10198702
Global Optimization with Multi-grid Seed-Growth Parameter Space Division Algorithm
5490125
Deterministic Global Optimization with a Neighbourhood Determination Algorithm Based on Neural Networks
Conventional global optimization algorithms accept a new value that deteriorates the object function at a certain probability to avoid the local optimum. But the computation cost is much higher and the global optimum is not guaranteed. A Neighbourhood Determination (ND) global optimization method was developed in this study. The local optimum neighbourhood boundary is determined and divided by a Wave Front Propagation (WFP) method. Further optimizations are needed only in the irregular-shaped undivided domain. The relationship between the initial solution and the final optimum neighbourhood is used to train the Levenberg-Marquardt neural network. Instead of the costly WFP algorithm, an artificial neural network predicts a new neighbourhood in the remaining domain with dramatically increased efficiency. As the definition domain is completely divided into different subdomains, the actual global optimal solution is found. Numerical examples demonstrate the high efficiency, global searching ability, robustness and stability of this method.
8073932
Parallel Seismic Propagation Simulation in Anisotropic Media by Irregular Grids Finite Difference Method on PC Cluster
A 3D Finite Difference Method (FDM) with spatially irregular grids is developed to simulate the seismic propagation in anisotropic media. Staggered irregular grid finite difference operator with second-order time and spatial accuracy are used to approximate the velocity-stress elastic wave equations. The parallel codes are implemented with Message Passing Interface (MPI) library and c language. The 3D model with complex earth structure geometry is split into more flexible subdomains by the proposed irregular method. The spurious diffractions from “staircase” interfaces can be easily eliminated without grid densification and costs less computing time. Parallel simulation scheme is described by pseudo codes. The spatial parallelism on PC cluster makes it a promising method for geo-science numerical computing. Parallel computation shows that the message passing between different CPUs are composed of the subdomain boundary information and need a considerable communication. The excellent parallelism speedup can be achieved through reasonable subdomain division and fast network connection.
8057684
A Scalable Parallel Algorithm for Global Optimization Based on Seed-Growth Techniques
Global optimization requires huge computations for complex objective functions. Conventional global optimization based on stochastic and probability algorithms can not guarantee an actual global optimum with finite searching iteration. A numerical implementation of the scalable parallel Seed-Growth (SG) algorithm is introduced for global optimization of two-dimensional multi-extremal functions. The proposed parallel SG algorithm is characterized by a parallel phase that exploits the local optimum neighborhood features of the objective function assigned to each processor. The seeds are located at the optimum and inner neighborhood points. Seeds grow towards nearby grids and attach flags to them until reaching the boundary points in each dimension. When all grids in the subspace assigned to each CPU have been searched, the local optimum neighborhood boundaries are determined. As the definition domain is completely divided into different subdomains, the global optimal solution of each CPU is found. A coordination phase follows which, by a synchronous interaction scheme, optimizes the partial results obtained by the parallel phase. The actual global optimum in the total definition space can be determined. Numerical examples demonstrate the high efficiency, global searching ability, robustness and stability of this method.
12847164
Storage Virtualization System with Load Balancing for SAN
Logical Volume Manager (LVM) has been a key subsystem for online disk storage management. Additional layer is created in the kernel to present a logical view of physical storage devices. Many transparent functions can be implemented between the logical and physical layers, such as merging several physical disks into a larger logical device, resizing logical devices without stopping the system. In a logical volume group, files can be striped into several physical disks so as to achieve high I/O performance. But data I/O parallelism by itself does not guarantee the optimal performance of an application since higher data throughput does not necessarily result in better application performance. This paper studied the dynamic load balancing and data redistribution algorithms in the storage virtualization layer when the load becomes imbalanced across the disks due to access pattern fluctuation. An extension of the heuristic load balancing method was proposed to the storage virtualization subsystem of Tsinghua-Mass Storage Network System (TH-MSNS). Logical volume I/O request status is monitored and the physical disks are sorted according to the access number of Logical Extents (LE) per time unit. The I/O operations on a LE of the hottest disk are transparently migrated to other disks. The preliminary performance simulations under a WWW server file access workload give satisfactory results by the promising cooling algorithm in storage virtualization systems.
45543536
Dynamic File Allocation in Storage Area Networks with Neural Network Prediction
Disk arrays are widely used in Storage Area Networks (SANs) to achieve mass storage capacity and high level I/O parallelism. Data partitioning and distribution among the disks is a promising approach to minimize the file access time and balance the I/O workload. But disk I/O parallelism by itself does not guarantee the optimal performance of an application. The disk access rates fluctuate with time because of access pattern variations, which leads to a workload imbalance. The user access pattern prediction is of great importance to dynamic data reorganization between hot and cool disks. Data migration occurs according to current and future disk allocation states and access frequencies. The objective of this paper is to develop a neural network based disk allocation trend prediction method and optimize the disks’ file capacity to their balanced level. A Levenberg-Marquardt neural network was adopted to predict the disk access frequencies with the I/O track. History. Data reorganization on disk arrays was optimized to provide a good workload balance. The simulation results proved that the proposed method performs well.
5687277
A self-adaptive scheduling algorithm of on-demand broadcasts
In mobile wireless systems data on air can be accessed by a large number of mobile users. Many of these applications such as wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems of on-demand broadcast environments. Traditionally, the response time of the requests has been used as a performance measure. In this paper we consider the performance as the average cost of request composed of three kinds of costs—access time cost, tuning time cost, and cost of handling failure request. Our main contribution is a self-adaptive scheduling algorithm named LDFC, which computes the delay cost of data item as the priority for broadcast. It performs well compared with some previous algorithms in this context.
43804396
Reconstruction of band-limited signals from local averages
The sampling theorem says that every band-limited signal is uniquely determined by its sampled values provided the sampling points satisfy certain conditions. However, sampled values obtained in practice may not be the exact values of a signal at sampling points, but only averages of the signal near these points. Grochenig (1992) proved that band-limited signals can be reconstructed exactly from local averages if the sampling density is large enough. We study the reconstruction of band-limited signals from local averages with symmetric averaging functions. We study the aliasing error arising when a non-band-limited signal is reconstructed from local averages and give explicit error bounds. Since the classical "point sampling" can be viewed as a limiting case of average sampling, we indeed give new aliasing error bounds for both regular and irregular sampling.
13911639
A Cost-Efficient Scheduling Algorithm of On-Demand Broadcasts
In mobile wireless systems data on air can be accessed by a large number of mobile users. Many of these applications including wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems of on-demand broadcast environments. Traditionally, the response time of the requests has been used as a performance measure. In this paper we consider the performance as the average cost of request composed of three kinds of costs – access time cost, tuning time cost, and cost of handling failure request. Our main contribution is a self-adaptive scheduling algorithm named LDFC, which computes the delay cost of data item as the priority of broadcast. It costs less compared with some previous algorithms in this context, and shows good adaptability as well even in pure push-based broadcasts.
18180354
Study of coordinative gene expression at the biological process level
MOTIVATION
Cellular processes are not isolated groups of events. Nevertheless, in most microarray analyses, they tend to be treated as standalone units. To shed light on how various parts of the interlocked biological processes are coordinated at the transcription level, there is a need to study the between-unit expressional relationship directly.


RESULTS
We approach this issue by constructing an index of correlation function to convey the global pattern of coexpression between genes from one process and genes from the entire genome. Processes with similar signatures are then identified and projected to a process-to-process association graph. This top-down method allows for detailed gene-level analysis between linked processes to follow up. Using the cell-cycle gene-expression profiles for Saccharomyces cerevisiae, we report well-organized networks of biological processes that would be difficult to find otherwise. Using another dataset, we report a sharply different network structure featuring cellular responses under environmental stress.


SUPPLEMENTARY INFORMATION
http://kiefer.stat.ucla.edu/lap2/download/KL_supplement.pdf.
206624051
A Comparative Study of ANT-Based Optimization for Dynamic Routing
An ANT is a mobile agent that is capable of solving various kinds of routing and congestion problems in computer networking by continuously modifying routing tables in respond to congestion. In a distributed problem solving paradigm, a society of ANTs (each contributing some information) collaborate to solve a larger problem. In recent years, Ant-based algorithms were used to solve classical routing problems such as: Travelling Salesman Problem, Vehicle Routing Problem, Quadratic Assignment Problem, connection-oriented/connectionless routing, sequential ordering, graph coloring and shortest common supersequence. By introducing the general idea of Ant-based algorithms with a focus on Ant Colony Optimization (ACO) and their mathematical models, this paper brings together a collection of ACO algorithms discussed their features, strength and weaknesses.