Energy efficiency of IT infrastructures has been a well-discussed research topic for several decades. The resulting approaches include hardware optimizations, resource management in operating systems, network protocols, and many more. The approach the authors present in this chapter is a self-optimization technique for IT infrastructures, which takes hard- and software components as well as users of software applications into account. It is able to ensure minimal energy consumption for a user request along with a set of non-functional requirements (e.g., the refresh rate of a data extraction tool). To optimize the ratio between utility of end users and the cost in terms of energy consumption, the system needs inherent variability leading to differentiated energy profiles and mechanisms to reconfigure the system at runtime. The authors present their approach called Energy Auto-Tuning (EAT) comprised of these mechanisms and an architecture which automatically tunes the energy efficiency of IT systems.

Energy efficiency is gaining more and more importance, since well-known ecological reasons lead to rising energy costs. In consequence, energy consumption is now also an important economical criterion. Energy consumption of single hardware resources has been thoroughly optimized for years. Now software becomes the major target of energy optimization. In this paper we introduce an approach called energy auto tuning(EAT), which optimizes energy efficiency of software systems running on multiple resources. The optimization of more than one resource leads to higher energy savings, because communication costs can be taken into account. E.g., if two components run on the same resource, the communication costs are likely to be less, compared to be running on different resources. The best results can be achieved in heterogeneous environments as different resource characteristics enlarge the synergy effects gainable by our optimization technique. EAT software systems derive all possible distributions of themselves on a given set of hardware resources and reconfigure themselves to achieve the lowest energy consumption possible at any time. In this paper we describe our software architecture to implement EAT.

Nicht-funktionale Eigenschaften geben Aussagen über Qualitätsaspekte einer Software. Mit einer Software-Produktlinie (SPL) wird eine Menge von verwandten Software-Produkten beschrieben, die auf Basis gemeinsam genutzter Bausteine und Architekturen entwickelt werden, um die Anforderungen unterschiedlicher Kundengruppen zu erfüllen. Hierbei werden gezielt Software-Bestandteile wiederverwendet, um Software effizienter zu entwickeln. In dieser Arbeit wird der Einfluss von Eingabedaten auf die nicht-funktionalen Eigenschaften von SPL untersucht. Es wird auf Basis von Messungen ausgewählter nicht-funktionaler Eigenschaften einzelner Software-Produkte ein Vorhersagemodell für beliebige Software-Produkte der SPL erstellt. Das Vorhersagemodell kann genutzt werden, um den Konfigurationsprozess zu unterstützen. Das Verfahren wird anhand einer SPL von verlustfreien Kompressionsalgorithmen evaluiert. Die Berücksichtigung von Eingabedaten kann die Vorhersage von nicht-funktionalen Eigenschaften einer SPL gegenüber einfacheren Vorhersagemodellen ohne die Berücksichtigung von Eingabedaten signifikant verbessern.

In vielen Bereichen des täglichen Lebens, angefangen vom Online-Banking bis hin zur Steuerung im Flugzeug, kommt Software mit laufzeitkritischen nicht-funktionalen Eigenschaften (NFE) zum Einsatz. Die Erfüllung der NFE spielt in diesen Anwendungen eine zentrale Rolle. Um dies zu erreichen, ist eine systematische und zielorientierte Behandlung dieser Anforderungen während der Entwicklung zwingend erforderlich. NFE zeichnen sich im Gegensatz zu funktionalen Eigenschaften durch besondere Merkmale aus, die ein adaptives Vorgehen zur Definition des Entwicklungsprozesses erzwingen. In der Arbeit wird eine Methodik zur kontextbasierten Anpassung von Vorgehensmodellen an laufzeitkritische NFE auf der Basis von Prozessmustern sowie das PROKRIS-Framework als unterstützende Umgebung vorgestellt.

In vielen Bereichen des täglichen Lebens, angefangen vom Online-Banking bis hin zur Steuerung im Flugzeug, kommt Software mit laufzeitkritischen nicht-funktionalen Eigenschaften (NFE) zum Einsatz. Die Erfüllung der NFE spielt in diesen Anwendungen eine zentrale Rolle. Um dies zu erreichen, ist eine systematische und zielorientierte Behandlung dieser Anforderungen während der Entwicklung zwingend erforderlich. NFE zeichnen sich im Gegensatz zu funktionalen Eigenschaften durch besondere Merkmale aus, die ein adaptives Vorgehen zur Definition des Entwicklungsprozesses erzwingen. In der Arbeit wird eine Methodik zur kontextbasierten Anpassung von Vorgehensmodellen an laufzeitkritische NFE auf der Basis von Prozessmustern sowie das PROKRIS-Framework als unterstützende Umgebung vorgestellt.

Energy efficiency of IT infrastructures has been a well-discussed research topic for several decades. The resulting approaches include hardware optimizations, resource management in operating systems, network protocols, and many more. The approach the authors present in this chapter is a self-optimization technique for IT infrastructures, which takes hard- and software components as well as users of software applications into account. It is able to ensure minimal energy consumption for a user request along with a set of non-functional requirements (e.g., the refresh rate of a data extraction tool). To optimize the ratio between utility of end users and the cost in terms of energy consumption, the system needs inherent variability leading to differentiated energy profiles and mechanisms to reconfigure the system at runtime. The authors present their approach called Energy Auto-Tuning (EAT) comprised of these mechanisms and an architecture which automatically tunes the energy efficiency of IT systems.

Energy efficiency is gaining more and more importance, since well-known ecological reasons lead to rising energy costs. In consequence, energy consumption is now also an important economical criterion. Energy consumption of single hardware resources has been thoroughly optimized for years. Now software becomes the major target of energy optimization. In this paper we introduce an approach called energy auto tuning(EAT), which optimizes energy efficiency of software systems running on multiple resources. The optimization of more than one resource leads to higher energy savings, because communication costs can be taken into account. E.g., if two components run on the same resource, the communication costs are likely to be less, compared to be running on different resources. The best results can be achieved in heterogeneous environments as different resource characteristics enlarge the synergy effects gainable by our optimization technique. EAT software systems derive all possible distributions of themselves on a given set of hardware resources and reconfigure themselves to achieve the lowest energy consumption possible at any time. In this paper we describe our software architecture to implement EAT.

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible.

Nicht-funktionale Eigenschaften geben Aussagen über Qualitätsaspekte einer Software. Mit einer Software-Produktlinie (SPL) wird eine Menge von verwandten Software-Produkten beschrieben, die auf Basis gemeinsam genutzter Bausteine und Architekturen entwickelt werden, um die Anforderungen unterschiedlicher Kundengruppen zu erfüllen. Hierbei werden gezielt Software-Bestandteile wiederverwendet, um Software effizienter zu entwickeln. In dieser Arbeit wird der Einfluss von Eingabedaten auf die nicht-funktionalen Eigenschaften von SPL untersucht. Es wird auf Basis von Messungen ausgewählter nicht-funktionaler Eigenschaften einzelner Software-Produkte ein Vorhersagemodell für beliebige Software-Produkte der SPL erstellt. Das Vorhersagemodell kann genutzt werden, um den Konfigurationsprozess zu unterstützen. Das Verfahren wird anhand einer SPL von verlustfreien Kompressionsalgorithmen evaluiert. Die Berücksichtigung von Eingabedaten kann die Vorhersage von nicht-funktionalen Eigenschaften einer SPL gegenüber einfacheren Vorhersagemodellen ohne die Berücksichtigung von Eingabedaten signifikant verbessern.

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible.