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We present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. The follower must solve an optimization problem that has been previously perturbed by means of a series of attacking actions led by the attacker. These attacking actions aim at augmenting the cost of the decision variables of the follower’s optimization problem. The objective, from the attacker’s viewpoint, is that of choosing an attacking strategy that reduces as much as possible the quality of the optimal solution attainable by the follower. The progressive approximation mechanism consists of the iterative solution of an interdiction problem in which the attacker actions are restricted to a subset of the whole solution space and a pricing subproblem invoked with the objective of proving the optimality of the attacking strategy. This scheme is especially useful when the optimal solutions to the follower’s subproblem intersect with the decision space of the attacker only in a small number of decision variables. In such cases, the progressive approximation method can solve interdiction games otherwise intractable for classical methods. We illustrate the efficiency of our approach on the shortest path, 0-1 knapsack and facility location interdiction games. Summary of Contribution: In this article, we present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. We exploit the discrete nature of this interdiction game to design an effective algorithmic framework that improves the performance of general-purpose solvers. Our algorithm combines elements from mathematical programming and computer science, including a metaheuristic algorithm, a binary search procedure, a cutting-planes algorithm, and supervalid inequalities. Although we illustrate our results on three specific problems (shortest path, 0-1 knapsack, and facility location), our algorithmic framework can be extended to a broader class of interdiction problems.
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Automated Configuration for Agile Software Environments
The increasing use of the DevOps paradigm in software systems has substantially increased the frequency of configuration parameter setting changes. Ensuring the correctness of such settings is generally a very challenging problem due to the complex interdependencies, and calls for an automated mechanism that can both run quickly and provide accurate settings. In this paper, we propose an efficient discrete combinatorial optimization technique that makes two unique contributions: (a) an improved and extended metaheuristic that exploits the application domain knowledge for fast convergence, and (b) the development and quantification of a discrete version of the classical tunneling mechanism to improve the accuracy of the solution. Our extensive evaluation using available workload traces that do include configuration information shows that the proposed technique can provide a lower-cost solution (by ~60%) with faster convergence (by ~48%) as compared to the traditional metaheuristic algorithms. Also, our solution succeeds in finding a feasible solution in approximately 30% more cases than the baseline algorithm.
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- Award ID(s):
- 2011252
- PAR ID:
- 10419598
- Date Published:
- Journal Name:
- IEEE Cloud
- Page Range / eLocation ID:
- 511 to 521
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CLOUD55607.2022.00074
