Content providers of P2P-Video-on-Demand (P2P-VoD) services aim to provide a high quality, scalable service to users, and at the same time, operate the system with a manageable operating cost. Given the volume-based c...
详细信息
ISBN:
(纸本)9781424499199
Content providers of P2P-Video-on-Demand (P2P-VoD) services aim to provide a high quality, scalable service to users, and at the same time, operate the system with a manageable operating cost. Given the volume-based charging model by ISPs, it is to the best interest of the P2P-VoD content providers to reduce peers' access to the content server so as to reduce the operating cost. In this paper, we address an important open problem: what is the “optimal replication ratio” in a P2P-VoD system such that peers will receive service from each other and at the same time, reduce the traffic to the content server. We address two fundamental problems: (1) what is the optimal replication ratio of a movie given its popularity, and (2) how to achieve the optimal ratios in a distributed and dynamic fashion. We formally show how movie popularities can impact server's workload, and formulate the video replication as an optimization problem. We show that the conventional wisdom of using the proportional replication strategy is non-optimal, and expand the design space to both passive replacement policy and active push policy to achieve the optimal replication ratios. We consider practical implementation issues, evaluate the performance of P2P-VoD systems and show that our algorithms can greatly reduce server's workload and improve streaming quality.
In this paper, the use of the genetic algorithm in optimization of a nonlinear filter in adaptive noise cancellation (ANC) system is proposed. While the standard adaptive algorithms in nonlinear systems may converge t...
详细信息
ISBN:
(纸本)9781424458981
In this paper, the use of the genetic algorithm in optimization of a nonlinear filter in adaptive noise cancellation (ANC) system is proposed. While the standard adaptive algorithms in nonlinear systems may converge to a local minimum, genetic algorithms (GAs) handle this problem efficiently. Computer simulations show that a superior performance is achieved using the proposed system with a not complex GA. A comparison of the proposed system with a popular ANC system also shows a high reduction of estimation error's power.
Recently Particle Swarm optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improv...
详细信息
Particle swarm optimization (PSO) is one recently proposed population-based stochastic optimization technique, and gradient-based descent methods are efficient local optimization techniques that are often used as a ne...
详细信息
Particle swarm optimization (PSO) is one recently proposed population-based stochastic optimization technique, and gradient-based descent methods are efficient local optimization techniques that are often used as a necessary ingredient of hybrid algorithms for global optimization problems (GOPs). By examining the properties of the two methods, a two-stage hybrid algorithm for global optimization is proposed. In the present algorithm, the gradient descent technique is used to find a local minimum of the objective function efficiently, and a PSO method with latent parallel search capability is employed to help the algorithm to escape from the previously converged local minima to a better point which is then used as a starting point for the gradient methods to restart a new local search. The above search procedure is applied repeatedly until a global minimum is found (when a global minimum is known in advance) or the maximum number of function evaluations is reached. In addition, a repulsion technique and partially initializing population method are incorporated in the new algorithm to increase its global jumping ability. Simulation results on 15 test problems including five large-scale ones with dimensions up to 1000 demonstrate that the proposed method is more stable and efficient than several other existing methods.
The paper established a mathematical model of integrated optimization of the navigation performance for a high-speed ship, improved the genetic algorithm to a new parallel genetic algorithm based on the sensitive vari...
详细信息
Working with the Naval Research Laboratory, Celestech has implemented advanced non-linear hyperspectral image (HSI) processing algorithms optimized for Graphics Processing Units (GPU). These algorithms have demonstrat...
详细信息
ISBN:
(纸本)9780819481597
Working with the Naval Research Laboratory, Celestech has implemented advanced non-linear hyperspectral image (HSI) processing algorithms optimized for Graphics Processing Units (GPU). These algorithms have demonstrated performance improvements of nearly 2 orders of magnitude over optimal CPU-based implementations. The paper briefly covers the architecture of the NIVIDIA GPU to provide a basis for discussing GPU optimization challenges and strategies. The paper then covers optimization approaches employed to extract performance from the GPU implementation of Dr. Bachmann's algorithms including memory utilization and process thread optimization considerations. The paper goes on to discuss strategies for deploying GPU-enabled servers into enterprise service oriented architectures. Also discussed are Celestech's on-going work in the area of middleware frameworks to provide an optimized multi-GPU utilization and scheduling approach that supports both multiple GPUs in a single computer as well as across multiple computers. This paper is a complementary work to the paper submitted by Dr. Charles Bachmann entitled "A Scalable Approach to Modeling nonlinear Structure in Hyperspectral Imagery and Other high-Dimensional Data Using Manifold Coordinate Representations". Dr. Bachmann's paper covers the algorithmic and theoretical basis for the HSI processing approach.
In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity ...
详细信息
ISBN:
(纸本)9781424466757
In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity (about 10 m/s) on a loose surface (dirty road). To tackle this highly non-linear control problem, we employ multi-objective evolutionary algorithms to explore and optimize the parameters of a neural network-based controller. The obtained controller is shown to be robust with respect to uncertainties of the robot parameters, speed of the maneuver and properties of the ground. The controller is tested using two mathematical models of significantly different complexity and accuracy.
This work presents a high-order finite element solver developed in the MatLab environment with procedures for highperformance computing based on a very simple domain decomposition technique. The code has been develop...
详细信息
ISBN:
(纸本)9781905088416
This work presents a high-order finite element solver developed in the MatLab environment with procedures for highperformance computing based on a very simple domain decomposition technique. The code has been developed with the goal of testing news procedures for the solution of finite elements problems using distributed computing. The code has been tested in many different problems including Poisson operator, Plane Stress, Plane Strain, Linear and nonlinear Elasticity, optimization, Contact and Reynolds Equation. This paper aims to present features of the developed software, mainly the aspects of domain decomposition and distributed computing.
Particle swarm optimization (PSO) is one recently proposed population-based stochastic optimization technique, and gradient-based descent methods are efficient local optimization techniques that are often used as a ne...
详细信息
Particle swarm optimization (PSO) is one recently proposed population-based stochastic optimization technique, and gradient-based descent methods are efficient local optimization techniques that are often used as a necessary ingredient of hybrid algorithms for global optimization problems (GOPs). By examining the properties of the two methods, a two-stage hybrid algorithm for global optimization is proposed. In the present algorithm, the gradient descent technique is used to find a local minimum of the objective function efficiently, and a PSO method with latent parallel search capability is employed to help the algorithm to escape from the previously converged local minima to a better point which is then used as a starting point for the gradient methods to restart a new local search. The above search procedure is applied repeatedly until a global minimum is found (when a global minimum is known in advance) or the maximum number of function evaluations is reached. In addition, a repulsion technique and partially initializing population method are incorporated in the new algorithm to increase its global jumping ability. Simulation results on 15 test problems including five large-scale ones with dimensions up to 1000 demonstrate that the proposed method is more stable and efficient than several other existing methods.
The LHCb experiment is dedicated to studying CP violation and rare decay phenomena. In order to achieve these physics goals precise tracking and vertexing around the interaction point is crucial. This is provided by t...
详细信息
The LHCb experiment is dedicated to studying CP violation and rare decay phenomena. In order to achieve these physics goals precise tracking and vertexing around the interaction point is crucial. This is provided by the VELO (VErtex LOcator) silicon detector. After digitization, FPGAs are employed to run several algorithms to suppress noise and reconstruct clusters. This is performed by an FPGA based processing board. An off-line software project, VETRA, has been developed which performs a bit perfect emulation of this complex processing in the FPGAs. This is a novel development as this hardware emulation is not standalone but rather is fully integrated into the LHCb software to allow the reconstruction of full data from the detector. This software platform facilitates the development and understanding of the behaviour of the processing algorithms, the optimization of the parameters of the algorithms that will be loaded into the FPGA and monitoring of the detector performance. This framework has also been adopted by the Silicon Tracker detector of LHCb. This processing framework was successfully used with the first 1500 tracks of data in the VELO obtained from the first LHC beam in September 2008. The software architecture and utilisation of the VETRA project will be discussed in detail.
暂无评论