Unifying Runtime Adaptation and Design Evolution

International audienceThe increasing need for continuously available software systems has raised two key-issues: self-adaptation and design evolution. The former one requires software systems to monitor their execution platform and automatically adapt their configuration and/or architecture to adjust their quality of service (optimization, fault-handling). The later one requires new design decisions to be reflected on the fly on the running system to ensure the needed high availability (new requirements, corrective and preventive maintenance). However, design evolution and selfadaptation are not independent and reflecting a design evolution on a running self-adaptative system is not always safe. We propose to unify run-time adaptation and run-time evolution by monitoring both the run-time platform and the design models. Thus, it becomes possible to correlate those heterogeneous events and to use pattern matching on events to elaborate a pertinent decision for run-time adaptation. A flood prediction system deployed along the Ribble river (Yorkshire, England) is used to illustrate how to unify design evolution and run-time adaptation and to safely perform runtime evolution on adaptive systems

Self-tuning of software systems through goal-based feedback control loop

Quality requirements of a software system cannot be optimally met, especially when it is running in an uncertain and changing environment. In principle, a controller at runtime can monitor the change impact on quality requirements of the system, update the expectations and priorities from the environment, and take reasonable actions to improve the overall satisfaction. In practice, however, existing controllers are mostly designed for tuning low- level performance indicators rather than high-level requirements. By maintaining a live goal model to represent the runtime requirements and linking the overall satisfaction to an earned business value indicator as feedback, we propose a control-theoretic self-tuning method that can dynamically tune the preferences of different quality requirements, and can autonomously make the tradeoff decisions among different quality requirements through our preference-based goal reasoning. The reasoning result is involved to reconfigure the variation points of the goal model, and accordingly mapped to the system architecture reconfiguration. The effectiveness of our self-tuning method is evaluated by comparing the earned business value with the static and ad-hoc methods and analysing the self-tuning process

PEMODELAN REQUIREMENTS DALAM MENGKONSTRUKSI PERANGKAT LUNAK SELF-ADAPTIVE

[Id]Mengkonstruksi perangkat lunak self-adaptive sangat berbeda dengan mengkonstruksi perangkat lunak non self-adaptive, hal ini menuntut banyak cara yang harus ditempuh untuk mencapai tujuan tersebut. Salah satunya adalah pada tahapan pemodelan requirements. Pendekatan yang digunakan saat melakukan pemodelan requirements untuk perangkat lunak self-adaptive, tidak cukup hanya menangkap kebutuhan sesuai dengan kondisi systems-as-is. Namun kebutuhan systems-to-be yang berhubungan dengan spesifikasi perilaku, dan kemampuannya untuk menangani perubahan ketika sistem sedang berjalan, merupakan faktor penting yang harus terpenuhi. Makalah ini membahas pemodelan requirements untuk mengembangkan self-adaptive systems, dengan mengintegrasikan pendekatan goal oriented requirements engineering dan feedback loop. Diawali dengan latar belakang, kemudian menguraikan penelitian terkait, dilanjutkan dengan konsep yang diusulkan beserta contoh penerapannya, dan diakhir bahasan kami menguraikan pekerjaan untuk masa depan serta kesimpulan.Kata kunci:Requirements modeling, goal oriented requirements engineering, self-adaptive systems, feedback loop[En]Construction of self-adaptive software is very different with the construction of non-self-adaptive software, its require many ways that must be through to gain these goals. one of them is on the requirement of modelling phase. The approach that used, when conduct modelling requirement is not enough to catch the needs appropriate with as-is system condition, but the requirement of to-be systems that connected with behaviour specification and its ability to handle transformation when system running is an important factor that must be fulfilled. this paper describes requirement modelling to develop self-adaptive systems, with goal oriented engineering integration approach and loop feedback. Started with the background, then untangle related research, continued with proposed concept and its implementation example, and in the last description, we untangle conclusion and our future works.Keywords: Requirements modeling, goal oriented requirements engineering, self-adaptive systems, feedback loo

Fuzzy logic based qos optimization mechanism for service composition

Increase emphasis on Quality of Service and highly changing environments make management of composite services a time consuming and complicated task. Adaptation approaches aim to mitigate the management problem by adjusting composite services to the environment conditions, maintaining functional and quality levels, and reducing human intervention. This paper presents an adaptation approach that implements self-optimization based on fuzzy logic. The proposed optimization model performs service selection based on the analysis of historical and real QoS data, gathered at different stages during the execution of composite services. The use of fuzzy inference systems enables the evaluation of the measured QoS values, helps deciding whether adaptation is needed or not, and how to perform service selection. Experimental results show significant improvements in the global QoS of the use case scenario, providing reductions up to 20.5% in response time, 33.4% in cost and 31.2% in energy consumption

Online Markov Chain Learning for Quality of Service Engineering in Adaptive Computer Systems

Computer systems are increasingly used in applications where the consequences of failure vary from financial loss to loss of human life. As a result, significant research has focused on the model-based analysis and verification of the compliance of business-critical and security-critical computer systems with their requirements. Many of the formalisms proposed by this research target the analysis of quality-of-service (QoS) computer system properties such as reliability, performance and cost. However, the effectiveness of such analysis or verification depends on the accuracy of the QoS models they rely upon. Building accurate mathematical models for critical computer systems is a great challenge. This is particularly true for systems used in applications affected by frequent changes in workload, requirements and environment. In these scenarios, QoS models become obsolete unless they are continually updated to reflect the evolving behaviour of the analysed systems. This thesis introduces new techniques for learning the parameters and the structure of discrete-time Markov chains, a class of models that is widely used to establish key reliability, performance and other QoS properties of real-world systems. The new learning techniques use as input run-time observations of system events associated with costs/rewards and transitions between the states of a model. When the model structure is known, they continually update its state transition probabilities and costs/rewards in line with the observed variations in the behaviour of the system. In scenarios when the model structure is unknown, a Markov chain is synthesised from sequences of such observations. The two categories of learning techniques underpin the operation of a new toolset for the engineering of self-adaptive service-based systems, which was developed as part of this research. The thesis introduces this software engineering toolset, and demonstrates its effectiveness in a case study that involves the development of a prototype telehealth service-based system capable of continual self-verification