Volume 13, Number 6, 2016
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The rapid progress of cloud technology has attracted a growing number of video providers to consider deploying their streaming services onto cloud platform for more cost-effective, scalable and reliable performance.In this paper, we utilize Markov decision process model to formulate the dynamic deployment of cloud-based video services over multiple geographically distributed datacenters.We focus on maximizing the average profits for the video service provider over a long run and introduce an average performance criteria which reflects the cost and user experience jointly.We develop an optimal algorithm based on the sensitivity analysis and sample-based policy iteration to obtain the optimal video placement and request dispatching strategy.We demonstrate the optimality of our algorithm with theoretical proof and specify the practical feasibility of our algorithm.We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee users quality of experience (QoE).
In order to fully utilize all potential available network resources and make the interoperability of systems possible, we propose to integrate cloud computing and peer-to-peer (P2P) computing environments together.We utilize the mobile multi-agent technology to construct an effective hierarchical integration model named Cloud-P2P.As the original management mechanisms for traditional cloud computing and P2P computing systems are no longer applicable to Cloud-P2P, we propose a novel hybrid collaborative management ring based on mobile multi-agent in order to ensure the efficiency and success rate of task implementation in the CloudP2P environment.This mechanism needs to divide the system into core ring, cloud inner rings and several peer rings.In each ring, every node is in collaboration with its neighbor nodes with multi-agent, or uses mobile agent moving from node to node with string or parallel methods to monitor the statuses and performances of all nodes, in order to avoid problems of performance bottleneck and single point failure.This paper analyses the node conditions of cloud computing and P2P computing environments in-depth, then elaborates on Cloud-P2P and the hybrid collaborative management ring based on mobile multi-agent (HCMRMMA).After that, the construction method of the network ring topology for Cloud-P2P is introduced.Finally, experimental results and performance analysis of HCMRMMA are presented.
Architecture description languages play an important role in modelling software architectures.However, many architecture description languages (ADLs) are either unable to deal with the verification and dynamic changes directly or too formal to be understood and manipulated.This paper presents xBreeze/ADL, a novel extensible markup language (XML)-based verification and evolution supported architecture description language, which is specifically designed for modelling the software architecture of large, complex systems.Five principle design goals are 1) to separate template from instance to define a loose coupling structure, 2) to present virtual and concrete link to identify service execution flow, 3) to clearly represent component behaviour to specify architecture semantics, 4) to introduce multi-dimension restrictions to define the architecture constraints, and 5) to use the graph transformation theory to implement the architecture configuration management (i.e., reconfiguration and verification).Various advanced features of xBreeze/ADL are illustrated by using an example on online movie ticket booking system.
Visual object tracking plays an important role in intelligent aerial surveillance by unmanned aerial vehicles (UAV).In ordinary applications, aerial videos are captured by cameras with a fixed-focus lens or a zoom lens, for which the field-of-view (FOV) of the camera is fixed or smoothly changed.In this paper, a special application of the visual tracking in aerial videos captured by the dual FOV camera is introduced, which is different from ordinary applications since the camera quickly switches its FOV during the capturing.Firstly, the tracking process with the dual FOV camera is analyzed, and a conclusion is made that the critical part for the whole process depends on the accurate tracking of the target at the moment of FOV switching.Then, a cascade mean shift tracker is proposed to deal with the target tracking under FOV switching.The tracker utilizes kernels with multiple bandwidths to execute mean shift locating, which is able to deal with the abrupt motion of the target caused by FOV switching.The target is represented by the background weighted histogram to make it well distinguished from the background, and a modification is made to the weight value in the mean shift process to accelerate the convergence of the tracker.Experimental results show that our tracker presents a good performance on both accuracy and efficiency for the tracking.To the best of our knowledge, this paper is the first attempt to apply a visual object tracking method to the situation where the FOV of the camera switches in aerial videos.
Cloud computing is one of the fastest growing and popular computer technologies, and there are more and more enterprise services based on the cloud computing.In order to save costs, more and more enterprises and their employees have hired the enterprise cloud services, and put much important information in the cloud gradually.Cloud service systems have become the main targets of malicious attacks.However, the cloud computing technologies are still not perfect, and the management and maintenance of enterprise cloud services are more complex compared to traditional network services of cloud computing.So, enterprise cloud services are more likely to encounter some security problems, and the influenced scale of these security problems is broad.But there are few researches on the security of enterprise cloud services.In this paper, we analyze the software as a service (SaaS) enterprise cloud services and introduce the research status of security problems in cloud computing environment.Combining with the analysis of the characteristics and application architecture of SaaS enterprise cloud services, we propose the security problems analysis model, the analysis system architecture and the relational model.Our researches can support further research of the automatic generation of solutions and guide the deployment of security policies of SaaS enterprise cloud services.
This paper presents control strategies for finite-time stabilization of a class of nonholonomic dynamic systems with unknown virtual control coefficients and system parameters.The minimal dilation degree technique and the terminal sliding mode control scheme with finite-time convergence are used to design the controllers.The systematic control strategy development involves the introduction of state transformations and the application of recursive terminal sliding mode structure.Depending on whether the system in question can be converted into a time-invariant linear system or not, two control schemes are proposed respectively guaranteeing that system states converge to zero in finite time.The effectiveness and the robust feature of the developed control approaches are testified by two practical examples:the simplified underactuated hovercraft system and the parking problem for a mobile robot of the unicycle type.
In this paper, a Duffing oscillator model with delayed velocity feedback is considered.Applying the time delayed feedback control method and delayed differential equation theory, we establish some criteria which ensure the stability and the existence of Hopf bifurcation of the model.By choosing the delay as bifurcation parameter and analyzing the associated characteristic equation, the existence of bifurcation parameter point is determined.We found that if the time delay is chosen as a bifurcation parameter, Hopf bifurcation occurs when the time delay is changed through a series of critical values.Some numerical simulations show that the designed feedback controllers not only delay the onset of Hopf bifurcation, but also enlarge the stability region for the model.
In this paper we address the problem of pressure management in water supply system (WSS) network.The model-based predictive control (MPC) strategies have some important features to deal with WSS.By hydraulic analysis of WSS,the predictive model is derived from the dynamic model and static model of WSS.Through WSS,the consumers demands are required to be met at all times according to some operational constraints that must be satisfied.The constraints of flow through actuators,the water level of reservoirs and the consumer areas pressure demand are determined by a specific system.In this work,we develop a constrained MPC controller that considers the zone control of the pressure outputs and incorporates steady state economic targets in the control cost function.The designed management strategies are applied to a case study and simulation results,covering different aspects,are provided.The output nodal pressure can be controlled in the desired zone by optimal scheduling the actuators of the WSS.If the variation range of reservoirs water level is broader,the rate of flow through the actuators is gentle,and vice versa.
This paper presents the design of decentralized repetitive control (RC) for multi-input multi-output (MIMO) systems.An optimization method is used to obtain a RC compensator that ensures system stability and good tracking performance.The designed compensator is in the form of a stable,low order,and causal filter,in which the compensator can be implemented separately without being merged with the RC internal model.This will reduce complexity in the implementation.Simulation results and comparison study are given to demonstrate the effectiveness of the proposed design.The novelty of design is also verified in experiments on a 2 degrees of freedom (DOF) robot.
In this paper,a new analysis and design method for proportional-integrative-derivative (PID) tuning is proposed based on controller scaling analysis.Integral of time absolute error (ITAE) index is minimized for specified gain and phase margins (GPM) constraints,so that the transient performance and robustness are both satisfied.The requirements on gain and phase margins are ingeniously formulated by real part constraints (RPC) and imaginary part constraints (IPC).This set of new constraints is simply related with three parameters and decoupling of the remaining four unknowns,including three controller parameters and the gain margin,in the nonlinear and coupled characteristic equation simultaneously.The formulas of the optimal GPM-PID are derived based on controller scaling analysis.Finally,this method is applied to liquid level control of coke fractionation tower,which demonstrate that the proposed method provides better disturbance rejection and robust tracking performance than some commonly used PID tuning methods.
A new longitudinal attitude control system design for an unmanned seaplane in the severe sea states is presented in this paper.We develop a nonlinear passive observer,which is used to achieve wave filtering and state estimation.Moreover,the observer can be extended to achieve adaptive wave filtering in varying sea states.Using the estimated low-frequency states,a backstepping sliding mode controller is designed to keep the longitudinal attitude of the unmanned seaplane stable.The stability of the total closed loop system is analyzed by using Lyapunov theory.Simulations are performed in different wave conditions,including Seastate 3 and Seastate 5.The simulations results show that the proposed longitudinal attitude controller can improve the anti-waves capability effectively.Moreover,adaptive wave filter has a significant advantage over a filter with fixed model parameters in varying sea states.
In order to compensate the network-induced random delays in networked control systems (NCSs),the semi-continuous hidden Markov model (SCHMM) is introduced in this paper to model the controller-to-actuator (CA) delay in the forward network channel.The expectation maximization algorithm is used to obtain the optimal estimation of the model s parameters,and the Viterbi algorithm is used to predict the CA delay in the current sampling period.Thus,the predicted CA delay and the measured sensor-tocontroller (SC) delay in the current sampling period are used to design an optimal controller.Under this controller,the exponentially mean square stability of the NCS is guaranteed,and the SC and CA delays are compensated.Finally,the effectiveness of the method proposed in this paper is demonstrated by a simulation example.Moreover,a comparative example is also given to illustrate the superiority of the SCHMM-based optimal controller over the discrete hidden Markov model (DHMM)-based optimal controller.
This paper addresses the state estimation problem for linear systems with additive uncertainties in both the state and output equations using a moving horizon approach.Based on the full information estimation setting and the game-theoretic approach to the H∞ filtering,a new optimization-based estimation scheme for uncertain linear systems is proposed,namely the H∞-full information estimator,H∞-FIE in short.In this formulation,the set of processed data grows with time as more measurements are received preventing recursive formulations as in Kalman filtering.To overcome the latter problem,a moving horizon approximation to the H∞-FIE is also presented,the H∞-MHE in short.This moving horizon approximation is achieved since the arrival cost is suitably defined for the proposed scheme.Sufficient conditions for the stability of the H∞-MHE are derived.Simulation results show the benefits of the proposed scheme when compared with two H∞ filters and the well-known Kalman filter.
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