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.     

Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX

With fast proliferation of QoS-enabled wireless packet networks, need for effective QoS control is increasing. In this paper, we focus on QoS provisioning in Mobile WiMAX access service network (ASN). We investigate a dynamic bandwidth provisioning method that can help to increase resource utilization. Our approach consists of two stages: traffic forecasting, followed by bandwidth provisioning. For the first stage, we use auto-regressive integrated moving average (ARIMA) model to forecast traffic based on online measurement. For the second stage, we use a bandwidth provisioning scheme that allocates bandwidths depending on the traffic forecasting. We modeled our problem as a Fractional Knapsack Problem for which we used a greedy algorithm in order to find an approximate solution. Through simulation studies with real-world data sets, we found that our approach could increase the bandwidth for the real-time traffic class and guarantee adequate service quality for the nonreal-time traffic class as well, while maximizing resource utilization.     

Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.     

Fuzzy tracking control design for nonlinear dynamic systems via T-Sfuzzy model

This study introduces a fuzzy control design method for nonlinear systems with a guaranteed H_∞ model reference tracking performance. First, the Takagi and Sugeno (TS) fuzzy model is employed to represent a nonlinear system. Next, based on the fuzzy model, a fuzzy observer-based fuzzy controller is developed to reduce the tracking error as small as possible for all bounded reference inputs. The advantage of proposed tracking control design is that only a simple fuzzy controller is used in our approach without feedback linearization technique and complicated adaptive scheme. By the proposed method, the fuzzy tracking control design problem is parameterized in terms of a linear matrix inequality problem (LMIP). The LMIP can be solved very efficiently using the convex optimization techniques. Simulation example is given to illustrate the design procedures and tracking performance of the proposed method     

Reconstruction of cylinder pressure for SI engine using recurrent neural network

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.     

Model reference output feedback fuzzy tracking control design for nonlinear discrete-time systems with time-delay

In this study, a model reference fuzzy tracking control design for nonlinear discrete-time systems with time-delay is introduced. First, the Takagi and Sugeno (TS) fuzzy model is employed to approximate a nonlinear discrete-time system with time-delay. Next, based on the fuzzy model, a fuzzy observer-based fuzzy controller is developed to reduce the tracking error as small as possible for all bounded reference inputs. The advantage of proposed tracking control design is that only a simple fuzzy observer-based controller is used in our approach without feedback linearization technique and complicated adaptive scheme. By the proposed method, the fuzzy tracking control design problem is parameterized in terms of a linear matrix inequality problem (LMIP). The LMIP can be efficiently solved using the convex optimization techniques. Simulation example is given to illustrate the design procedures and tracking performance of the proposed method.     

Integrated Multimedia Personal Communications with Asymmetric Services for Mobile Users in Cellular Systems

Wireless personal communication networks are evolving to provide integrated multimedia services to mobile users. An important issue in their design is the accommodation of various services each having predefined Quality-of-Service (QoS) requirements. Efficient resource utilization can be achieved using a medium access scheme that allows various traffic components to be statistically multiplexed and a call admission control to prevent overload. Some multimedia services have asymmetric resource needs. For example, web browsing generally requires more downlink bandwidth than uplink bandwidth. We present a medium access scheme and a call admission control algorithm for guaranteed QoS provisioning in asymmetric integrated multimedia personal communication networks. An overall performance analysis model is developed. Performance characteristics are calculated and presented for an example system.     

A NEURAL NETWORK APPROACH TO ON-LINE IDENTIFICATION OF NONLINEAR SYSTEMS

Research investigating neural identification of dynamic systems has concentrated on off-line techniques. To be suitable for adaptive process control, on-line algorithms must be developed. This study investigates a modified back-propagation technique to achieve practical on-line capability. A technique denoted history-stack enhancement greatly improves the identification performance of the neural network. As a demonstration, a composite system of formidable but realistic nonlinear components was constructed and used to compare identification techniques including a recursive linear estimator and die new neural method. The results show that on-line neural identification is feasible for a wide range of formidable nonlinear characteristics individually found in industrial processes. Although performance is slower than with linear identification, the asymptotic accuracy of the neural technique is better for the nonlinear system being identified.     

Formal QoS Policy Based Grid Resource Provisioning Framework

Grid resource provisioning is a complex task that is often compromised due to non-availability of the desired resources. The heterogeneous and dynamic nature of the Grid resources depends on the Quality of Service (QoS) based resource provisioning for allocation of appropriate resources to Grid applications. Until resource provisioning is considered a fundamental capability, predictable QoS cannot be delivered to the Grid consumers. A resource provisioning policy based on QoS parameters is required for efficient Grid resource provisioning. In this paper a resource provisioning framework has been proposed. This framework offers resource provisioning policy that caters to provisioned resource allocation and resource scheduling. The policy rules have been specified in XML schema. The policy has been validated by Z Formal specification language and implementation has been demonstrated through a case study. The experimental results demonstrate that QoS based provisioned approach is effective in minimizing cost and submission burst time of applications in comparison to the existing approaches.     

Output feedback model predictive control for LPV systems based on quasi-min–max algorithm

This paper proposes a robust output feedback model predictive control (MPC) scheme for linear parameter varying (LPV) systems based on a quasi-min–max algorithm. This approach involves an off-line design of a robust state observer for LPV systems using linear matrix inequality (LMI) and an on-line robust output feedback MPC algorithm using the estimated state. The proposed MPC method for LPV systems is applicable for a variety of systems with constraints and guarantees the robust stability of the output feedback systems. A numerical example for an LPV system subject to input constraints is given to demonstrate its effectiveness.     

An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user’s QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.

     

MANET中基于动态资源管理的QoS路由模型研究

由于时变链路和节点移动的无规则性导致了常规路由协议在MANET环境下无法正常工作,在MANET中确立QoS路由和保障QoS路由传输是促使该网络走向实用的关键。针对这一情况,在通用QoS框架和INSIGNIA结构基础上,提出了一种基于动态资源管理的QoS路由模型,该模型通过QoS路由建立模块和动态资源调整机制来实现MANET中路由的QoS保障。模拟结果显示该模型可以有效地支持MANET中满足QoS需求的多媒体信息传输。     

移动预测下一种基于QoS保障的组播协议

基于QoS路由及保障路由实施是支持MANET应用的关键.为解决MANET中QoS问题,在移动预测机制下提出了一种基于QoS保障的组播协议.该协议通过簇头选举,移动预测和QoS控制策略在组播源与含目的节点的组播簇头间确定最稳传输路径,并采用基于动态资源管理的QoS路由模型来保障路由实施.仿真结果显示该方案可获得较高传输成功率和较低控制开销,能有效支持MANET中具有QoS需求的组播传输.     

Prescribed performance adaptive neural output feedback dynamic surface control for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics

This paper studies the output feedback tracking control problem for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics using a prescribed performance adaptive neural dynamic surface control design approach. A nonlinear mapping technique is employed to address the state constraints. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. The unmodeled dynamics is addressed by introducing an available dynamic signal. Subsequently, we construct the controller and parameter adaptive laws using a backstepping technique. Based on Lyapunov stability theory, it is shown that all signals in the closed‐loop system are semiglobally uniformly ultimately bounded and that the tracking error always remains within the prescribed performance bound. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

磁悬浮球系统的非线性自适应控制方法

针对磁悬浮球系统被控对象变化时控制器自适应问题,提出了一种反馈线性化和在线参数辨识相结合的非线性自适应控制方法。基于状态反馈精确线性化方法建立磁悬浮球系统的数学模型,通过状态反馈设计了一种非线性控制器,并给出了控制器参数的在线辨识方法。MATLAB平台上在线实验结果表明,与反演滑模自适应控制方法相比,提出的方法无须在平衡位置近似线性化,可以在平衡位置实现对不同对象的自适应控制,且具有理想的稳态调节性能。     

An adaptive recurrent fuzzy system for nonlinear identification

This paper describes the architecture and training procedure of a recurrent fuzzy system (RFS). The RFS is composed of a fuzzy inference system (FIS) and a delayed feedback connection. The recurrent property comes from feeding the FIS output back to the FIS input via an adjustable feedback parameter. Both the on-line and off-line training procedures based on the backpropagation-through-time (BPTT) algorithm have been investigated. The adjoint model of the RFS is obtained and used to compute the gradients. It is shown that the off-line training is insufficient to adapt to changes in system dynamics. So, an on-line training procedure is derived. In this procedure, a first in first out stack is used to store a certain history of the input–output data to perform a truncated BPTT algorithm. A quasi-Newton optimization method with a line search algorithm is used to adjust the RFS parameters. The performance of the developed RFS is demonstrated by applying to the identification of nonlinear dynamic systems. The simulation studies show that the proposed identification model has the ability to learn dynamics of highly nonlinear systems and compensate system uncertainties. The results are promising for the further application in the area of control and modeling.     

Assured end-to-end QoS through adaptive marking in multi-domain differentiated services networks

The issue of resource management in multi-domain Differentiated Services (DiffServ) networks has attracted a lot of attention from researchers who have proposed various provisioning, adaptive marking and admission control schemes. In this paper, we propose a Reinforcement Learning-based Adaptive Marking (RLAM) approach for providing assured end-to-end quality of service (QoS) in the form of end-to-end delay and throughput assurances, while minimizing packet transmission cost since ‘expensive’ Per Hop Behaviors like Expedited Forwarding (EF) are used only when necessary. The proposed scheme tries to satisfy per flow end-to-end QoS through control action,s which act on flow aggregates in the core of the network. Using an ns2 simulation of a multi-domain DiffServ network with multimedia traffic, the RLAM scheme is shown to be effective in significantly lowering packet transmission costs without sacrificing end-to-end QoS, when compared to the commonly used static marking scheme.     

A novel pricing approach to support QoS in 3G networks

Pricing in 3G and other communication networks may control and manage the utilisation of network resources. The available network resources get strained with increased usage levels, which results in poor service to the users. Most users prefer receiving high quality services at affordable costs. This requires the provision of QoS guarantees for network services at a low cost. In a real business scenario, this relationship is hard to achieve; moreover revenue sources for network operators have been shifting from the provision of network access to provisioning of rich services, e.g. multimedia services. To attain a functional compromise, we propose a pricing scheme that relies on service profiles to manage resource utilisation in a DiffServ-enabled 3G network. The service profiles define the QoS achieved for accessing services through a common resource pool, in which resource sharing is used to maximise network resource utilisation, user satisfaction and profits for the network operators. In an NGN scenario users would select pricing profiles according to their budgets, and the network will map these profiles to a set of QoS options that may translate to the choice of an access network for service access. In this paper, we present the mathematical model of the proposed pricing scheme, the proposed design of an evaluation framework, QoS performance results, and a service provisioning scenario illustrating the applicability of the proposed pricing scheme.     

Managing end-to-end QoS in distributed embedded applications

Maintaining end-to-end quality of service (QoS) is a challenge in distributed real time embedded systems due to dynamically changing network environments and resource requirements. The authors' middleware QoS management approach encapsulates QoS behaviors as software components. Using the Corba component model, they build these specialized QoS components and combine them to produce a comprehensive management system that maintains QoS. The authors illustrate the approach by building a real-world medium-scale system with these components. Using this example, they demonstrate the reusability of each component in different contexts.     

Nonlinear adaptive model following control for a 3-DOF tandem-rotor model helicopter

This paper considers two-input, two-output nonlinear adaptive model following control of a 3-DOF (degree-of-freedom) tandem rotor model helicopter. The control performance is studied by real time implementation of the control algorithms in an actual helicopter testbed. Since the decoupling matrix of the model helicopter is singular, the system is not decouplable by static state feedback, and it is challenging to design a feedback control system. Dynamic state feedback is applied. The controller is designed using a nonlinear structure algorithm. Furthermore, a parameter identification scheme is introduced in the closed-loop system to improve the control performance. Three identification methods are discussed.