Database migration is a ubiquitous need faced by enterprises that generate and use vast amounts of data. This is because of database software updates, or it is from changes to hardware, project standards, and other business factors. Migrating a large collection of databases is a way more challenging task than migrating a single database because of the presence of additional constraints. These constraints include capacities of shifts and sizes of databases. 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. These parameters are the size of each database, the size of each shift, and the cost of testing each application. Each of these parameters can be either constant or arbitrary. Additionally, the cost of testing an application can be proportional to the number of databases that the application uses. 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. We also provide approximability and inapproximability results as well as lower bounds for the running time of any exact algorithm for the database migration problem. We show that the database migration problem is equivalent to a variation of the classical hypergraph partitioning problem. Our theoretical results also imply new theoretical results for the hypergraph partitioning problem that are interesting in their own right. Finally, we adapt heuristic algorithms devised for the hypergraph partitioning problem to the database migration problem, and we also give experimental results for the adapted heuristics. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: B. Caskurlu and U. U. Acikalin are supported by The Scientific and Technological Research Council of Türkiye [Grant 122E599]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0021 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0021 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Abstract In a strategic-form game, a strategy profile is an equilibrium if no viable coalition of agents (or players) benefits (in the Pareto sense) from jointly changing their strategies. Weaker or stronger equilibrium notions can be defined by considering various restrictions on coalition formation. For instance, in a Nash equilibrium, it is assumed that viable coalitions are singletons, and in a super strong equilibrium, it is assumed that every coalition is viable. Restrictions on coalition formation can be justified by communication limitations, coordination problems, or institutional constraints. In this paper, inspired by social structures in various real-life scenarios, we introduce certain restrictions on coalition formation, and on their basis, we introduce a number of equilibrium notions. As an application, we study our equilibrium notions in resource selection games (RSGs), and we present a complete set of existence and nonexistence results for general RSGs and their important special cases.

In this paper, we study two closely related problems on bipartite graphs, viz., the partial vertex cover problem and the budgeted maximum coverage problem. Both these problems arise in a number of different application domains, including, but not limited to, computer security and transportation logistics. It is well known that the vertex cover problem is solvable in polynomial time on bipartite graphs. However, the computational complexity of the partial vertex cover problem on bipartite graphs was open, thus far. In this paper, we establish that the partial vertex cover problem is \bf NP-hard, even on bipartite graphs. Our result also establishes that the closely related budgeted maximum coverage problem is \bf NP-hard on bipartite graphs. For the latter problem, we present an $\frac{8}{9}$-approximation algorithm. Our approximation guarantee matches and resolves the integrality gap of the natural linear programming relaxation for this problem and improves upon a recent $\frac{4}{5}$-approximation algorithm for the same problem.

In this paper, we formulate and study a capacity allocation game between a set of receivers (players) that are interested in receiving multicast data (video/multimedia) being streamed from a server through a multihop network. We consider fractional multicast streaming, where the multicast stream from the source (origin-server) to any particular receiver (end-user) can be split over multiple paths. The receivers are selfish and noncooperative, but must collaboratively purchase capacities of links in the network, as necessary for delivery of the multicast stream from the source to the individual receivers, assuming that the multicast stream is network-coded. For this multicast capacity allocation (network formation) game, we show that the Nash equilibrium is guaranteed to exist in general. For a 2-tier network model where the receivers must obtain the multicast data from the source through a set of relay nodes, we show that the price of stability is at most 2, and provide a polynomial-time algorithm that computes a Nash equilibrium whose social cost is within a factor of 2 of the socially optimum solution. For more general network models, we show that there exists a 2-approximate Nash equilibrium, whose cost is at most two times the social optimum. We also give a polynomial-time algorithm that computes a (2+∈)-approximate Nash equilibrium for any ∈ > 0, whose cost is at most two times the social optimum. Simulation studies show that our algorithms generate efficient Nash equilibrium allocation solutions for a vast majority of randomly generated network topologies.