User-centric Optimization Research Articles

User-Centric Optimization for Constraint Web Service Composition using a Fuzzyguided Genetic Algorithm System

User-Centric Optimization for Constraint Web Service Composition using a Fuzzyguided Genetic Algorithm System

A user-centric optimization of emergency map symbols to facilitate common operational picture

ABSTRACT Common operational understanding among engaged emergency responders is facilitated through shared operational pictures during crisis situations. Sharing is typically achieved through interactive tools, either desktop or web-based, in which map displays play an essential role. That role can be further strengthened if (1) agreed emergency symbols that are used in map-based interactive tools are sufficient to encode multifaceted operational information visually; and (2) the symbols are legible and meaningful for the diverse users of those tools. The authors revisited official emergency map symbols in use in Norway and reconsidered them against current requirements. To this end, they first conducted several meetings with stakeholders to elicit adequate revision requirements. Next, the reconsideration included the extension of the symbol set, symbol modification, and grouping. After the reconsideration, emergency management officers and specialists were interviewed. The interviews confirmed the agreement with the symbol categorization, extension of the symbols, and their modifications. The interviewees also made numerous suggestions to be considered in a follow-up study. Moreover, two concepts – symbol standardization and symbol harmonization – were proposed.

User Centric Optimization in MANETS using Firefly algorithm

User Centric Optimization in MANETS using Firefly algorithm

User-Centric Optimization for Constraint Web Service Composition using a Fuzzyguided Genetic Algorithm System

Service-Oriented Applications (SOA) are being regarded as the main pragmatic solution for distributed environments. In such systems, however each service responds the user request independently, it is essential to compose them for delivering a compound value-added service. Since, there may be a number of compositions to create the requested service, it is important to find one which its properties are close to user's desires and meet some non-functional constraints and optimize criteria such as overall cost or response time. In this paper, a user-centric approach is presented for evaluating the service compositions which attempts to obtain the user desires. This approach uses fuzzy logic in order to inference based on quality criteria ranked by user and Genetic Algorithms to optimize the QoS-aware composition problem. Results show that the Fuzzy-based Genetic algorithm system enables user to participate in the process of web service composition easier and more efficient.

Perturbation analysis of stochastic hybrid systems and applications to resource contention games

We provide an overview of the recently developed general infinitesimal perturbation analysis (IPA) framework for stochastic hybrid systems (SHSs), and establish some conditions under which this framework can be used to obtain unbiased performance gradient estimates in a particularly simple and efficient manner. We also propose a general scheme for systematically deriving an abstraction of a discrete event system (DES) in the form of an SHS. Then, as an application of the general IPA framework, we study a class of stochastic non-cooperative games termed “resource contention games” modeled through stochastic flow models (SFMs), where two or more players (users) compete for the use of a sharable resource. Simulation results are provided for a simple version of such games to illustrate and contrast system-centric and user-centric optimization.

Resource contention games in multiclass stochastic flow models

We consider resource contention games in a stochastic hybrid system setting using Stochastic Flow Models (SFM) with multiple classes and class-dependent objectives. We present a general modeling framework for such games, where Infinitesimal Perturbation Analysis (IPA) estimators are derived for the derivatives of various class-dependent objectives. This allows us to study these games from the point of view of system-centric optimization of a performance metric and compare it to the user-centric approach where each user optimizes its own performance metric. We derive explicit solutions for a specific model in which the competing user classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. The unbiasedness of the IPA estimators is established in this case and it is shown that under certain conditions the system-centric and user-centric optimization solutions coincide.

Perturbation Analysis of Stochastic Hybrid Systems and Applications to Some Non-Cooperative Games

We establish some conditions under which Infinitesimal Perturbation Analysis (IPA) for general Stochastic Hybrid Systems (SHS) becomes particularly simple and efficient. We then apply IPA to a class of stochastic non-cooperative games termed “resource contention games” modeled through Stochastic Flow Models (SFMs), where two or more players (users) compete for the use of a sharable resource. Simulation examples are provided for a simple version of such games to illustrate and contrast system-centric and user-centric optimization.

Perturbation Analysis and Resource Contention Games in Multiclass Stochastic Flow Models

Abstract We present a stochastic hybrid system framework for Stochastic Flow Models (SFMs) with multiple classes and class-dependent performance objectives. Using Infinitesimal Perturbation Analysis (IPA) estimators for the derivatives of such objectives, we formulate and solve resource contention games that contrast system-centric and user-centric optimization in such SFMs.