The main reason for investigating adaptive real-time systems is to provide predictability and flexibility for systems and environments where requirements on resources are inherently unstable and difficult to predict in advance. Such a difficulty is due to different causes. First of all, modern computer architectures include several low-level mechanisms that are designed to enhance the average performance of applications, but unfortunately introduce high variations on tasks’ execution times. In other situations, as in multimedia systems, processes can have highly variable execution times that also depend on input data [Riz06]. As a consequence, the overall workload of a computing system is subject to significant variations, which can produce an overload and degrade the performance of the entire system in an unpredictable fashion [Loo03, Eke05]. This situation is particularly critical for small embedded devices used in consumer electronics, telecommunication systems, industrial automation, and automotive systems. In fact, in order to satisfy a set of constraints related to weight, space, and energy consumption, these systems are typically built using small microprocessors with low processing power and limited resources.

For most of these systems, the classical real-time approach based on a rigid off-line design, worst-case assumptions and a priori guarantee would keep resources unused for most of the time, therefore it is not acceptable for efficiency reasons. When resources are scarce, they cannot be wasted. On the other hand, an off-line design based on average-case behavior is also critical, because it would be difficult to guarantee timing constraints when resources are overloaded.
To prevent unpredictable performance degradations due to overloads, a real-time system must react to load variations, degrading its performance in a controlled fashion acting on system, as well as application parameters. The process of controlling the performance of a system as a function of workload variations is referred to as Quality of Service (QoS) Management. Performing efficient QoS management requires specific support at different levels of the system architecture. Hence, new software methodologies are emerging in Embedded Systems, which strictly relate to Real-Time Operating Systems (RTOS), Middleware, and Networks.

Real-time scheduling is the kernel mechanism having the most impact on RTOS performance. Most scheduling algorithms have been developed around one of three basic schemes: table driven [Foh95], fixed priority [Aud95], or dynamic priority [Spu96]. Depending on whether scheduling decisions are resolved before or during runtime, they are classified as offline or online.

Adapting to changing environmental situations may involve changes to task parameters at runtime. System wide changes, e.g., for changing operational modes in the system, have been addressed by mode change algorithms [Foh93].
Feedback scheduling changes task parameters, in particular periods [But02], to respond to online variations in the environment and current load conditions of the system. As both conditions can vary frequently, too frequent responses, which in turn influence the conditions, can introduce instability in the system. Feedback control scheduling applies control theory to estimate effects of changes and to choose parameters to provide for smooth responses and avoid instability [Cas06].
Each of the basic scheduling paradigms has specific advantages. When advantages of different schemes are demanded in the same system, more than one scheme could be used for different tasks. For example, in a complex system including hard periodic and soft aperiodic tasks, two scheduling schemes need to be integrated for satisfying the different requirements of each task class.
In hierarchical scheduling [Reg01] a meta algorithm arbitrates between a set of diverse scheduling algorithms. Thus, it can appear to the individual scheduling algorithms and their applications that they execute alone in the system. Furthermore, the amount of the CPU portion can be set individually for each scheduler and application. Special attention has to be given to shared resources.
In those systems subject to higly varialble workload, an overload condition could degrade the system performance in an unpredictable fashion. Novel scheduling methodologies have been recently proposed to cope with transient and permanent overload conditions. Transient overloads due to execution overruns can be effectively handled using resource reservation techniques [Mer94], according to which each activity consumes a fraction of the processing resource, independently of the actual execution demand. Permanent overload conditions, typically occurring in a periodic environment, can be efficiently handled by sporadic job skipping [Kor95] or by rate adaptation techniques (like elastic scheduling) [But02], which keep the load below a given threshold by acting on task periods.
Major needs for flexible scheduling techniques are typical of industries working in consumer electronics, industrial automation, and telecommunications, as resulted from a study carried out within the ARTIST 5FP project [Bou05].

For example, mobile terminals today are getting more and more advanced and their source code consists of several million lines of code involving a large number of parallel activities. For these applications, the use of flexible real-time scheduling techniologies would allow to safely mix real-time and non real-time processes. The benefit of such a solution would be a much more scalable platform. Adding and removing features would become predictable and less hazardous, allowing configuring the system without worrying about unpleasant surprises.

In the area of Industrial Automation, the continuous increment in processing power and memory capacity in local processors gives the opportunity to add new tasks into them, increasing system complexity in terms of supervision, diagnostics, presentation, communication, etc. Adaptive tasks scheduling that preserves the real-time constraints is a possible way to handle such situation and manage the complexity of the application.

In telecommunication companies, the main current interest seems to be in exploring the use of real-time extensions for the Linux OS. It also seems that QoS mechanisms are starting to be recognized as important for these embedded applications to increase the efficiency of subsystems and to support the possibility to serve more clients with similar levels of resources.

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