Modern software applications are subject to uncertain operating conditions, such as dynamics in the availability of services and variations of system goals. Consequently, runtime changes cannot be ignored, but often cannot be predicted at design time. Control theory has been identified as a principled way of addressing runtime changes and it has been applied successfully to modify the structure and behavior of software applications. Most of the times, however, the adaptation targeted the resources that the software has available for execution (CPU, storage, etc.) more than the software application itself. This paper investigates the research efforts that have been conducted to make software adaptable by modifying the software rather than the resource allocated to its execution. This paper aims to identify: the focus of research on control-theoretical software adaptation; how software is modeled and what control mechanisms are used to adapt software; what software qualities and controller guarantees are considered. To that end, we performed a systematic literature review in which we extracted data from 42 primary studies selected from 1512 papers that resulted from an automatic search. The results of our investigation show that even though the behavior of software is considered non-linear, research efforts use linear models to represent it, with some success. Also, the control strategies that are most often considered are classic control, mostly in the form of Proportional and Integral controllers, and Model Predictive Control. The paper also discusses sensing and actuating strategies that are prominent for software adaptation and the (often neglected) proof of formal properties. Finally, we distill open challenges for control-theoretical software adaptation.

International audienceWe are at the dawn of a huge data explosion therefore companies have fast growing amounts of data to process. For this purpose Google developed MapReduce, a parallel programming paradigm which is slowly becoming the de facto tool for Big Data analytics. Although to some extent its use is already wide-spread in the industry, ensuring performance constraints for such a complex system poses great challenges and its management requires a high level of expertise. This paper answers these challenges by providing the first autonomous controller that ensures service time constraints of a concurrent MapReduce workload. We develop the first dynamic model of a MapReduce cluster. Furthermore, PI feedback control is developed and implemented to ensure service time constraints. A feedforward controller is added to improve control response in the presence of disturbances, namely changes in the number of clients. The approach is validated online on a real 40 node MapReduce cluster, running a data intensive Business Intelligence workload. Our experiments demonstrate that the designed control is successful in assuring service time constraints

International audienceCompanies have a fast growing amounts of data to process and store, a data explosion is happening next to us. Currentlyone of the most common approaches to treat these vast data quantities are based on the MapReduce parallel programming paradigm.While its use is widespread in the industry, ensuring performance constraints, while at the same time minimizing costs, still providesconsiderable challenges. We propose a coarse grained control theoretical approach, based on techniques that have already provedtheir usefulness in the control community. We introduce the first algorithm to create dynamic models for Big Data MapReduce systems,running a concurrent workload. Furthermore we identify two important control use cases: relaxed performance - minimal resourceand strict performance. For the first case we develop two feedback control mechanism. A classical feedback controller and an evenbasedfeedback, that minimises the number of cluster reconfigurations as well. Moreover, to address strict performance requirements afeedforward predictive controller that efficiently suppresses the effects of large workload size variations is developed. All the controllersare validated online in a benchmark running in a real 60 node MapReduce cluster, using a data intensive Business Intelligenceworkload. Our experiments demonstrate the success of the control strategies employed in assuring service time constraints