Control and Scheduling Joint Design

International audienceControl systems and real-time computing have for a long time been associated in control systems, with the aim of controlling a process and bringing it towards a state which complies with the objectives specified by the user. The process is often a phys- ical device, for instance mechanical (rolling mills), electromechanical (DVD player, robots), thermal (internal combustion engines), chemical (reactors), hydraulic (energy production). . . These can be complex processes associating several of these technolo- gies, for instance in terrestrial, aerial or underwater vehicles. They can also be com- puting components (scheduling of tasks, network gateway, website management) or electronic (power supply of a chip, phase locked loop), or even simulated components (avatar control in a virtual world, "hardware-in-the-loop" real-time simulators). The increasing complexity of these systems requires reviewing their properties in order to better integrate the control system design and implementation constraints executed on computing systems. The first section will recall the main properties of closed loop control as well as the constraints and limitations induced by their digital implementation. Section 1.2 examines how the control task scheduling constraints can be relaxed by exploiting these properties. The design of scheduling controllers can be performed in the formalism of sampled systems (Section 1.3), but also in that of weakly strong real-time scheduling (Section 1.4). Finally, an example of designing and implementing the scheduling control of tasks in a video decoder will be detailed in Section 1.5

A simulation model to implement multiple client class server-client software architecture

In this chapter we introduce the simulation environment, which will be used to apply the proposed nonlinear control methodologies in this thesis. A simulation environment is vital to evaluate, validate and compare the various existing control methodologies with the proposed technique in a controlled environment. This is because a multi-client class system deployed in physical resources (in other words a case study or test bed) provides variable performance even under same settings / inputs in the multiple runs. A known limitation of simulation environments is it abstracts away some of the behavior from the analysis to trade o ff between the consistency. Therefore, the validation of this thesis utilizes the strengths of both the simulation and case study based evaluations. In following sections, we provide a description of characteristics and the process of multi-client class system followed by the architecture and implementation details of the simulation environment

Using nonlinear control of resources to achieve differential performance objectives in software systems

In many competitive business domains, software systems have become vital to achieve the business objectives efficiently. In such software systems, maintaining performance properties such as response time and throughput at runtime is important to avoid customer dissatisfaction and violation of service level agreements. This is a challenging task as service providers typically need to share computing resources between service consumers in order to deliver those services efficiently under dynamic and unpredictable environmental conditions. Managing such systems using human-in-the-loop decision making methods at runtime is neither efficient nor cost-effective. As a result, runtime performance management tasks need to be automated. Closed-loop approaches based on control engineering methodologies have been widely investigated, as a way to achieve relative and absolute performance management objectives at runtime, while sharing a limited amount of resources. These approaches are based on linear modelling and control methods. However, linear approaches neglect the prominent nonlinear dynamics of the relative and absolute performance management systems and provide effective control only in a limited operating range. In this thesis, we classify the nonlinearities that exist in the relative and absolute performance management schemes. We then introduce two novel nonlinear feedback control methods to reduce the runtime impact of nonlinearities on the control system. In the first approach, compensators are integrated into the control system to reduce the impact of nonlinearities. In particular, a Hammerstein-Wiener block-oriented model is used for relative performance management while a MIMO Wiener model is used for absolute performance management. In the second approach, we represent the dynamics of the nonlinear system with multiple linear models. Multiple models and multiple linear controllers are implemented together with a switching scheme, to select the most suitable controller to provide control under the current operating conditions. In addition, we present a class library of control components, to facilitate the implementation of complex control systems for software systems. The evaluations conducted using simulation studies and experimental real-world case studies indicate that the proposed nonlinear approaches can significantly improve the performance and resource management capabilities compared to other state-of-the-art approaches. We further demonstrate that the class library significantly improves the efficiency of the control system engineering process

Data-Driven Predictive Control with Nonlinear Compensation for Performance Management in Virtualized Software System

This paper deals with data-driven predictive control for relative performance management in virtualized software system. The system dynamics are characterized in Hammerstein-Wiener structure to capture nonlinear and linear characteristics. The proposed control approach is the implementation of Subspace-based Predictive Control with the integration of nonlinear compensation. The compensator functions are inverse static input and output nonlinearity models from the Hammerstein-Wiener system identification. The subspace predictors are formulated from the linear model input and output of Wiener block. The experimental results from three scenarios of performance objectives show the reliability of Subspace-based Predictive Control to manage the virtualized software system

Feedback controllers in the cloud

Autonomic management of quality of service attributes by dynamic resource allocation is one of the requirements in cloud computing environments. In order to provide these requirements in a flexible way existing cloud providers expose static rule /threshold/heuristic based decision implementation frameworks to their consumers. These rule /threshold/heuristic based methods are relatively easy to design and develop. However, they suffer from lack of well-founded design process to decide important design parameters (e.g.: thresholds, the number of instances to add/remove, calm time) and difficulty of dynamically adjusting to different conditions. In addition, incorrect/non-adaptable rule settings could cause long term instabilities leading to service outages. The feedback control has been shown to be useful for performance management and resource allocation in many complex software systems. In this work, we investigate the advantages and limitations of applying feedback controllers in cloud platforms. In addition, we illustrate the suitability of standard feedback controllers depending on the consumer requirements/applications. Finally, we propose a novel platform as a service architecture to design, develop, integrate and runtime manage feedback controllers for the cloud consumer applications

Can control-component libraries reduce the costs of developing control engineering-based self-adaptive systems?

Many approaches have been proposed to develop self-adaptive software systems based on control engineering methods in recent years. However, these research works only evaluate the self-adaptive capabilities of the proposed control solution, but no evaluation is performed to quantify the costs of implementing such a control solution. This paper provides results of an empirical study, conducted to quantify the implementation, testing and knowledge requirement costs of building a self-adaptive software system using control engineering methods. Our objective is to investigate, whether these costs can be significantly reduced if a library of prepackaged control components is available to software engineers. The findings of the study indicate that the aforementioned costs can be significantly reduced when supporting libraries are available. We also list the lessons learned from this study and recommendations, which may be useful in designing experiments to evaluate engineering costs of self-adaptive methods in the future

4M-Switch: Multi-mode-multi-model supervisory control framework for performance differentiation in virtual machine environments

When resources are shared in a virtual machine environment, providing different performance levels to different customer applications is a challenging task. In order to sustain stability, the control solution not only has to take into account the time-based dynamics, but also has to adapt to various operating modes. This paper proposes the 4M-Switch supervisory control system design framework, which takes into account the possible operating modes and dimension changes of the VM environment at design time and then adapts the control solution to achieve required management goals when changes occur at runtime. 4M-Switch utilizes a piece-wise linear modeling approach to present the behavior of the system using multiple models and simple switching logic to change the controller parameters to mitigate the effects of nonlinearities. The experiment results conducted under a range of conditions show that 4M-Switch approach effectively adapts the control solution and provides significantly more stable performance differentiation compared to the existing approaches

An improved Hammerstein-Wiener System Identification with application to virtualized software system

This paper proposes a system identification procedure to approximate virtualized software system dynamics within the framework of a Hammerstein-Wiener model. The approach is an extension of the existing works where the linear dynamics are estimated in Frequency Sampling Filter (FSF) structure and the inverse static output nonlinearity are synthesized in B-Spline curve functions. Furthermore, the issue on parameter selection for B-spline model approximation is addressed by using a data clustering method. An experimental test-bed of virtualized software system is established to generate experimental data which are used to confirm the performance of the proposed approach. The identification results have shown that the model efficacy is increased with the proposed approach because the dimension of the nonlinear model is reduced significantly while maintaining the desired accuracy