The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform
Huan He - One of the best experts on this subject based on the ideXlab platform.
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Can P2P Technology Benefit Eyeball ISPs? A Cooperative Profit Distribution Answer
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Ke Xu, Yifeng Zhong, Huan HeAbstract:Peer-to-Peer (P2P) technology has been promoting the development of Internet applications, like Video on Demand (VoD) and file sharing. However, under the traditional pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the adoption of P2P-assisted applications. So far, there is no proper profit distribution mechanism to solve this problem. In this paper, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which both eyeball ISPs and Peer-assisted Content Providers (PCPs) form coalitions and compute a fair Pareto Point to determine profit distribution. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition and give a model to discuss the potential competition among ISPs. We show that such a cooperative method not only guarantees the fair profit distribution among network participants, but also improves the economic efficiency of the network system; and the potential competition among ISPs will make the network more efficient. This paper systematically studies solutions to unbalanced profit distribution caused by P2P and presents a feasible cooperative method to increase and fairly distribute the profit.
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Cooperative Game-Based Pricing and Profit Distribution in P2P Markets
Internet Resource Pricing Models, 2013Co-Authors: Ke Xu, Yifeng Zhong, Huan HeAbstract:Peer-to-Peer (P2P) technology has been the foundation of many important Internet applications, like Video on Demand (VoD) and file sharing. However, under the traditional pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the development of P2P technology. In this chapter, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which both eyeball ISPs and Peer-assisted Content Providers (PCPs) form a separate coalition and compute a fair Pareto Point to determine profit distribution. Here the eyeball ISPs refer to the ISPs which specialize in delivery to hundreds of thousands of residential users, supporting the last-mile connectivity [8]. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition and give a model to discuss the potential competition among ISPs. We show that such a cooperative method not only guarantees the fair profit distribution among network participators, but also helps improve the economic efficiency of the network system.
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Can P2P Technology Benefit Eyeball ISPs? A Cooperative Profit Distribution Answer
arXiv: Networking and Internet Architecture, 2012Co-Authors: Ke Xu, Yifeng Zhong, Huan HeAbstract:Peer-to-Peer (P2P) technology has been regarded as a promising way to help Content Providers (CPs) cost-effectively distribute content. However, under the traditional Internet pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the progress of P2P technology. In this paper, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which eyeball ISPs and Peer-assisted CPs (PCPs) form two coalitions respectively and then compute a fair Pareto Point to determine profit distribution. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition. We show that such a cooperative method not only guarantees the fair profit distribution among network participators, but also helps to improve the economic efficiency of the overall network system. To our knowledge, this is the first work that systematically studies solutions for P2P caused unbalanced profit distribution and gives a feasible cooperative method to increase and fairly share profit.
John E. Renaud - One of the best experts on this subject based on the ideXlab platform.
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Morphing UAV Pareto Curve Shift for Enhanced Performance
45th AIAA ASME ASCE AHS ASC Structures Structural Dynamics & Materials Conference, 2004Co-Authors: Michael T. Rusnell, John E. Renaud, Shawn E. Gano, Stephen M. BatillAbstract:Research in unmanned aerial vehicles (UAVs) has grown in interest over the past couple decades. Historically, UAVs were designed to maximize endurance and range, but demands for UAV designs have changed in recent years. In addition to the traditional demands for endurance and range, today customer demands include maneuverability. Therefore, UAVs are being designed to morph, to change their geometrical shape during flight, for enhanced maneuvering capability. In this investigation the morphing UAV concept under study is referred to as the buckle wing. The design of the buckle-wing airfoil geometries is posed as a multilevel, multiobjective optimization problem. This buckle-wing design problem includes two competing objectives of maneuverability and long range/endurance. Multiobjective problems have many optimal solutions each depicting a dierent compromise scenario. Each optimal solution is a Pareto Point, and the set of all these Points represents the Pareto curve. This is a powerful means of showing the global picture of the solution eld. The goal of this paper is to explore and compare the Pareto curves of the buckle-wing UAV to that of a conventional non-morphing UAV. In order to make this performance comparison, Compromise Programming is used as the optimizing method, and the VortexPanel Method is used in calculating the aerodynamics. The buckle-wing UAV’s enhanced capabilities are demonstrated both quantitatively and graphically.
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An interactive multiobjective optimization design strategy for decision based multidisciplinary design
Engineering Optimization, 2002Co-Authors: Ravindra V. Tappeta, John E. Renaud, Jose F. RodriguezAbstract:This research focuses on Multidisciplinary Design and Optimization (MDO) of large scale systems that have multiple objective functions. The primary goal is to develop and test an interactive multi-objective optimization algorithm for multidisciplinary system design that takes into account the Decision Maker's (DM's) preferences during the design process. An interactive Multi-Objective Optimization Design Strategy (iMOODS) developed in Tappeta and Renaud [1-3] has been modified in this research to include a MDO algorithm to address both the multiobjective and multidisciplinary issues involved in system design. This interactive strategy (iMOODS with MDO capability) provides the DM with a formal means for efficient design exploration around a given Pareto Point. The strategy has been successfully applied to two design problems which are multidisciplinary in nature and have multiple objective functions. The first problem is the design of an autonomous hovercraft system that has four complexly coupled discipli...
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Interactive Multiobjective Optimization Design Strategy for Decision Based Design
Journal of Mechanical Design, 1999Co-Authors: Ravindra V. Tappeta, John E. RenaudAbstract:This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An interactive MultiObjective Optimization Design Strategy (iMOODS) has been developed in this research to include the Pareto sensitivity analysis, Pareto surface approximation and local preference functions to capture the DM’s preferences in an Iterative Decision Making Strategy (IDMS). This new multiobjective optimization procedure provides the DM with a formal means for efficient design exploration around a given Pareto Point. The use of local preference functions allows the iMOODS to construct the second order Pareto surface approximation more accurately in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objective and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.
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INTERACTIVE MULTIOBJECTIVE OPTIMIZATION PROCEDURE
AIAA Journal, 1999Co-Authors: Ravindra V. Tappeta, John E. RenaudAbstract:This research focuses on multiobjective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multiobjective optimization algorithm that takes into account the designer's preferences during the design process. In this research, an interactive multiobjective optimization procedure (IMOOP) that uses an aspiration-level approach to generate Pareto Points is developed. This method provides the designer or the decision maker (DM) with a formal means for efficient design exploration around a given Pareto Point. More specifically, the procedure provides the DM with the Pareto sensitivity information and the Pareto surface approximation at a given Pareto design for decision making and tradeoff analysis. The IMOOP has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for its objective and constraints. The second problem is the design and sizing of a high-performance and low-cost 10-bar structure that has multiple objectives. The results indicate that the Pareto designs predicted by the Pareto surface approximation are reasonable and the performance of the second-order approximation is superior compared to that of the first-order approximation. Using this procedure a set of new aspirations that reflect the DM's preferences are easily and efficiently generated, and the new Pareto design corresponding to these aspirations is close to the aspirations themselves. This is important in that it builds the confidence of the DM in this interactive procedure for obtaining a satisfactory final Pareto design in a minimal number of iterations.
Ke Xu - One of the best experts on this subject based on the ideXlab platform.
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Can P2P Technology Benefit Eyeball ISPs? A Cooperative Profit Distribution Answer
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Ke Xu, Yifeng Zhong, Huan HeAbstract:Peer-to-Peer (P2P) technology has been promoting the development of Internet applications, like Video on Demand (VoD) and file sharing. However, under the traditional pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the adoption of P2P-assisted applications. So far, there is no proper profit distribution mechanism to solve this problem. In this paper, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which both eyeball ISPs and Peer-assisted Content Providers (PCPs) form coalitions and compute a fair Pareto Point to determine profit distribution. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition and give a model to discuss the potential competition among ISPs. We show that such a cooperative method not only guarantees the fair profit distribution among network participants, but also improves the economic efficiency of the network system; and the potential competition among ISPs will make the network more efficient. This paper systematically studies solutions to unbalanced profit distribution caused by P2P and presents a feasible cooperative method to increase and fairly distribute the profit.
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Cooperative Game-Based Pricing and Profit Distribution in P2P Markets
Internet Resource Pricing Models, 2013Co-Authors: Ke Xu, Yifeng Zhong, Huan HeAbstract:Peer-to-Peer (P2P) technology has been the foundation of many important Internet applications, like Video on Demand (VoD) and file sharing. However, under the traditional pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the development of P2P technology. In this chapter, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which both eyeball ISPs and Peer-assisted Content Providers (PCPs) form a separate coalition and compute a fair Pareto Point to determine profit distribution. Here the eyeball ISPs refer to the ISPs which specialize in delivery to hundreds of thousands of residential users, supporting the last-mile connectivity [8]. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition and give a model to discuss the potential competition among ISPs. We show that such a cooperative method not only guarantees the fair profit distribution among network participators, but also helps improve the economic efficiency of the network system.
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Can P2P Technology Benefit Eyeball ISPs? A Cooperative Profit Distribution Answer
arXiv: Networking and Internet Architecture, 2012Co-Authors: Ke Xu, Yifeng Zhong, Huan HeAbstract:Peer-to-Peer (P2P) technology has been regarded as a promising way to help Content Providers (CPs) cost-effectively distribute content. However, under the traditional Internet pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the progress of P2P technology. In this paper, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which eyeball ISPs and Peer-assisted CPs (PCPs) form two coalitions respectively and then compute a fair Pareto Point to determine profit distribution. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition. We show that such a cooperative method not only guarantees the fair profit distribution among network participators, but also helps to improve the economic efficiency of the overall network system. To our knowledge, this is the first work that systematically studies solutions for P2P caused unbalanced profit distribution and gives a feasible cooperative method to increase and fairly share profit.
Frank Vahid - One of the best experts on this subject based on the ideXlab platform.
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Design space exploration of parameterized systems using design of experiments
2011Co-Authors: Frank Vahid, David SheldonAbstract:Recent trends have led to parameterization of many computing components, such as parameterized processors, caches, FPGAs or networks–on-chip, as well as parameters in design tools such as optimization flags. Tuning parameterized systems to meet design goals like performance, energy, size, or power, has become harder due to the enormous design space created by such parameters and due to the large time required to evaluate each system configuration. Previous design space exploration approaches for parameterized systems have either focused on custom or randomized search heuristics. We map such design space exploration onto a statistical paradigm known as Design of Experiments, a paradigm under development since the 1920s that uses methodical experiment selection and sophisticated analysis to obtain maximum information using a minimum number of experiments. We introduce our DPG (Design-of-experiments Pareto-Point Generator) method that performs flexible exploration by allowing the designer to provide information about the number and types of parameters, the approximate time to evaluate a configuration, and the total allowable exploration time. From that information, DPG automatically determines a custom set of experiments to best explore the design space within the allowable time. Such customized design-of-experiments-based exploration represents the unique contribution of this work. We show that DPG provides competitive results across different domains, without requiring the designer to have a detailed understanding of parameter impacts. We created a web-based DPG tool to support designers from various domains, which accepts information from the designer and generates experiments that the designer conducts (iteratively), and generates data and plots from the analysis, including Pareto-Points. The effectiveness of the DoE paradigm for system tuning may have broad applicability for design automation.
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making good Points application specific Pareto Point generation for design space exploration using statistical methods
Field Programmable Gate Arrays, 2009Co-Authors: David Sheldon, Frank VahidAbstract:Field-programmable gate arrays (FPGAs) commonly implement system architectures composed from soft-core configurable components, such as a cache with configurable size or associativity, a processor with configurable datapath units, or a configurable network-on-chip connecting dozens of processors. Configurable components increasingly exist even on pre-fabricated platforms. Tuning configurable components to the particular application running on the architecture and to particular design constraints represents a challenging task often left to a designer. Knowledge of the Pareto-optimal Points of a system for particular applications can be of benefit to designers seeking to make appropriate design tradeoffs for given constraints. Previous methods for generating Pareto Points required extensive knowledge of an architecture's parameter interdependencies, used a simplistic approach that failed to find many parameters, or used randomized search algorithms that may have long runtimes. We introduce an algorithm for finding Pareto Points, based on statistically rigorous methods derived from the Design of Experiments paradigm and extended for the purpose of finding Pareto Points. The resulting DoE-based Pareto Point Generator, or DPG, algorithm finds thorough Pareto Points while running 3 times faster than randomized search algorithms, without requiring designer knowledge of parameter interdependencies--in fact, the approach determines those interdependencies automatically, representing an added bonus. We demonstrate DPG on Platune's configurable processor-bus-cache system-on-chip, Noxim's configurable network-on-chip, and the configurable Microblaze FPGA processor.
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FPGA - Making good Points: application-specific Pareto-Point generation for design space exploration using statistical methods
Proceeding of the ACM SIGDA international symposium on Field programmable gate arrays - FPGA '09, 2009Co-Authors: David Sheldon, Frank VahidAbstract:Field-programmable gate arrays (FPGAs) commonly implement system architectures composed from soft-core configurable components, such as a cache with configurable size or associativity, a processor with configurable datapath units, or a configurable network-on-chip connecting dozens of processors. Configurable components increasingly exist even on pre-fabricated platforms. Tuning configurable components to the particular application running on the architecture and to particular design constraints represents a challenging task often left to a designer. Knowledge of the Pareto-optimal Points of a system for particular applications can be of benefit to designers seeking to make appropriate design tradeoffs for given constraints. Previous methods for generating Pareto Points required extensive knowledge of an architecture's parameter interdependencies, used a simplistic approach that failed to find many parameters, or used randomized search algorithms that may have long runtimes. We introduce an algorithm for finding Pareto Points, based on statistically rigorous methods derived from the Design of Experiments paradigm and extended for the purpose of finding Pareto Points. The resulting DoE-based Pareto Point Generator, or DPG, algorithm finds thorough Pareto Points while running 3 times faster than randomized search algorithms, without requiring designer knowledge of parameter interdependencies--in fact, the approach determines those interdependencies automatically, representing an added bonus. We demonstrate DPG on Platune's configurable processor-bus-cache system-on-chip, Noxim's configurable network-on-chip, and the configurable Microblaze FPGA processor.
Ravindra V. Tappeta - One of the best experts on this subject based on the ideXlab platform.
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An interactive multiobjective optimization design strategy for decision based multidisciplinary design
Engineering Optimization, 2002Co-Authors: Ravindra V. Tappeta, John E. Renaud, Jose F. RodriguezAbstract:This research focuses on Multidisciplinary Design and Optimization (MDO) of large scale systems that have multiple objective functions. The primary goal is to develop and test an interactive multi-objective optimization algorithm for multidisciplinary system design that takes into account the Decision Maker's (DM's) preferences during the design process. An interactive Multi-Objective Optimization Design Strategy (iMOODS) developed in Tappeta and Renaud [1-3] has been modified in this research to include a MDO algorithm to address both the multiobjective and multidisciplinary issues involved in system design. This interactive strategy (iMOODS with MDO capability) provides the DM with a formal means for efficient design exploration around a given Pareto Point. The strategy has been successfully applied to two design problems which are multidisciplinary in nature and have multiple objective functions. The first problem is the design of an autonomous hovercraft system that has four complexly coupled discipli...
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Interactive Multiobjective Optimization Design Strategy for Decision Based Design
Journal of Mechanical Design, 1999Co-Authors: Ravindra V. Tappeta, John E. RenaudAbstract:This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An interactive MultiObjective Optimization Design Strategy (iMOODS) has been developed in this research to include the Pareto sensitivity analysis, Pareto surface approximation and local preference functions to capture the DM’s preferences in an Iterative Decision Making Strategy (IDMS). This new multiobjective optimization procedure provides the DM with a formal means for efficient design exploration around a given Pareto Point. The use of local preference functions allows the iMOODS to construct the second order Pareto surface approximation more accurately in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objective and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.
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INTERACTIVE MULTIOBJECTIVE OPTIMIZATION PROCEDURE
AIAA Journal, 1999Co-Authors: Ravindra V. Tappeta, John E. RenaudAbstract:This research focuses on multiobjective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multiobjective optimization algorithm that takes into account the designer's preferences during the design process. In this research, an interactive multiobjective optimization procedure (IMOOP) that uses an aspiration-level approach to generate Pareto Points is developed. This method provides the designer or the decision maker (DM) with a formal means for efficient design exploration around a given Pareto Point. More specifically, the procedure provides the DM with the Pareto sensitivity information and the Pareto surface approximation at a given Pareto design for decision making and tradeoff analysis. The IMOOP has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for its objective and constraints. The second problem is the design and sizing of a high-performance and low-cost 10-bar structure that has multiple objectives. The results indicate that the Pareto designs predicted by the Pareto surface approximation are reasonable and the performance of the second-order approximation is superior compared to that of the first-order approximation. Using this procedure a set of new aspirations that reflect the DM's preferences are easily and efficiently generated, and the new Pareto design corresponding to these aspirations is close to the aspirations themselves. This is important in that it builds the confidence of the DM in this interactive procedure for obtaining a satisfactory final Pareto design in a minimal number of iterations.