A high-performance computing method for data allocation in distributed database systems

Hababeh, Ismail; Ramachandran, Muthu; Bowring, Nicholas

doi:10.1007/s11227-006-0001-8

Cited by 32 publications

(20 citation statements)

References 7 publications

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“…Optimal distribution architecture established by [6] 3.2 Adaptation of the logistic process A supply chain process consists of a set of tasks in a planned pattern or sequence (rout sheet). These tasks may correspond to manufacture operations, transport phases.…”

Section: Allocated Fragmentsmentioning

confidence: 99%

Key Factors for Information Dissemination on Communicating Products and Fixed Databases

Kubler

Derigent

Thomas

et al. 2012

Service Orientation in Holonic and Multi-Agent Manufacturing Control

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Intelligent products carrying their own information are more and more present nowadays. In recent years, some authors argued the usage of such products for the Supply Chain Management Industry. Indeed, a multitude of informational vectors take place in such environments like fixed databases or manufactured products on which we are able to embed significant proportion of data. By considering distributed database systems, we can allocate specific data fragments to the product useful to manage its own evolution. The paper aims to analyze the Supply Chain performance according to different strategies of information distribution. Thus, different distribution patterns between informational vectors are studied. The purpose is to determine the key factors which lead to improve information distribution performance in term of time properties.

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Section: Allocated Fragmentsmentioning

confidence: 99%

Key Factors for Information Dissemination on Communicating Products and Fixed Databases

Kubler

Derigent

Thomas

et al. 2012

Service Orientation in Holonic and Multi-Agent Manufacturing Control

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“…Reference [4] presents a system for allocating fragments of databases, to provide high-bandwidth, high-parallelism and highly available access, and processing. The sites are grouped by communication speed, and fragments are allocated in a way that reduces the computational cost of the potential access to the data.…”

Section: Grid Storage and Computationmentioning

confidence: 99%

Efficient allocation and composition of distributed storage

2008

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In this paper, we investigate the composition of cheap network storage resources to meet specific availability and capacity requirements. We show that the problem of finding the optimal composition for availability and price requirements can be reduced to the knapsack problem, and propose three techniques for efficiently finding approximate solutions. The first algorithm uses a dynamic programming approach to find mirrored storage resources for high availability requirements, and runs in the pseudo-polynomial O(n 2 c) time where n is the number of sellers' resources to choose from and c is a capacity function of the requested and minimum availability. The second technique is a heuristic which finds resources to be agglomerated into a larger coherent resource, with complexity of O(n log n). The third technique finds a compromise between capacity and availability (which in our phrasing is a complex integer programming problem) using a genetic algorithm. The algorithms can be implemented on a broker that intermediates between buyers and sellers of storage resources. Finally, we show that a broker in an open storage market, using the combination of the three algorithms can more frequently meet user requests and lower the cost of requests that are met compared to a broker that simply matches single resources to requests.

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“…Hababeh et al, [3] developed a method for grouping the distributed sites into clusters and customizing the database fragments allocation to the clusters and their sites. The method proceeds in three steps: (i) it groups the computing sites into disjoint clusters, (ii) it allocates the fragments to the clusters, and (iii) it allocates the fragments within each cluster to its sites independently of the other clusters and sites.…”

Section: Introductionmentioning

confidence: 99%

“…The method proceeds in three steps: (i) it groups the computing sites into disjoint clusters, (ii) it allocates the fragments to the clusters, and (iii) it allocates the fragments within each cluster to its sites independently of the other clusters and sites. Hababeh et al, [3] reported results of computational tests using a database system with 6 sites and 8 fragments.…”

Section: Introductionmentioning

confidence: 99%