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).     

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.     

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.     

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。     

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.     

A SAS-based solution to evaluate study design efficiency of phase I pediatric oncology trials via discrete event simulation

Previous exploration of oncology study design efficiency has focused on Markov processes alone (probability-based events) without consideration for time dependencies. Barriers to study completion include time delays associated with patient accrual, inevaluability (IE), time to dose limiting toxicities (DLT) and administrative and review time. Discrete event simulation (DES) can incorporate probability-based assignment of DLT and IE frequency, correlated with cohort in the case of DLT, with time-based events defined by stochastic relationships. A SAS-based solution to examine study efficiency metrics and evaluate design modifications that would improve study efficiency is presented. Virtual patients are simulated with attributes defined from prior distributions of relevant patient characteristics. Study population datasets are read into SAS macros which select patients and enroll them into a study based on the specific design criteria if the study is open to enrollment. Waiting times, arrival times and time to study events are also sampled from prior distributions; post-processing of study simulations is provided within the decision macros and compared across designs in a separate post-processing algorithm. This solution is examined via comparison of the standard 3 + 3 decision rule relative to the “rolling 6” design, a newly proposed enrollment strategy for the phase I pediatric oncology setting.     

基于云计算的图书馆云服务模型研究

通过借鉴OCLC在云计算图书馆的设计思路以及基于云计算整合图书馆资源与日常服务的办法,提出了在云计算图书馆云服务的构建思路及其模型。     

Diagnosability of fuzzy discrete event systems

In this paper, discrete event systems (DESs) are reformulated as fuzzy discrete event systems (FDESs) and fuzzy discrete event dynamical systems (FDEDSs). These frameworks include fuzzy states, events and IF-THEN rules. In these frameworks, all events occur at the same time with different membership degrees. Fuzzy states and events have been introduced to describe uncertainties that occur often in practical problems, such as fault diagnosis applications. To measure a diagnoser’s fault discrimination ability, a fuzzy diagnosability degree is proposed. If the diagnosability of the degree of the system yields one a diagnoser can be implemented to identify all possible fault types related to a system. For any degree less than one, researchers should not devote their time to distinguish all possible fault types correctly. Thus, two different diagnosability definitions FDEDS and FDES are introduced. Due to the specialized fuzzy rule-base embedded in the FDEDS, it is capable of representing a class of non-linear dynamic system. Computationally speaking, the framework of diagnosability of the FDEDS is structurally similar to the framework of diagnosability of a non-linear system. The crisp DES diagnosability has been turned into the term fuzzy diagnosability for the FDES. The newly proposed diagnosability definition allows us to define a degree of diagnosability in a class of non-linear systems. In addition, a simple fuzzy diagnosability checking method is introduced and some numerical examples are provided to illustrate this theoretical development. Finally, the potential applications of the proposed method are discussed.     

Real time identification of discrete event systems using Petri nets

The paper defines the identification problem for Discrete Event Systems (DES) as the problem of inferring a Petri Net () model using the observation of the events and the available output vectors, that correspond to the markings of the measurable places. Two cases are studied considering different levels of the system knowledge. In the first case the place and transition sets are assumed known. Hence, an integer linear programming problem is defined in order to determine a modelling the DES. In the second case the transition and place sets are assumed unknown and only an upper bound of the number of places is given. Hence, the identification problem is solved by an identification algorithm that observes in real time the occurred events and the corresponding output vectors. The integer linear programming problem is defined at each observation so that the can be recursively identified. Some results and examples characterize the identified systems and show the flexibility and simplicity of the proposed technique. Moreover, an application to the synthesis of supervisory control of systems via monitor places is proposed.     

EFADS: Efficient,flexible and anonymous data sharing protocol for cloud computing with proxy re-encryption

The concept of cloud computing has emerged as the next generation of computing infrastructure to reduce the costs associated with the management of hardware and software resources. It is vital to its success that cloud computing is featured efficient, flexible and secure characteristics. In this paper, we propose an efficient and anonymous data sharing protocol with flexible sharing style, named EFADS, for outsourcing data onto the cloud. Through formal security analysis, we demonstrate that EFADS provides data confidentiality and data sharer's anonymity without requiring any fully-trusted party. From experimental results, we show that EFADS is more efficient than existing competing approaches. Furthermore, the proxy re-encryption scheme we propose in this paper may be independent of interests, i.e., compared to those previously reported proxy re-encryption schemes, the proposed scheme is the first pairing-free, anonymous and unidirectional proxy re-encryption scheme in the standard model.     

Model-driven auto-scaling of green cloud computing infrastructure

Cloud computing can reduce power consumption by using virtualized computational resources to provision an application’s computational resources on demand. Auto-scaling is an important cloud computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling infrastructure to create greener computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach.     

MDP based optimal pricing for a cloud computing queueing model

We study the optimization of dynamic pricing in a queueing model with a finite buffer, where arrival rates depend on advertised price levels. We apply our study to a pricing policy in a cloud computing service provider setup.The main result of this paper is the multi-threshold structure of the optimal policy.