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.
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.
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.
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.
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.
38637907
Average sampling in spline subspaces
We show that every function in a spline subspace is uniquely determined and can be reconstructed by its local averages near certain points. Regular and irregular average sampling theorems for spline subspaces are obtained.
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.
8720012
The convergence of sampling series based on multiresolution analysis
The sampling theory says that a function may be determined by its sampled values at some certain points provided the function satisfies some certain conditions. In this paper, we consider the convergence of the sampling series for bounded functions with the sampling function from some multiresolution analysis. We also present a convergence theorem on the sampling series with irregular sampling points and give the approximation error.
18703573
Ant colony optimization for routing and load-balancing: survey and new directions
Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigating stagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include: evaporation, aging, pheromone smoothing and limiting, privileged pheromone laying and pheromone-heuristic control. The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNet and its extensions, as well as ASGA and SynthECA. Discussions on new directions include an ongoing work of the authors in applying multiple ant colony optimization in load-balancing.