Volume 8, Number 3, 2011
Special Issue on Cloud Computing (pp.269-308)
Display Mode： |
Cloud computing has attracted great interest from both academic and industrial communities. Different paradigms, architectures and applications based on the concept of cloud have emerged. Although many of them have been quite successful, efforts are mainly focusing on the study and implementation of particular setups. However, a generic and more flexible solution for cloud construction is missing. In this paper, we present a composition-based approach for cloud computing (compositional cloud) using Imperial College Cloud (IC Cloud) as a demonstration example. Instead of studying a specific cloud computing system, our approach aims to enable a generic framework where various cloud computing architectures and implementation strategies can be systematically studied. With our approach, cloud computing providers/adopters are able to design and compose their own systems in a quick and flexible manner. Cloud computing systems will no longer be in fixed shapes but will be dynamic and adjustable according to the requirements of different application domains.
With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtualized resources are provided as services. With virtualization technology, cloud computing offers diverse services (such as virtual computing, virtual storage, virtual bandwidth, etc.) for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users' embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.
Virtual machines have attracted significant attention especially within the high performance computing community. However, there remain problems with respect to security in general and intrusion detection and diagnosis in particular which underpin the realization of the potential offered by this emerging technology. In this paper, one such problem has been highlighted, i.e., intrusion severity analysis for large-scale virtual machine based systems, such as clouds. Furthermore, the paper proposes a solution to this problem for the first time for clouds. The proposed solution achieves virtual machine specific intrusion severity analysis while preserving isolation between the security module and the monitored virtual machine. Furthermore, an automated approach is adopted to significantly reduce the overall intrusion response time. The paper includes a detailed description of the solution and an evaluation of our approach with the objective to determine the effectiveness and potential of this approach. The evaluation includes both architectural and experimental evaluation thereby enabling us to strengthen our approach at an architectural level as well. Finally, open problems and challenges that need to be addressed in order to make further improvements to the proposed approach have been highlighted.
Cloud computing can be realized by service interoperation and its essence is to provide cloud services through network. The development of effective methods to assure the trustworthiness of service interoperation in cloud environment is a very important problem. The essence of cloud security is trust and trust management. Combining quality of service (QoS) with trust model, this paper constructs a QoS-aware and quantitative trust-model that consists of initial trust value, direct trust value, and recommendatory trust value of service, making the provision, discovery, and aggregation of cloud services trustworthy. Hence, it can assure trustworthiness of service interoperation between users and services or among services in cloud environment. At the same time, based on this model, service discovery method based on QoS-aware and quantitative trust-model (TQoS-WSD) is proposed, which makes a solid trust relationship among service requestor, service provider and service recommender, and users can find trustworthy service whose total evaluation value is higher. Compared to QoS-based service discovery (QoS-WSD) method, it is proved by the experiment for TQoS-WSD method that more accurate result of service discovery will be achieved by service requestor, while reasonable time cost is increased. Meanwhile, TQoS-WSD method strongly resists the effect of service discovery by untrustworthy QoS values and improves service invocation success-rate and thus assures trustworthiness of services interoperation.
The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system (FACTS) in this paper is an advanced super-conducting magnetic energy storage (ASMES). Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller (FMRLC) is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters (i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met). Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system (i.e., single machine-infinite bus (SMIB) system), under various fault conditions and disturbances.
For homogeneous charge compression ignition (HCCI) combustion, the auto-ignition process is very sensitive to in-cylinder conditions, including in-cylinder temperature, in-cylinder components and concentrations. Therefore, accurate control is required for reliable and efficient HCCI combustion. This paper outlines a simplified gasoline-fueled HCCI engine model implemented in Simulink environment. The model is able to run in real-time and with fixed simulation steps with the aim of cycle-to-cycle control and hardware-in-the-loop simulation. With the aim of controlling the desired amount of the trapped exhaust gas recirculation (EGR) from the previous cycle, the phase of the intake and exhaust valves and the respective profiles are designed to vary in this model. The model is able to anticipate the auto-ignition timing and the in-cylinder pressure and temperature. The validation has been conducted using a comparison of the experimental results on Ricardo Hydro engine published in a research by Tianjin University and a JAGUAR V6 HCCI test engine at the University of Birmingham. The comparison shows the typical HCCI combustion and a fair agreement between the simulation and experimental results.
One of the major tasks in a molecular dynamics (MD) simulation is the selection of adequate potential functions, from which forces are derived. If the potentials do not model the behaviour of the atoms correctly, the results produced from the simulation would be useless. Three popular potentials, namely, Lennard-Jones (LJ), Morse, and embedded-atom method (EAM) potentials, were employed to model copper workpiece and diamond tool in nanometric machining. From the simulation results and further analysis, the EAM potential was found to be the most suitable of the three potentials. This is because it best describes the metallic bonding of the copper atoms; it demonstrated the lowest cutting force variation, and the potential energy is most stable for the EAM.
Simultaneous perturbation stochastic approximation (SPSA) belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation (FASPA) algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks (NNs). Accordingly, a multilayer perceptron (MLP) network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
This paper presents an optimisation-based verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated: genetic algorithms (GAs) and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.
In this paper, a self contained capsubot (capsule robot) propulsion mechanism is investigated. The proposed capsubot works on the principle of internal force-static friction. A modified linear DC motor is used to drive the capsubot. A novel acceleration profile is proposed for the moving part (linear cylinder) based on the principle. A significant feature of the proposed capsubot is that it is legless, wheelless, and trackless. The developed capsubot with a proposed propulsion mechanism demonstrates a very good average velocity. The propulsion mechanism has the potential to be used for the propulsion of a wireless-controlled self-propelling capsule endoscope. Simulation and experimental results demonstrate the performance of the self-contained capsubot with the proposed acceleration profile.
In this paper, the stabilization problem is considered for the class of wireless networked control systems (WNCS). An indicator is introduced in the WNCS model. The packet drop sequences in the indicator are represented as states of a Markov chain. A new discrete Markov switching system model integrating 802.11 protocol and new scheduling approach for wireless networks with control systems are constructed. The variable controller can be obtained easily by solving the linear matrix inequality (LMI) with the use of the Matlab toolbox. Both the known and unknown dropout probabilities are considered. Finally, a simulation is given to show the feasibility of the proposed method.
This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on the information set on which consumers and merchants base their subscription decisions. In the studied case, we observe that a market with few cards dominating only emerges when decisions are based on very limited information. Under the same conditions using a complete information set, all cards survive in the long run. The use of an agent-based model, focusing on the interactions between merchants and consumers, as a basis for subscription decisions allows us to investigate the dynamics of the market and the effect of the indirect network externalities rather than investigating only equilibrium outcomes.
This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.
【Special Collection】Top Articles by Academicians and Fellows
IJAC collects all the articles written by world-famous academicians and fellows. Don’t miss it!
2019 International Academic Conference List
International Journal of Automation and Computing (IJAC) maintains this list of conferences at the beginning of each year that are highly relevant to current hot research topics, including artificial intelligence, machine learning, computer vision, pattern recognition, robotics and automatic control.
ICAC'19 Call for Papers
Paper submission deadline：30 April 2019