency Model Conit Theory Practicality Generality Figure 1. How the conit based consistency model achieves two typically conflicting goals. In the context of traditional replicated databases, much research [2, 3, 4, 8, 9, 10, 18, 19, 20, 24, 25, 26, 30, 31, 32] has been performed on relaxed consistency models. However, such traditional models typically achieve only one of generality and practicality. Some of the consistency models [2, 19, 20, 31] are general enough to allow a wide range of applications to express their consistency semantics. However, ....

....range of applications to express their consistency semantics. However, they provide no practical, efficient, applicationindependent protocols to enforce the model and no natural API for application programmers, thus failing to meet the practicality requirement. Other relaxed consistency models [3, 4, 8, 9, 10, 18, 24, 25, 26, 30, 32] have easy to use interfaces and can be efficiently implemented, but they typically address the consistency requirements of a specific class of applications. In this paper, we propose a conit based continuous consistency model for wide area data replication to simultaneously achieve generality ....

[Article contains additional citation context not shown here]

N. Krishnakumar and A. Bernstein. Bounded Ignorance: A Technique for Increasing Concurrency in a Replicated System. ACM Transactions on Database Systems, 19(4), December 1994.

....Protocols Natural Easy to Use API Weight Spec. Conit based Continuous Consistency Model Conit Theory Practicality Generality Figure 1. How the conit based consistency model achieves two typically conflicting goals. In the context of traditional replicated databases, much research [2, 3, 4, 7, 8, 9, 15, 16, 17, 20, 21, 22, 25, 26] has been performed on relaxed consistency models. However, such traditional models typically achieve only one of generality and practicality. Some of the consistency models [2, 16, 17, 26] are general enough to allow a wide range of applications to express their consistency semantics. However, ....

....range of applications to express their consistency semantics. However, they provide no practical, efficient, applicationindependent protocols to enforce the model and no natural API for application programmers, thus failing to meet the practicality requirement. Other relaxed consistency models [3, 4, 7, 8, 9, 15, 20, 21, 22, 25] have easy to use interfaces and can be efficiently implemented, but they typically address the consistency requirements of a specific class of applications. In this paper, we propose a conit based continuous consistency model for wide area data replication to simultaneously achieve generality ....

[Article contains additional citation context not shown here]

N. Krishnakumar and A. Bernstein. Bounded Ignorance: A Technique for Increasing Concurrency in a Replicated System. ACM Transactions on Database Systems, 19(4), December 1994.

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N. Krishnakumar and A. Bernstein. Bounded ignorance: A technique for increasing concurrency in replicated system. ACM Transaction on Data Base Systems, 19(4):586 -- 625, Feb. 1994.

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N. Krishnakumar and A. Bernstein. Bounded ignorance: A technique for increasing concurrency in replicated system. ACM Transaction on Data Base Systems, 19(4):586 -- 625, Feb. 1994.

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N. Krishnakumar and A. J. Bernstein. Bounded ignorance: A technique for increasing concurrency in a replicated system. ACM Trans. on Database Sys., 19(4):586--625, December 1994.

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N. Krishnakumar and A. J. Bernstein. Bounded ignorance: A technique for increasing concurrency in a replicated system. ACM Trans. on Database Sys., 19(4):586--625, December 1994.

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Krishnakumar, N., Bernstein, A.J.: Bounded ignorance: A technique for increasing concurrency in a replicated system. ACM Trans. on Database Sys. 19 (1994) 586{ 625

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N. Krishnakumar and A. Bernstein. Bounded ignorance: A technique for increasing concurrency in a replicated system. ACM Transactions on Data Base Systems, 19(4), Dec. 1994.

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