3,311 research outputs found
Optimum Algorithms for a Model of Direct Chaining
Direct chaining is a popular and efficient class of hashing algorithms. In this paper we study
optimum algorithms among direct chaining methods, under the restrictions that the records in the hash table are not moved after they are inserted, that for each chain the relative ordering of the records in the chain does not change after more insertions, and that only one link field is used per table slot. The varied-insertion coalesced hashing method (VICH), which is proposed and analyzed in [CV84], is conjectured to be optimum
among all direct chaining algorithms in this class. We give strong evidence in favor of the conjecture by showing that VICH is optimum under fairly general conditions
Optimal Orchestration of Virtual Network Functions
-The emergence of Network Functions Virtualization (NFV) is bringing a set of
novel algorithmic challenges in the operation of communication networks. NFV
introduces volatility in the management of network functions, which can be
dynamically orchestrated, i.e., placed, resized, etc. Virtual Network Functions
(VNFs) can belong to VNF chains, where nodes in a chain can serve multiple
demands coming from the network edges. In this paper, we formally define the
VNF placement and routing (VNF-PR) problem, proposing a versatile linear
programming formulation that is able to accommodate specific features and
constraints of NFV infrastructures, and that is substantially different from
existing virtual network embedding formulations in the state of the art. We
also design a math-heuristic able to scale with multiple objectives and large
instances. By extensive simulations, we draw conclusions on the trade-off
achievable between classical traffic engineering (TE) and NFV infrastructure
efficiency goals, evaluating both Internet access and Virtual Private Network
(VPN) demands. We do also quantitatively compare the performance of our VNF-PR
heuristic with the classical Virtual Network Embedding (VNE) approach proposed
for NFV orchestration, showing the computational differences, and how our
approach can provide a more stable and closer-to-optimum solution
Algorithm Portfolios for Noisy Optimization
Noisy optimization is the optimization of objective functions corrupted by
noise. A portfolio of solvers is a set of solvers equipped with an algorithm
selection tool for distributing the computational power among them. Portfolios
are widely and successfully used in combinatorial optimization. In this work,
we study portfolios of noisy optimization solvers. We obtain mathematically
proved performance (in the sense that the portfolio performs nearly as well as
the best of its solvers) by an ad hoc portfolio algorithm dedicated to noisy
optimization. A somehow surprising result is that it is better to compare
solvers with some lag, i.e., propose the current recommendation of best solver
based on their performance earlier in the run. An additional finding is a
principled method for distributing the computational power among solvers in the
portfolio.Comment: in Annals of Mathematics and Artificial Intelligence, Springer
Verlag, 201
New directions for Artificial Intelligence (AI) methods in optimum design
Developments and applications of artificial intelligence (AI) methods in the design of structural systems is reviewed. Principal shortcomings in the current approach are emphasized, and the need for some degree of formalism in the development environment for such design tools is underscored. Emphasis is placed on efforts to integrate algorithmic computations in expert systems
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