Abstract
Database migration is an ubiquitous need faced by enterprises that generate and use vast amount of data. This is due to database software updates, or from changes to hardware, project standards, and other business factors [1]. Migrating a large collection of databases is a way more challenging task than migrating a single database, due to the presence of additional constraints. These constraints include capacities of shifts, sizes of databases, and timing relationships. In this paper, we present a comprehensive framework that can be used to model database migration problems of different enterprises with customized constraints, by appropriately instantiating the parameters of the framework. We establish the computational complexities of a number of instantiations of this framework. We present fixed-parameter intractability results for various relevant parameters of the database migration problem. Finally, we discuss a randomized approximation algorithm for an interesting instantiation.
K. Subramani—This research was supported in part by the Air Force Research Laboratory Information Directorate, through the Air Force Office of Scientific Research Summer Faculty Fellowship Program and the Information Institute®, contract numbers FA8750-16-3-6003 and FA9550-15-F-0001.
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Subramani, K., Caskurlu, B., Velasquez, A. (2019). Minimization of Testing Costs in Capacity-Constrained Database Migration. In: Disser, Y., Verykios, V. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2018. Lecture Notes in Computer Science(), vol 11409. Springer, Cham. https://doi.org/10.1007/978-3-030-19759-9_1
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