A simulation of an economic,self-organising resource allocation approach for application layer networks

Application Layer Networks like Utility Grid Networks or Cloud Computing Systems, often depicted as large scale self-organising architectures using a shared infrastructure, will need innovative management approaches for controlling and matching services demand and supply. This article shows a self-organising resource allocation approach based on principles of the Catallaxy, an economic concept of F.A. von Hayek. The implementation uses a coevolutionary learning algorithm for adaptation of the agent strategy to the dynamic Application Layer Network environment. The simulation study performs a sensitivity analysis of a large scale Application Layer Network scenario with 2000 agents. A set of representative agents is selected and their evolutionary behaviour analysed.     

The catallaxy approach for decentralized economic-based allocation in Grid resource and service markets

Efficient resource allocation in dynamic large-scale environments is one of the challenges of Grids. In centralized economic-based allocation approaches, the user requests can be matched to the fastest, cheapest or most available resource. This approach, however, shows limitations in scalability and in dynamic environments. In this paper, we explore a decentralized economic approach for resource allocation in Grid markets based on the Catallaxy paradigm. Catallactic agents discover selling nodes in the resource and service Grid markets, and negotiate with each other maximizing their utility by following a strategy. By means of simulations, we evaluate the behavior of the approach, its resource allocation efficiency and its performance with different demand loads in a number of Grid density and dynamic environments. Our results indicate that while the decentralized economic approach based on Catallaxy applied to Grid markets shows similar efficiency to a centralized system, its decentralized operation provides greater advantages: scalability to demand and offer, and robustness in dynamic environments.     

Broker-based SLA-aware composite service provisioning

QoS-aware service composition aims to satisfy users’ quality of services (QoS) needs during service composition. Traditional methods simply attempt to maximize user satisfaction by provisioning the composite service instance with the best QoS. These “best-effort” methods fail to take into account that there also exist other consumers competing for the service resources and their decisions of service selection/composition can impact on QoS. Since user's QoS needs can be met once the demanded level is reached, in this paper, we propose an “on-demand” strategy for QoS-aware service composition to replace the traditional “best-effort” strategy. The service broker is introduced to facilitate implementation of this strategy: it first purchases a number of service instances for each component from providers and then provisions the composite services with different QoS classes to consumers. This paper focuses on how the broker follows the service level agreement (SLA) to provision composite services in the “on-demand” manner. This problem is formally expressed as the minimization of the QoS distance function between SLA and QoS of composite service instances, under a series of constraints. Heuristic approaches are proposed for the problem and experiments are conducted at last to verify their effectiveness and efficiency.     

ANEMA: Autonomic network management architecture to support self-configuration and self-optimization in IP networks

The increasing scale, technology advances and services of modern networks have dramatically complicated their management such that in the near future it will be almost impossible for human administrators to monitor them. To control this complexity, IBM has introduced a promising approach aiming to create self-managed systems. This approach, called Autonomic Computing, aims to design computing equipment able to self-adapt its configuration and to self-optimize its performance depending on its situation in order to fulfill high-level objectives defined by the human operator.In this paper, we present our autonomic network management architecture (ANEMA) that implements several policy forms to achieve autonomic behaviors in the network equipments. In ANEMA, the high-level objectives of the human administrators and the users are captured and expressed in terms of ‘Utility Function’ policies. The ‘Goal’ policies describe the high-level management directives needed to guide the network to achieve the previous utility functions. Finally, the ‘behavioral’ policies describe the behaviors that should be followed by network equipments to react to changes in their context and to achieve the given ‘Goal’ policies.In order to highlight the benefits of ANEMA architecture and the continuum of policies to introduce autonomic management in a multiservice IP network, a testbed has been implemented and several scenarios have been executed.     

对等网络中应用事件代理实现基于主题的情报分发*

针对集中式情报系统存在单点失效而导致整体失效、情报中心须事先确定,其角色和与系统拓扑过于紧耦合、以及信息融合能力不能满足多信息源的处理要求等问题,结合Petri网分析和描述情报分发过程,提出一种构建于对等网络之上的基于主题的自组织情报分发模型,并给出融合和分发核心算法。应用事件代理自组织成Gnutella P2P网络进行验证,结果显示系统能够较好解决以上问题,同时具有较高的时效。     

供需网环境下基于角色的工作流建模研究*

目前的工作流模型大多是基于过程建模的,并没有强调供需网环境下角色之间的交互关系。基于此,首先讨论了供需网中角色的概念及其属性;然后借鉴形式化和非形式化方式建模的优点,系统总结了模型建模的步骤,提出了供需网环境下基于角色的工作流模型;最后结合马尔可夫链理论验证了模型的稳定性。这种建模思想有利于扩大工作流管理系统的适用范围,保证供需网内部的安全性和稳定性。     

A three-phase methodology for developing or evaluating bank networks

One basic characteristic of modern society, besides the fast spread of information, is the increased use of banking services in order to fulfil consumer needs. Apart from their income, modern consumers rely to an increasing extent on banking services and money transactions as well, in order to buy consumer products and cover their financial needs. Thus the new standards of the ‘consumer society’ significantly affect the role and the importance of banking services. The end of the monopoly era characterized by increased competition leads banks to modify their strategies, to provide more and better services, to reorganize their structure and expand or merge their networks. In this context we propose a three‐phase methodology for developing and/or evaluating banking networks. The above methodology calculates an ‘attractiveness index’ for each area, and combines geographic information systems with demand covering models so as to propose the best network structure.     

Distributed on-demand routing for LEO satellite systems

Notwithstanding the limited commercial success of the first narrowband low earth orbit (LEO) satellite systems, the interest of the scientific community in this type of systems has been revived on the basis of the current trend toward the migration to all IP-based services. LEO systems can play a pivotal role in providing services to areas where there is no substantial terrestrial infrastructure. Above all, LEO satellite systems can be used as backbone networks to interconnect autonomous systems worldwide. Such an approach provides flexibility in managing the resulting integrated network infrastructure and supporting innovative applications. In this context, routing data from the source all the way to the destination constitutes a daunting challenge. In this paper, a location-assisted on-demand routing (LAOR) protocol is proposed and evaluated. The proposed protocol introduces for the first time in satellite systems the concept of on-demand routing. However, its implementation is tailored to the requirements imposed by the characteristics of the topology of LEO satellite systems. The performance of the LAOR protocol is assessed for different link-cost metrics and compared to the one of centralized routing protocols proposed in the literature so far. Simulation studies further document and confirm the positive characteristics of the proposed protocol.     

On-demand e-supply chain integration: A multi-agent constraint-based approach

With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. This strategic shift depends on the match between the demands and offerings that deliver the services. To achieve this, we need to coordinate the flow of information among the services, and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The real-world situation is complicated as a result of undetermined requirements of services involved in the chain, unpredictable solutions contributed by service providers, and dynamic selection and aggregation of solutions to services. This paper examines an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

Data protection and legal compliance in cloud computing

Cloud computing is Internet-based computing, where all IT-resources like software, data and other devices are provided on-demand. Cloud computing is a new way of delivering computing resources. Computing services ranging from data storage and processing to software, such as email handling, are now available instantly, commitment-free and on-demand. Since we are in a time of belt-tightening, this new economic model for computing has found fertile ground and is seeing massive global investment.     

On the Simulation of Grid Market Coordination Approaches

Grid computing has recently become an important paradigm for managing computationally demanding applications, composed of a collection of services. The dynamic discovery of services, and the selection of a particular service instance providing the best value out of the discovered alternatives, poses a complex multi-attribute n:m allocation decision problem, which is often solved using a central resource broker. However, decentralized approaches to this service allocation problem represent a much more flexible alternative, thus promising improvements in the efficiency of the resulting negotiations and service allocations. This paper compares centralized and decentralized service allocation mechanisms in Grid market scenarios according to a defined set of metrics.     

A game-theoretic approach to the financial benefits of infrastructure-as-a-service

Financial benefits are an important factor when cloud infrastructure is considered to meet processing demand. The dynamics of on-demand pricing and service usage are investigated in a two-stage game model for a monopoly Infrastructure-as-a-Service (IaaS) market. The possibility of hybrid clouds (public clouds plus own infrastructure) turns out to be essential in order that not only the provider but also the clients have significant benefits from on-demand services. Even if the client meets all demand in the public cloud, the threat of building a hybrid cloud keeps the instance price low. This is not the case when reserved instances are offered as well. Parameters like load profiles and economies of scale have a huge effect on likely future pricing and on a cost-optimal split-up of client demand between either a client’s own data center and a public cloud service or between reserved and on-demand cloud instances.     

A low-level resource allocation in an agent-based Cloud Computing platform

The distribution of computational resources in a Cloud Computing platform is a complex process with several parameters to consider such as the demand for services, available computational resources and service level agreements with end users. Currently, the state-of-the-art presents centralized approaches derived from previous technologies related to cluster of servers. These approaches allocate computational resources by means of the addition/removal of (physical/virtual) computational nodes. However, virtualization technology currently allows for research into new techniques, which makes it possible to allocate at a lower level. In other words, not only is it possible to add/remove nodes, but also to modify the resources of each virtual machine (low level resource allocation). Thus, agent theory is a key technology in this field, allowing decentralized resource allocation. This innovative approach has undeniable improvements such us computational load distribution and reduced computation time. The evaluation was carried out through experiments in a real Cloud environment, thus proving the validity of the proposed approach.     

IMS 重叠网络多路径传输内容分发机制

基于IMS的流媒体业务通常以集中方式为终端客户提供直播、点播等内容服务.然而集中式的服务模式并不能充分利用资源,还会因为流量过于集中而拒绝为更多终端提供服务,甚至会因为媒体服务器的失效而影响业务.提出了一种基于IMS重叠网络支持多路径传输的内容分发机制,在传统IMS网络中通过引入重叠网络实现资源的共享与分发,提高了网络资源利用率、降低了中心媒体服务器负载.实验表明与传统媒体服务器为中心的内容分发机制相比基于IMS重叠网络的内容分发机制具有显著优势.     

一种克服磁盘存取受限的视频点播机制

1 引言在视频服务器中,大量的待点播视频流数据以分级存储模式被加以组织,其中点播概率小的节目被存放在低速廉价的光盘库中作为非在线视频(off-line)。为保证视频的播送质量,非在线视频必须先存放到硬盘缓冲区中才能被播放。进一步考虑到网络传输性能因素,将视频服务器按层次化结构进行组织,将待点播的视频数据靠近用户存放,保证缓存的数据     

System Concept and Simulation Model for a Proposed Class of Delay-Tolerant Variably-Priced Network Services

We describe a system concept for therevenue-producing disposition of surplus capacity atoff-peak times in real trunking networks. The idea is toapproximate a competitive market for distribution of the networks' time-varying excess capacity with apricing strategy controlled by the network. The schemeis intended to allow network operators to stimulatebackground traffic loads to gain new revenues from otherwise idle time on existing installedresources. The concept is suitable for low prioritydelay-tolerant or opportunistic applications such asremote backups, software distribution, dispatchingbatched faxes, disseminating newsgroup updates,updating web page caches or routing tables. Backgroundservice subscribers are notified of price reductions atoff-peak times to elicit additional traffic for the network. Traffic aggregators act on behalf ofsubscribing organizations or groups of users. Thebackground service is completely subordinate to theconventional tariff-priced on-demand calling services and the variable background pricing merges withthe foreground under suitable total load. This paperfocuses on the network problem of price setting tocontinually maximize the price-volume product in a time-varying price-sensitive trafficenvironment such as this concept implies. Aprice-stimulated offered traffic environment issimulated in which time of day, price, and hidden demandlatency and demand curve characteristics all affect the offeredtraffic. An analytically optimum strategy is availablefor the particular traffic model used and theperformance of a fuzzy logic price controller is tested against the revenue-optimal strategy. Dependingon econometric assumptions for latent traffic demand andprice-volume curves, increases in revenue from 4%-20%are obtained in simulation of a 30-trunk group having a typical daily load pattern.     

Personal Rapid Transit in an open-control framework

Over the last decade, authorities have begun inquiring about the use of safe, comfortable, ecological vehicles for operation in an urban context as an alternative to private cars. Several on-demand transport projects have emerged with new automated vehicles known as cybercars or Personal Rapid Transit (PRT). Our state-of-the-art survey of the literature about automated On-Demand Transport (ODT) control solutions highlighted the desirability of a decentralized approach, although centralized approaches do have some advantages. In order to benefit from the advantages of both centralized/hierarchical and decentralized/heterarchical control approaches, we propose a new concept of control: open-control. In this paper, the context is intelligent transportation, where vehicles (e.g., PRTs) can be seen as autonomous decisional entities that are part of a transport system. In this context, the open-control concept is used to support two solutions to PRT routing with uncertainty and perturbations. This open-control concept, developed in our lab, exhibits the traditional explicit control, as well as an innovative type of control called implicit control, which allows system entities to be influenced via an Optimization Mechanism (OM). After introducing the open-control paradigm, we illustrate two applications of the implicit control of a PRT fleet, one based on a stigmergic method and the second based on an embedded version of the Dijkstra’s algorithm. We present a real implementation of the second approach applied to an experimental PRT network. We describe our experimental platform for PRT control and report our first experimental results. These experiments clearly show the reactivity of the control faced with unpredictable events, such as path perturbation or dynamic insertion of PRT in the network.     

基于云计算的高校实验室公用机管理方案

云计算的五个基本特征是:自我按需服务、宽带网络、资源池、快速可扩展性和服务质量跟踪。文章介绍基于云计算五大特征的高校实验室公用机管理系统方案、系统应用及其效果。