虚拟化技术在疾病预防控制系统的应用探讨

以浙江省免疫规划信息系统作为8小时工作模式的业务系统实例,阐述了数据中心应用虚拟化技术后在硬件成本、管理强度、资源分配和利用等方面带来的改变。     

一种新的实现效用函数最大最小公平性的分布式算法

计算机网络中的一个核心问题就是如何在竞争的流之间公平地分配带宽。由于网络是分布式的,因此又要求带宽分配算法可以是分布式的,这样就可以在路由器端和用户端配置,从而分别不停地调节速率以达到网络平衡和最优状态。不同的算法对应不同的公平性标准,其中应用最为广泛的就是最大最小公平性以及效用函数最大最小公平性。本文提出了一种新的方法来实现效用函数最大最小公平性,这种方法基于非线性优化中的向量优化方法和Kuhn-Tucker条件。利用这种方法可以把网络优化问题转化为一系列等价的条件,这些条件的最优解就是原问题的解。而求解这些条件可以运用经典的非线性优化的梯度投影算法,由此产生了一种分布式的用户链路算法并通过案例验证了其正确性及性能。     

Locally linear reconstruction for instance-based learning

Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k.     

Solving a real-time allocation problem with constraint programming

In this paper, we present an original approach (CPRTA for “Constraint Programming for solving Real-Time Allocation”) based on constraint programming to solve a static allocation problem of hard real-time tasks. This problem consists in assigning periodic tasks to distributed processors in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. Two efficient new approaches are proposed and validated with experimental results. Moreover, CPRTA exhibits very interesting properties. It is complete (if a problem has no solution, the algorithm is able to prove it); it is non-parametric (it does not require specific tuning) thus allowing a large diversity of models to be easily considered. Finally, thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process.     

On the complexity of bandwidth allocation in radio networks

We define and study an optimization problem that is motivated by bandwidth allocation in radio networks. Because radio transmissions are subject to interference constraints in radio networks, physical space is a common resource that the nodes have to share in such a way, that concurrent transmissions do not interfere. The bandwidth allocation problem we study under these constraints is the following. Given bandwidth (traffic) demands between the nodes of the network, the objective is to schedule the radio transmissions in such a way that the traffic demands are satisfied. The problem is similar to a multicommodity flow problem, where the capacity constraints are replaced by the more complex notion of non-interfering transmissions. We provide a formal specification of the problem that we call round weighting  . By modeling non-interfering radio transmissions as independent sets, we relate the complexity of round weighting to the complexity of various independent set problems (e.g. maximum weight independent set, vertex coloring, fractional coloring). From this relation, we deduce that in general, round weighting is hard to approximate within n^1−ε$n^{1-\epsilon }$ (n$n$ being the size of the radio network). We also provide polynomial (exact or approximation) algorithms e.g. in the following two cases: (a) when the interference constraints are specific (for instance for a network whose vertices belong to the Euclidean space), or (b) when the traffic demands are directed towards a unique node in the network (also called gathering, analogous to single commodity flow).     

Resource allocation optimization for quantitative service differentiation on server clusters

The goal of service differentiation is to provide different service quality levels to meet changing system configuration and resource availability and to satisfy different requirements and expectations of applications and users. In this paper, we investigate the problem of quantitative service differentiation on cluster-based delay-sensitive servers. The goal is to support a system-wide service quality optimization with respect to resource allocation on a computer system while provisioning proportionality fairness to clients. We first propose and promote a square-root proportional differentiation model. Interestingly, both popular delay factors, queueing delay and slowdown, are reciprocally proportional to the allocated resource usage. We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then study the problem of service differentiation provisioning from an important relative performance metric, slowdown. We give a closed-form expression of the expected slowdown of a popular heavy-tailed workload model with respect to resource allocation on a server cluster. We design a two-tier resource management framework, which integrates a dispatcher-based node partitioning scheme and a server-based adaptive process allocation scheme. We evaluate the resource allocation framework with different models via extensive simulations. Results show that the square-root proportional model provides service differentiation at a minimum cost of system delay. The two-tier resource allocation framework can provide fine-grained and predictable service differentiation on cluster-based servers.     

A constant-time dynamic storage allocator for real-time systems

Dynamic memory allocation has been used for decades. However, it has seldom been used in real-time systems since the worst case of spatial and temporal requirements for allocation and deallocation operations is either unbounded or bounded but with a very large bound. In this paper, a new allocator called TLSF (Two Level Segregated Fit) is presented. TLSF is designed and implemented to accommodate real-time constraints. The proposed allocator exhibits time-bounded behaviour, O(1), and maintains a very good execution time. This paper describes in detail the data structures and functions provided by TLSF. We also compare TLSF with a representative set of allocators regarding their temporal cost and fragmentation. Although the paper is mainly focused on timing analysis, a brief study and comparative analysis of fragmentation incurred by the allocators has been also included in order to provide a global view of the behaviour of the allocators. The temporal and spatial results showed that TLSF is also a fast allocator and produces a fragmentation close to that caused by the best existing allocators.

合同战术训练评估系统体系结构

在介绍合同战术训练评估需求分析、系统总体结构的基础上,研究了其中的演习结果评估子系统的框架和层次结构,横向上将其分为主框架、行动评估模块、算法插件3个部分,纵向上把它分为数据采集、数据处理、成绩报告3层,从而有效地降低了系统各组成部分间的耦合程度,并使系统能综合运用多样化的数据采集手段以及效能分析方法,具有一定的参考价值。     

认知无线电中动态频谱分配问题的研究

介绍了认知无线电的研究现状,对认知无线电系统中已有的动态频谱分配模型进行了分析并结合各种算法进行了比较。在此基础上总结并提出了频谱共享的一些挑战性问题,探讨了动态频谱分配的一些热点问题并构想了未来的发展方向。     

大容量软件管理片上存储器分配技术综述

在现代计算机体系结构中,对于大容量的片上存储器,越来越多地采用软件来管理,被认为是解决存储墙问题的一个有效途径。本文介绍了片上大容量软件管理存储器分配技术的研究进展,对有代表性的技术进行了概要的介绍和评述,并预测了技术的发展方向。