A heuristic approximation for reducing problem size in network file allocation models

The design of distributed processing systems has increasingly drawn the attention of systems designers as more and more organizations recognize the advantages of decentralizing operations. However, determination of an optimal or preferred distributed data base configuration is a nontrivial task. A viable model for allocating files to nodes in a distributed system must consider the tradeoffs between costs and service and the unique characteristics of the system, including the planned redundancy inherent in the file organization. It also must be solvable within the current state of the art.This paper presents a mixed-integer linear programming model which allows assignment of replicated and/or partitioned files to the nodes of a distributed network while considering query, update and storage costs associated with the file assignments. The resulting model, though large, is solvable even for networks with a substantial number of nodes and links. Because the model relies on an average communication cost for determining file location, results are presented comparing the model performance to that of a model utilizing exact communication costs.     

Price co-ordination for a resource allocation problem in a large-scale system

A two-level partially decentralized control for the optimal resource allocation in a large-scale system is considered. The case of different information of the co-ordinator and also of particular local controllers is researched. The co-ordinator has information which has essential meaning for the whole system, whereas the local controllers have more detailed information concerning the particular subsystems. An original modification of the price co-ordination method which is suitable for different information of particular controllers is proposed. Owing to this, the case of correlated random variables appearing in particular subsystems can be taken into account.     

Neural network approach for allocation with capacity

In this paper we discuss neural network approach for allocation with capacity constraints problem. This problem can be formulated as zero-one integer programming problem. We transform this zero-one integer programming problem into an equivalent nonlinear programming problem by replacing zero-one constraints with quadratic concave equality constraints. We propose two kinds of neural network structures based on penalty function method and augmented Lagrangian multiplier method, and compare them by theoretical analysis and numerical simulation. We show that penalty function based neural network approach is not good to combinatorial optimization problem because it falls in the dilemma whether terminating at an infeasible solution or sticking at any feasible solution, and augmented Lagrangian multiplier method based neural network can alleviate this suffering in some degree.     

Automatic resource allocation in a distributed camera network

In this paper, we present a hierarchical smart resource coordination and reconfiguration framework for distributed systems. We view the coordination problem as one of context aware resource reconfiguration. The fundamental unit in this hierarchy is a Fault Containment Unit (FCU) that provides run-time fault-tolerance by deciding on the best alternative course of action when a failure occurs. FCUs are composed hierarchically and are responsible for dynamically reconfiguring failing FCUs at lower levels. When such a reconfiguration is not possible, FCUs propagate the failure upward for resolution. We evaluate the effectiveness of our framework in a people tracking application using a network of cameras. The task for our multi-camera network is to allocate pairs of cameras that localize a subject optimally given the current run-time context. The system automatically derives policies for switching between camera pairs that enable robust tracking while being attentive to certain performance measures. Our approach is unique in that we model the dynamics in the scene and the camera network configuration steers the policies to provide robust tracking.     

An efficient Nash-implementation mechanism for network resource allocation

We propose a mechanism for auctioning bundles of multiple divisible goods in a network where buyers want the same amount of bandwidth on each link in their route. Buyers can specify multiple routes (corresponding to a source-destination pair). The total flow can then be split among these multiple routes. We first propose a one-sided VCG-type mechanism. Players do not report a full valuation function but only a two-dimensional bid signal: the maximum quantity that they want and the per-unit price they are willing to pay. The proposed mechanism is a weak Nash implementation, i.e., it has a non-unique Nash equilibrium that implements the social-welfare maximizing allocation. We show the existence of an efficient Nash equilibrium in the corresponding auction game, though there may exist other Nash equilibria that are not efficient. We then generalize this to arbitrary bundles of various goods. Each buyer submits a bid separately for each good but their utility function is a general function of allocations of bundles of various divisible goods. We then present a double-sided auction mechanism for multiple divisible goods. We show that there exists a Nash equilibrium of this auction game which yields the efficient allocation with strong budget balance.     

Neural network models

The remarkable processing capabilities of the nervous system must derive at least in part from the large numbers of neurons participating (roughly 10¹⁰), since the timescales involved are of the order of milliseconds, rather than the nanoseconds of modern computers. We summarise common features of the neural network models which attempt to capture this behaviour and describe the many levels of parallelism which they exhibit. A range of models has been implemented on the SIMD (ICL Distributed Array Processor) and MIMD (Meiko Computing Surface) hardware at Edinburgh. Examples include: (i) training algorithms in the context of the Hopfield net, with specific application to the storage of words and text with content-addressable memory; (ii) the back-propagation training algorithm for the multi-layer perception; (iii) image restoration with Hopfield and Tank analogue neurons, and (iv) the Durbin and Willshaw elastic net, as applied to the travelling salesman problem.     

Multi-objective scheduling and a resource allocation problem in hospitals

This study addresses the issue of scheduling medical treatments for resident patients in a hospital. Schedules are made daily according to the restrictions on medical equipment and physicians who are being assigned at the same time. The problem is formulated as a multi-objective binary integer programming (BIP) model. Three types of metaheuristics are proposed and implemented to deal with the discrete search space, numerous variables, constraints and multiple objectives: a variable neighborhood search (VNS)-based method, scatter search (SS)-based methods and a non-dominated sorting genetic algorithm (NSGA-II). This paper also provides the results of computational experiments and compares their ability to find efficient solutions to the multi-objective scheduling problem.     

Solving a dynamic resource allocation problem through continuous optimization

A class of dynamic resource allocation problems with infinite planning horizon are studied. We observe special structures in the dynamic programming formulation of the problem, which enable us to convert it to continuous optimization problems that can be more easily solved. Structural properties of the problems are discussed, and explicit solutions are given for some special cases.     

Overlay network resource allocation using a decentralized market-based approach

We present a decentralized market-based approach to resource allocation in a heterogeneous overlay network. This resource allocation strategy dynamically assigns resources in an overlay network to requests for service based on current system utilization, thus enabling the system to accommodate fluctuating demand for its resources. Our approach is based on a mathematical model of this resource allocation environment that treats the allocation of system resources as a constrained optimization problem. From the solution to the dual of this optimization problem, we derive a simple decentralized algorithm that is extremely efficient. Our results show the near optimality of the proposed approach through extensive simulation of this overlay network environment. The simulation study utilizes components taken from a real-world middleware application environment and clearly demonstrates the practicality of the approach in a realistic setting.     

Fairness-improved and QoS-guaranteed resource allocation for NOMA-based S-IoT network

Dear editor, Recently,the satellite-integrated Internet of Things (S-IoT)has supported many emergent IoT applications[1],such as smart agriculture,remote health...     

一种启发式网络虚拟化资源分配算法

网络虚拟化是克服当前Internet僵化问题的一种重要方法,而资源分配是网络虚拟化技术的核心.为了平衡负载,本文提出了一种启发式资源分配算法HVNE.该算法充分利用虚拟节点和虚拟链路间的关联因素(虚拟网络拓扑),将节点映射和链路映射两个过程合并为一个统一的过程,改善了传统映射算法在拓扑稀疏时,算法性能不理想的问题.此外,HVNE允许同一个虚拟请求中的多个虚拟节点映射到同一个物理节点,节约了物理链路资源.HVNE将无向图的"k-区域划分优化"理论与传统的拓扑分割理论相结合,定义了虚拟拓扑间节点的关联因子,改进了传统的星形分割方法,使之能适用于大规模网络.仿真实验表明,HVNE在保证网络负载的情况下,获得了较好的虚拟请求接受率,较高的资源利用率和网络收益.     

A futures market-based model for dynamic network resource allocation

Management of telecommunication network requires quick, continuous and decentralized allocation of network bandwidth to various sorts of demands. So as to achieve the efficient network resource allocation, this paper describes a market-based model combining futures market with the agent-based approach. That is, utilization time is divided into many timeslots, and futures markets in hereafter use of bandwidth are opened. In our model, all market participants (software agents) observe only market prices and decide to buy or sell bandwidth trying to maximize their utilities over time so that they can secure enough network resources. The authors discuss network resource allocation through simulation using the proposed model. Masayuki Ishinishi, Ph.D.: He graduated from National Defense Academy in 1995 and 2000. He received the B.E. (1995) and M.E.(2000) degrees in computer science from National Institution for Academic Degrees (NIAD). He received his Ph.D. degree from Tokyo Institute of Technology in 2003. He has been a communications officer at Air Communications and Systems Wing in Japan Air Self-Defence Force (JASDF) since 2003. His research interests include information assurance, agent-based modeling and simulation, multi-agent system and market-based control. He is a member of IEEJ, IPSJ and JSAI. Yuhsuke Koyama, Ph.D.: He received the B.Econ., M.Econ., and Ph.D. degrees in economics from Kyoto University, in 1996, 1998, 2002, respectively. He has been a research associate of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, mathematical sociology and experimental economics. He is a member of JAFEE, JAMS, JASESS and JASAG. Hiroshi Deguchi, Ph.D.: He received his Ph.D. degree in systems science from Tokyo Institute of Technology, in 1986. He also received the Dr. Econ. degree from Kyoto University in 2001. He has been a Professor of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, computational organization theory, agent-based modeling, social system theory, gaming simulation, and philosophy of science. He is a member of SICE, JAMS, IPSJ, PHSC, JASAG and JAFEE. Hajime Kita, Ph.D.: He received the B.E., M.E., and Ph.D. degrees in electrical engineering from Kyoto University, in 1982, 1984, 1991, respectively. He has been a Professor of Kyoto University since 2003, His research field is systems science/engineering, and his research interests are evolutionary computation, neural networks and socio-economic analysis of energy systems, and agentbased modeling. He is a member of IEEJ, IEICE, ISCIE, JNNS, JSER, ORSJ and SICE.     

Measurement-based optimal resource allocation for network services with pricing differentiation

In this paper, we introduce a model for allocating available resources in service-oriented network, with particular focus on delay sensitive services. The model is based on a pricing scheme for the offered services and also takes into consideration the quality of service requirements of each service class through a probabilistic delay-bound constraint. The proposed policy is dynamic in nature and relies on online measurements of the incoming traffic for adjusting the class allocations. We illustrate its performance and its robustness to various tuning parameters through an extensive simulation study that considers various simulation scenarios including experiments based on real network traces.     

QoE-driven resource allocation for mobile IP services in wireless network

In this study, quality of experience(QoE)-driven resource allocation for multi-applications in Internet Protocol(IP)-based wireless networks is studied. Considering that the mean opinion score(MOS) summation maximization problem is not fair to satisfy heterogeneous users’ QoE with various mobile applications, we apply multi-objective optimization method to maximize each user’s MOS utility. At the beginning of this work, the relationship between MOS utility and user transmission rate for three multimedia applications, that is, File Download, Internet Protocol Television, and Voice over Internet Protocol are discussed. However, the relations under diverse evaluation models are quite different and users in various mobile applications have different requirements, which make the optimization problem difficult to solve. To meet each user’s minimum rate requirement,the idea of Nash bargaining solution is applied in the Hungarian-based subcarrier assignment problem. Then to simplify the power allocation problem, the concept of equivalent channel is introduced. Further by applying the tolerance membership function, we develop a fuzzy Max-Min decision model for generating an optimal power allocation solution. Simulation results demonstrate the satisfying characteristics of the proposed algorithm in terms of MOS utility and average data rate.     

Resource pooling for frameless network architecture with adaptive resource allocation

The system capacity for future mobile communication needs to be increased to fulfill the emerging requirements of mobile services and innumerable applications. The cellular topology has for long been regarded as the most promising way to provide the required increase in capacity. However with the emerging densification of cell deployments, the traditional cellular structure limits the efficiency of the resource, and the coordination between different types of base stations is more complicated and entails heavy cost. Consequently, this study proposes frameless network architecture (FNA) to release the cell boundaries, enabling the topology needed to implement the FNA resource allocation strategy. This strategy is based on resource pooling incorporating a new resource dimension-antenna/antenna array. Within this architecture, an adaptive resource allocation method based on genetic algorithm is proposed to find the optimal solution for the multi-dimensional resource allocation problem. Maximum throughput and proportional fair resource allocation criteria are considered. The simulation results show that the proposed architecture and resource allocation method can achieve performance gains for both criteria with a relatively low complexity compared to existing schemes.     

Neural network models for breast cancer prognosis

Estimating the risk of relapse for breast cancer patients is necessary, since it affects the choice of treatment. This problem involves analysing data of times to relapse of patients and relating them to prognostic variables. Some of the times to relapse will usually be censored.We investigate various ways of using neural network models to extend traditional statistical models in this situation. Such models are better able to model both non-linear effects of prognostic factors and interactions between them, than linear logistic or Cox regression models. With the dataset used in our study, however, the prediction of the risk of relapse is not significantly improved when using a neural network model. Predicting the risk that a patient will relapse within three years, say, is possible from this data, but not when any relapse will happen.     

基于效用的虚拟网络资源分配模型

在虚拟网络环境中,提出了基于效用的链路资源分配模型,使链路资源在存在拥塞的情况下达到最优分配。模型设计了单寡头垄断情况下虚拟业务提供用户与垄断者的效用函数,以及其达到均衡的条件,同时增加寡头数来讨论企业进入条件以及资源最优分配下的企业数量。由仿真分析可以得出,基于效用的资源分配模型能够有效地促进用户与企业之间、企业与企业之间的博弈,在双方利益最大化基础上实现资源最优分配。     

Neural network approach for multicriteria solid transportation problem

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.     

Neural network language models for off-line handwriting recognition

Unconstrained off-line continuous handwritten text recognition is a very challenging task which has been recently addressed by different promising techniques. This work presents our latest contribution to this task, integrating neural network language models in the decoding process of three state-of-the-art systems: one based on bidirectional recurrent neural networks, another based on hybrid hidden Markov models and, finally, a combination of both. Experimental results obtained on the IAM off-line database demonstrate that consistent word error rate reductions can be achieved with neural network language models when compared with statistical N-gram language models on the three tested systems. The best word error rate, 16.1%, reported with ROVER combination of systems using neural network language models significantly outperforms current benchmark results for the IAM database.     

Neural network based models for software effort estimation: a review

Prediction of software development effort is the key task for the effective management of any software industry. The accuracy and reliability of prediction mechanisms is also important. Neural network based models are competitive to traditional regression and statistical models for software effort estimation. This comprehensive article, covers various neural network based models for software estimation as presented by various researchers. The review of twenty-one articles covers a range of features used for effort prediction. This survey aims to support the research for effort prediction and to emphasize capabilities of neural network based model in effort prediction.