Heuristic (computer science)

About: Heuristic (computer science) is a research topic. Over the lifetime, 28087 publications have been published within this topic receiving 596086 citations. The topic is also known as: computer heuristic & computer heuristics.

Sensor Selection via Convex Optimization

Abstract: We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the (m k) possible choices of sensor measurements is not practical unless m and k are small. In this paper, we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m 3 operations; for m= 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2-GHz personal computer.

Rethinking virtual network embedding: substrate support for path splitting and migration

Abstract: Network virtualization is a powerful way to run multiple architectures or experiments simultaneously on a shared infrastructure. However, making efficient use of the underlying resources requires effective techniques for virtual network embedding--mapping each virtual network to specific nodes and links in the substrate network. Since the general embedding problem is computationally intractable, past research restricted the problem space to allow efficient solutions, or focused on designing heuristic algorithms. In this paper, we advocate a different approach: rethinking the design of the substrate network to enable simpler embedding algorithms and more efficient use of resources, without restricting the problem space. In particular, we simplify virtual link embedding by: i) allowing the substrate network to split a virtual link over multiple substrate paths and ii) employing path migration to periodically re-optimize the utilization of the substrate network. We also explore node-mapping algorithms that are customized to common classes of virtual-network topologies. Our simulation experiments show that path splitting, path migration,and customized embedding algorithms enable a substrate network to satisfy a much larger mix of virtual networks

Efficient algorithms for Web services selection with end-to-end QoS constraints

Abstract: Service-Oriented Architecture (SOA) provides a flexible framework for service composition Using standard-based protocols (such as SOAP and WSDL), composite services can be constructed by integrating atomic services developed independently Algorithms are needed to select service components with various QoS levels according to some application-dependent performance requirements We design a broker-based architecture to facilitate the selection of QoS-based services The objective of service selection is to maximize an application-specific utility function under the end-to-end QoS constraints The problem is modeled in two ways: the combinatorial model and the graph model The combinatorial model defines the problem as a multidimension multichoice 0-1 knapsack problem (MMKP) The graph model defines the problem as a multiconstraint optimal path (MCOP) problem Efficient heuristic algorithms for service processes of different composition structures are presented in this article and their performances are studied by simulations We also compare the pros and cons between the two models

Model-driven data acquisition in sensor networks

Abstract: Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.