Data replication in Mariposa

Sidell, Jeff; Aoki, Paul M.; Sah, Adam; Staelin, Carl; Yu, Aihong

doi:10.1109/icde.1996.492198

Cited by 50 publications

(35 citation statements)

References 15 publications

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“…There are methods that do only fragmentation [1,24,26,33,34] and methods that do only allocation of predefined fragments [3,4,7,10,13,20,30]. Some methods also exist that integrate both tasks [9,11,17,19,25,27,29]. Replication, however, is typically done as a separate task [5,8,15,21,22,32], although some methods, like ours, take an integral view of fragmentation, allocation and replication [11,27,29].…”

Section: Related Workmentioning

confidence: 99%

“…Some methods also exist that integrate both tasks [9,11,17,19,25,27,29]. Replication, however, is typically done as a separate task [5,8,15,21,22,32], although some methods, like ours, take an integral view of fragmentation, allocation and replication [11,27,29]. Dynamic replication algorithms [5,15,21,22,32] can optimize for different measures, but we believe that refragmentation and reallocation must be considered as alternatives to replication.…”

Section: Related Workmentioning

confidence: 99%

“…Mariposa [27,28] is a notable exception to the traditional, manually fragmented systems. It provides refragmentation, reallocation and replication based on a bidding protocol.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

DYFRAM: dynamic fragmentation and replica management in distributed database systems

Hauglid

Ryeng

Nørvåg

2010

Distrib Parallel Databases

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In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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DYFRAM: dynamic fragmentation and replica management in distributed database systems

Hauglid

Ryeng

Nørvåg

2010

Distrib Parallel Databases

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“…Moreover, indexes, constructed in the cache, accelerate the queries. Future work includes the expansion of the infrastructure with caching of materialized views, similar to [25], or partial columns, similar to [21]. Furthermore, additional CPUs are employed to speed up queries.…”

Section: Cost Of Structuresmentioning

confidence: 99%

An Economic Model for Self-Tuned Cloud Caching

Dash

Kantere

Ailamaki

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-Cloud computing, the new trend for service infrastructures requires user multi-tenancy as well as minimal capital expenditure. In a cloud that services large amounts of data that are massively collected and queried, such as scientific data, users typically pay for query services. The cloud supports caching of data in order to provide quality query services. User payments cover query execution costs and maintenance of cloud infrastructure, and incur cloud profit. The challenge resides in providing efficient and resource-economic query services while maintaining a profitable cloud. In this work we propose an economic model for self-tuned cloud caching targeting the service of scientific data. The proposed economy is adapted to policies that encourage high-quality individual and overall query services but also brace the profit of the cloud. We propose a cost model that takes into account all possible query and infrastructure expenditure. The experimental study proves that the proposed solution is viable for a variety of workloads and data.

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“…This market mechanism leads to an equitable temperature distribution in the system 135]. Other domains where market mechanisms were successfully applied include purchasing memory in an operating systems 53], allocating virtual circuits 89], \stealing" unused CPU cycles in a network of computers 79,269], predicting option futures in nancial markets 214], and numerous scheduling and distributed resource allocation problems 159,165,245,255,275,276].…”

Section: Computational Economicsmentioning

confidence: 99%

Theory of Collective Intelligence

Wolpert

2004

Collectives and the Design of Complex Systems

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This paper surveys the emerging science of how to design a \COllective INtelligence" (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized, personalized communication and/or control. ii) There is a provided world utility function that rates the possible histories of the full system.In particular, we a r e i n terested in COINs in which e a c h a g e n t runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they \cooperate" as far as the world utility i s concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we w i s h t o s o l v e the COIN design problem implicitly, via the \adaptive" character of the RL algorithms of each of the agents. This approach i n troduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we d o n o t have phenomena like t h e tragedy of the commons, Braess's paradox, or the liquidity trap? Although still very young, research speci cally concentrating on the COIN design problem has already resulted in successes in arti cial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scienti c elds, such as economics, game theory, and population biology.

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Data replication in Mariposa

Cited by 50 publications

References 15 publications

DYFRAM: dynamic fragmentation and replica management in distributed database systems

DYFRAM: dynamic fragmentation and replica management in distributed database systems

An Economic Model for Self-Tuned Cloud Caching

Theory of Collective Intelligence

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