An efficient server bandwidth costs decreased mechanism towards mobile devices in cloud-assisted P2P-VoD system

Recently, more and more devices with small buffer size such as PDAs or mobile phones are joining in the VoD system, which leads to two major challenges: how to efficiently distribute their bandwidth resources with small buffer size, and how to provide assistant mechanism to make them playback smoothness. In face of this situation and for the purpose of decreasing the server bandwidth costs, we propose a peers’ downloading mechanism called NCDLT to solve above challenges. It contains two algorithms. The first is neighbors and chunks downloading selection (NCS) algorithm and it ensures peers to find neighbors who can provide video data with lower refusal rate. The second is distributed linear taxation algorithm (DLT) and it makes peers with lower capability acquire enough download rate to reduce the request to servers. The simulation results demonstrate that our algorithms can offload the server bandwidth costs and improve the download rate of peers with small buffer size.     

视频点播中视频服务器节目替换算法研究

提出了一种分布集群式的视频点播体系结构,针对视频点播系统特点设计了两种适用于系统不同的运行阶段的视频服务器节目替换算法。在视频点播系统初始化时间段内使用改进的LFRU算法进行节目替换;系统达到稳定状态后使用最小加权周期频率替换算法进行节目替换。对比实验表明两种替换算法适合分布集群式视频点播系统,其替换效率较高。     

SipaaS: Spot instance pricing as a Service framework and its implementation in OpenStack

Designing dynamic pricing mechanisms that efficiently price resources in line with a provider's profit maximization goal is a key challenge in cloud computing environments. Despite the large volume of research published on this topic, there is no publicly available software system implementing dynamic pricing for Infrastructure as a Service cloud spot markets. This paper presents the implementation of a framework called Spot instance pricing as a Service (SipaaS) that supports an auction mechanism to price and allocate virtual machine instances. SipaaS is an open‐source project offering a set of web services to price and sell virtual machine instances in a spot market resembling the Amazon EC2 spot instances. Cloud providers, who aim at utilizing SipaaS, should install add‐ons in their existing platform to make use of the framework. As an instance, we provide an extension to the Horizon – the OpenStack dashboard project – to employ SipaaS web services and to add a spot market environment to OpenStack. To validate and evaluate the system, we conducted an experimental study with a group of 10 users utilizing the provided spot market in a real environment. Results show that the system performs reliably in a practical test environment. Copyright © 2015 John Wiley & Sons, Ltd.     

IPSS: An approach towards automated decisions in CIM systems

IPSS (Industrial Problem Solving System), a specialized prototype Expert System development environment for operations planning. This hybrid system combines features of knowledge based systems as well as conventional procedural knowledge. IPSS architecture is presented and applications are discussed.     

Human migration networks and policy interventions: bringing population distributions in line with system optimization

In this paper, we demonstrate that, through policy interventions, in the form of subsidies, a system‐optimum for a multiclass human migration network can be achieved, despite the migrants, who can be refugees, behaving in a user‐optimized manner. The formulation and analysis are conducted using variational inequality theory. The policy intervention allows governmental decision‐makers to moderate the flow of migrants while enhancing societal welfare. An algorithm is proposed and applied to compute the solutions to a series of numerical examples, with changes in initial populations and utility functions, inspired by a pandemic, followed by a natural disaster.     

Maintenance considerations in CIM design: An expert system perspective

An unmanned manufacturing system (UMS) will typically be characterized by a large number of highly reliable components which are interconnected in a highly complex fashion. These characteristics suggest: (1) there will be a large number of ways in which a UMS can fail; (2) the mean time between repeat occurrences of a given type of problem will be quite large; and (3) the time and place where a failure creates an observable symptom may be quite different from the time and place where the failure occurred. This suggests that an UMS could be difficult to maintain. Maintenance personnel obviously need assistance for dealing with the complex cognitive problems which can arise in troubleshooting the UMS. This paper describes a procedure for developing an expert system to provide this assistance. Simulation is utilized to generate proxy data on how the system could be expected to behave when a given type of failure occurs. Transfer-function/intervention analysis is utilized to develop models which describes the system transient response to the various types of failures. Failure problems are diagnosed by comparing the shape of the actual transient response with the shapes of transient responses which correspond to the various types of failures.     

A decision support system for strategic planning in large-scale marketing channels: An example in the tile industry

This paper presents a decision support system for strategic planning in marketing channels. A dynamic model of the marketing channel is employed which comprises manufacturer and retailer levels. Decision making is achieved through a game theoretical inference mechanism in which each player (manufacturer/retailer) optimises for a long-term profit maximisation objective. Both historical data and managerial expertise are used for the parameterisation of the system's knowledge base. The decision support system provides a forecast of profit and sales and computes pricing and shelf space allocation strategies that maximise long-term profit. It offers facilities such as the study of coalitions, long-term decision-making in all phases of a product's life cycle, the impact of pricing, allocation strategies, production expansion, cost regulation, and others. The operationality of the system is illustrated in decision-making situations in the tile industry.     

An organizational approach to designing an intelligent knowledge-based system: Application to the decision-making process in design projects

Knowledge-based engineering (KBE) approaches are designed to reduce the time and cost of product development by capturing, retaining and re-using design knowledge. They currently focus on repetitive design tasks where knowledge is considered as a static resource. However, knowledge is intrinsically linked to the organizations and people who use it. Thus, to be efficient, these knowledge-based systems (KBS) have to be able to take into account all the mechanisms of knowledge creation, sharing and evaluation made by the users. Using the agent paradigm, new knowledge-based systems can be designed in order to address this research issue. Indeed, the agents have social abilities and are able to achieve very complex tasks. These two features are necessary for making a knowledge-based system efficient. However, there still exists today a lack of approaches and methodologies to help design such applications. This paper presents DOCK, a methodology to design an intelligent knowledge-based system that aims to support the knowledge management process. In order to take into account all the mechanisms of knowledge generation, sharing and re-use, DOCK is based on the hypothesis that efficient modelling of human organizations, by highlighting their roles, collaborations, skills, goals and knowledge, will help the KBS designer to specify an adapted knowledge-based system. Finally, DOCK is implemented to design the SMA SNOTRA that is dedicated to supporting a decision-making process for design projects.     

Electricity storage systems in the future German energy sector: An optimization of the German electricity generation system until 2040 considering grid restrictions

Due to the growing feed-in of electricity based on renewables, electricity storage systems will be essential in the future energy sector. Because of the volatile feed-in, electricity will have to be shifted temporally. Additionally, load centers and regions of potentially high wind-based electricity production are located far away from each other in Germany, resulting in the need to transport electricity from the north to the south. According to the targets defined by the German government, more than 60% of electricity generation in 2040 is to be based on renewables. A strategic allocation of storage systems might help to improve the utilization of grid capacities and integrate renewables at the same time. To analyze this, we implemented the possibility to commission storage systems throughout Germany in the energy system model PERSEUS-NET-ESS. This investment and dispatch model includes a DC approach of the German transmission grid and, thus, calculates not only the installed capacities, but also their optimal allocation. Besides storage systems, gas turbines or load shift potentials can be used for the integration of renewables. In this paper, we use PERSEUS-NET-ESS to evaluate the alternatives taking the grid restrictions into account. Results indicate that it is beneficial to commission about 3.2 GW of battery storage systems until 2040, provided that storage investment will drop to about 150 €/kWh until then. The main part of the capacity is to be deployed in northern Germany close to the sea, where electricity from off-shore wind parks will be fed into the grid. At the same time, the storage systems will be located mainly close to congested grid lines. For the case of battery storage systems being impossible in the model, gas turbines are commissioned instead. Modeling will also consider the load shift potential due to electric mobility. It can substitute almost all of the commissioned storage systems and at the same time reduce the total generation capacity needed.