A conceptual modeling framework for discrete event simulation using hierarchical control structures

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.     

A modeling and simulation framework for mobile cloud computing

Mobile cloud computing (MCC) is an emerging paradigm for transparent elastic augmentation of mobile devices capabilities, exploiting ubiquitous wireless access to cloud storage and computing resources. MCC aims at increasing the range of resource-intensive tasks supported by mobile devices, while preserving and extending their resources. Its main concerns regard the augmentation of energy efficiency, storage capabilities, processing power and data safety, to improve the experience of mobile users. The design of MCC systems is a challenging task, because both the mobile device and the Cloud have to find energy-time tradeoffs and the choices on one side affect the performance of the other side. The analysis of the MCC literature points out that all existing models focus on mobile devices, considering the Cloud as a system with unlimited resources. Also, to the best of our knowledge, no MCC-specific simulation tool exists. To fill this gap, in this paper, we propose a modeling and simulation framework for the design and analysis of MCC systems, encompassing all their components. The main pillar of the proposed framework is the autonomic strategy consisting of adaptive loops between every mobile devices and the Cloud. The proposed model of the mobile device takes into account online estimations of the actual Cloud performance – not only the nominal values of the performance indicators. At the same time, the model of the Cloud takes into consideration the characteristics of the workload, to adapt its configuration in terms of active virtual machines and task management strategies. Moreover, the developed discrete event simulator is an effective tool for the evaluation of an MCC system as a whole, or single components, considering different classes of parallel jobs.     

An autonomic cloud environment for hosting ECG data analysis services

Advances in sensor technology, personal mobile devices, wireless broadband communications, and Cloud computing are enabling real-time collection and dissemination of personal health data to patients and health-care professionals anytime and from anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful in terms of processing capabilities and information management and play a major role in peoples daily lives. This technological advancement has led us to design a real-time health monitoring and analysis system that is Scalable and Economical for people who require frequent monitoring of their health. In this paper, we focus on the design aspects of an autonomic Cloud environment that collects peoples health data and disseminates them to a Cloud-based information repository and facilitates analysis on the data using software services hosted in the Cloud. To evaluate the software design we have developed a prototype system that we use as an experimental testbed on a specific use case, namely, the collection of electrocardiogram (ECG) data obtained at real-time from volunteers to perform basic ECG beat analysis.     

Web Services with generic simulation models for discrete event simulation

Today the Internet and the World Wide Web (WWW) are on the cusp of a paradigm shift. Up to now most actions in the WWW are sorts of human–computer interaction, but the introduction of the eXtensible Markup Language (XML) changed the perception. The Internet will be seen as a great space of information and with the use of XML and following technologies like Web Services, Grid Computing and Semantic Web the difference between human–machine interaction and machine–machine interaction vanishes. This work investigates the usefulness of XML in the simulation domain and uses Web Service technology to build the SimASP framework for discrete event simulation (DES).     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.     

Predictive analytics using statistical,learning, and ensemble methods to support real-time exploration of discrete event simulations

Discrete event simulations (DES) provide a powerful means for modeling complex systems and analyzing their behavior. DES capture all possible interactions between the entities they manage, which makes them highly expressive but also compute-intensive. These computational requirements often impose limitations on the breadth and/or depth of research that can be conducted with a discrete event simulation.This work describes our approach for leveraging the vast quantity of computing and storage resources available in both private organizations and public clouds to enable real-time exploration of discrete event simulations. Rather than directly targeting simulation execution speeds, we autonomously generate and execute novel scenario variants to explore a representative subset of the simulation parameter space. The corresponding outputs from this process are analyzed and used by our framework to produce models that accurately forecast simulation outcomes in real time, providing interactive feedback and facilitating exploratory research.Our framework distributes the workloads associated with generating and executing scenario variants across a range of commodity hardware, including public and private cloud resources. Once the models have been created, we evaluate their performance and improve prediction accuracy by employing dimensionality reduction techniques and ensemble methods. To make these models highly accessible, we provide a user-friendly interface that allows modelers and epidemiologists to modify simulation parameters and see projected outcomes in real time.     

Improving efficiency in social services with discrete event simulation

The Kentucky Cabinet for Health and Family Services’ main office in Louisville, Kentucky deals with hundreds of clients with various needs each day. The office provides food stamps, child care assistance, and financial support among other services. A simulation model of the intake process was developed and analyzed in order to investigate various alternatives concerning process flow, staffing, and layout. The simulation model shows long wait times for clients coming into the system to receive benefits. Working with a state budget during variable economic times leaves no room for hiring new workers, so changes must come from within. The flexibility of the simulation model allows for experiments on the system to be created and observed. This paper describes the simulation model, the analysis of alternatives with the model, and the changes implemented in the system as a result of the analysis.     

Using discrete event simulation to model excavator operator performance

The traditional design process of fluid power systems such as hydraulic excavators has placed much emphasis on technical performance rather than human components. This research aims to develop human‐performance models to assess operator performance and human interaction during excavation processes. Task analysis, time studies, and statistical distributions were developed into task‐network models and imbedded into four Micro Saint simulation models with regard to various expertise and control types. An empirical study was conducted using the simulation models. Results indicated that both expertise and control type had a significant impact on operator performance, resulting in both time and consistency differences at various points during excavation processes. Models also revealed implications of operator fatigue leading to stress for the operator. Recommendations suggest that designers consider the placement of controls and measures to reduce operator workload for better performance in future systems. © 2010 Wiley Periodicals, Inc.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

Application of discrete event simulation for robust system design of a melt facility

This paper presents a methodology to identify robust operating regions through the selection of controllable factory variables, using discrete event simulation. A casting plant melt facility was used as an industrial test bed to develop these techniques. A robust system design was determined by response surface analysis of key production parameters. Furthermore, robust operating policies that maximise throughput, while minimizing work-in-progress and thus energy consumption were identified.     

Parallel discrete event simulation with SIMULA

The area of Discrete Event Simulation (DES) is the least impacted by parallel processing even though most of its applications require tremendous amounts of processing time. The common approach of parallelizing individually special purpose programs leads to very limited improvements in performance. We propose here the parallelization of general DES applications written in SIMULA, as a part of an ongoing project that aims towards developing methodologies and architectures for parallel DES. SIMULA is a general purpose process oriented language whose structure allows the creation of processes which can participate in a quasi-parallel execution according to an interleaved fashion. Problems related to the process interference representation, parallel scheduling and process synchronization are defined and solved. To test and verify the theoretical results the parallel execution of experimental and real DES applications has been simulated. The results show that significant improvement in performance can be expected.     

Active fault tolerant control of discrete event systems using online diagnostics

The aim of this paper is to deal with the problem of fault tolerant control in the framework of discrete event systems modeled as automata. A fault tolerant controller is a controller able to satisfy control specifications both in nominal operation and after the occurrence of a fault. This task is solved by means of a parameterized controller that is suitably updated on the basis of the information provided by online diagnostics: the supervisor actively reacts to the detection of a malfunctioning component in order to eventually meet degraded control specifications. Starting from an appropriate model of the system, we recall the notion of safe diagnosability as a necessary step in order to achieve fault tolerant control. We then introduce two new notions: (i) “safe controllability”, which represents the capability, after the occurrence of a fault, of steering the system away from forbidden zones and (ii) “active fault tolerant system”, which is the property of safely continuing operation after faults. Finally, we show how the problem can be solved using a general control architecture based on the use of special kind of diagnoser, called “diagnosing controller”, which is used to safely detect faults and to switch between the nominal control policy and a bank of reconfigured control policies. A simple example is used to illustrate the new notions and the control architecture introduced in the paper.     

Using Ada for discrete event simulation

The process interaction approach is proposed for developing a discrete simulation environment in Ada. The introduction of simulation facilities in Ada not only concerns the classical aspect of model building, but allows a new class of problems to be tackled, that is the testing of correctness of programs intended for real-time applications. In this paper attention is focused on the presentation of the process scheduling in the simulation context and on the definition of standard forms of interactions among processes. Simulation facilities are organized by making use of Ada's structuring concepts.     

GRAPHIC illustrations of autonomic computing concepts

Illustrations are general aids to communication, with those that are particularly effective becoming standard exemplars. In the computing domain, for instance, frequent reference is made to lift controllers, automated bank teller machines, and car parks. So far, there are few equivalent illustrations for autonomic systems. The purpose of this paper is to clarify the requirements for standard exemplars, propose an autonomic computing exemplar for consideration, and assess it against the criteria identified. The exemplar described, an autonomic printing service, aims to be GRAPHIC: general, realistic, appealing, powerful, holistic, informative, and comprehensible. Experience in implementing a demonstration printing service with autonomic characteristics is also briefly discussed.     

基于云计算的网络安全的探析

随着云计算的快速的发展,越来越多的用户将应用和数据托管到了云端。但是由于云计算具有的虚拟化、多用户、可伸缩等新特性,使得传统的安全技术并不能保证云计算的安全,云计算中的安全问题成了阻碍云计算发展的最主要的问题之一。因此,云计算的安全性成为了当前研究云计算的重点。     

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter‐networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter‐networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy‐efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.     

数字图书馆云计算安全分析及管理方法研究

云计算技术在近几年快速兴起,并且快速应用到图书馆系统中来,但随之带来的还有安全问题.本文就云时代的数字图书馆所面临的安全问题进行了阐述,并且就如何解决这些安全问题进行了细致的研究.     

Evaluation of carsharing network’s growth strategies through discrete event simulation

Carsharing organizations are nowadays faced with the emergence of new markets due to the growing popularity of their services. To keep up with the growing demand, they have to constantly adapt their network and balance their stations’ capacities by implementing new strategies. These strategies involve creation of new carsharing stations, increasing the capacity of stations, merging or demerging carsharing stations etc. Currently, the decision makers rely on an intuitive strategy selection process which often results in inadequate decisions being made representing an immediate loss in resources, time and market penetration. This paper presents a discrete event simulation based decision support tool that assists the decision makers in selecting best network growth strategies to implement for meeting adequately the demand growth while maximizing the members’ satisfaction level and minimizing the number of vehicles used. Our discrete event simulation model allows modeling the activities at any given set of carsharing stations, regardless of their number and capacities. A benchmarking comparison of different potential strategies is done. An application of the proposed model on a region of Communauto’s Montréal (Québec, Canada) carsharing network is provided.     

Efficient multi-keyword ranked query over encrypted data in cloud computing

Cloud computing infrastructure is a promising new technology and greatly accelerates the development of large scale data storage, processing and distribution. However, security and privacy become major concerns when data owners outsource their private data onto public cloud servers that are not within their trusted management domains. To avoid information leakage, sensitive data have to be encrypted before uploading onto the cloud servers, which makes it a big challenge to support efficient keyword-based queries and rank the matching results on the encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. In the current multi-keyword ranked search approach, the keyword dictionary is static and cannot be extended easily when the number of keywords increases. Furthermore, it does not take the user behavior and keyword access frequency into account. For the query matching result which contains a large number of documents, the out-of-order ranking problem may occur. This makes it hard for the data consumer to find the subset that is most likely satisfying its requirements. In this paper, we propose a flexible multi-keyword query scheme, called MKQE to address the aforementioned drawbacks. MKQE greatly reduces the maintenance overhead during the keyword dictionary expansion. It takes keyword weights and user access history into consideration when generating the query result. Therefore, the documents that have higher access frequencies and that match closer to the users’ access history get higher rankings in the matching result set. Our experiments show that MKQE presents superior performance over the current solutions.