Norha M. Villegas

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

A framework for evaluating quality-driven self-adaptive software systems

Over the past decade the dynamic capabilities of self-adaptive software-intensive systems have proliferated and improved significantly. To advance the field of self-adaptive and self-managing systems further and to leverage the benefits of self-adaptation, we need to develop methods and tools to assess and possibly certify adaptation properties of self-adaptive systems, not only at design time but also, and especially, at run-time. In this paper we propose a framework for evaluating quality-driven self-adaptive software systems. Our framework is based on a survey of self-adaptive system papers and a set of adaptation properties derived from control theory properties. We also establish a mapping between these properties and software quality attributes. Thus, corresponding software quality metrics can then be used to assess adaptation properties.

Towards Practical Runtime Verification and Validation of Self-Adaptive Software Systems

Software validation and verification (VV and (ii) present a proposal for including V&V operations explicitly in feedback loops for ensuring the achievement of software self-adaptation goals. Both of these contributions provide valuable starting points for V&V researchers to help advance this field.

DYNAMICO: A Reference Model for Governing Control Objectives and Context Relevance in Self-Adaptive Software Systems

Despite the valuable contributions on self-adaptation, most implemented approaches assume adaptation goals and monitoring infrastructures as non-mutable, thus constraining their applicability to systems whose context awareness is restricted to static monitors. Therefore, separation of concerns, dynamic monitoring, and runtime requirements variability are critical for satisfying system goals under highly changing environments. In this chapter we present DYNAMICO, a reference model for engineering adaptive software that helps guaranteeing the coherence of (i) adaptation mechanisms with respect to changes in adaptation goals; and (ii) monitoring mechanisms with respect to changes in both adaptation goals and adaptation mechanisms. DYNAMICO improves the engineering of self-adaptive systems by addressing (i) the management of adaptation properties and goals as control objectives; (ii) the separation of concerns among feedback loops required to address control objectives over time; and (iii) the management of dynamic context as an independent control function to preserve context-awareness in the adaptation mechanism.

Using Models at Runtime to Address Assurance for Self-Adaptive Systems

A self-adaptive software system modifies its behavior at runtime in response to changes within the system or in its execution environment. The ful- fillment of the system requirements needs to be guaranteed even in the presence of adverse conditions and adaptations. Thus, a key challenge for self-adaptive software systems is assurance. Traditionally, confidence in the correctness of a system is gained through a variety of activities and processes performed at de- velopment time, such as design analysis and testing. In the presence of self- adaptation, however, some of the assurance tasks may need to be performed at runtime. This need calls for the development of techniques that enable contin- uous assurance throughout the software life cycle. Fundamental to the develop- ment of runtime assurance techniques is research into the use of models at runtime

Managing dynamic context to optimize smart interactions and services

With the rapid growth of socio-technical ecosystems, smart interactions and services are permeating every walk of life. As smart interactions must managed automatically and interactively in response to evolving users matters of concern, the smart Internet requires creative approaches where services and interactions are implemented with awareness of, and dynamic adaptation to, users, computational environments, changing policies and unknown requirements. Consequently, modeling and managing dynamic context is critical for implementing smart services and smart interactions effectively. Thus, smart interactions need infrastructure to acquire, compose, and distribute context information to multiple execution endpoints. Moreover, context management must be controlled and governed to optimize system properties. This chapter surveys context modeling and management approaches intended for the optimization of smart interactions and services, discusses the main challenges and requirements of context-awareness in the smart Internet, and provides a feature-based framework useful for the evaluation and implementation of context modeling and management mechanisms.

Improving context-awareness in self-adaptation using the DYNAMICO reference model

Self-adaptation mechanisms modify target systems dynamically to address adaptation goals, which may evolve continuously due to changes in system requirements. These changes affect values and thresholds of observed context variables and monitoring logic, or imply the addition and/or deletion of context variables, thus compromising self-adaptivity effectiveness under static monitoring infrastructures. Nevertheless, self-adaptation approaches often focus on adapting target systems only rather than monitoring infrastructures. Previously, we proposed DYNAMICO, a reference model for self-adaptive systems where adaptation goals and monitoring requirements change dynamically. This paper presents an implementation of DYNAMICO comprising our SMARTERCONTEXT monitoring infrastructure and QoS-CARE adaptation framework in a self-adaptation solution that maintains its context-awareness relevance. To evaluate our reference model we use self-adaptive system properties and the Znn.com exemplar to compare the Rainbow system with our DYNAMICO implementation. The results of the evaluation demonstrate the applicability, feasibility, and effectiveness of DYNAMICO, especially for self-adaptive systems with context-awareness requirements.

Self-Healing Distributed Scheduling Platform

Distributed systems require effective mechanisms to manage the reliable provisioning of computational resources from different and distributed providers. Moreover, the dynamic environment that affects the behaviour of such systems and the complexity of these dynamics demand autonomous capabilities to ensure the behaviour of distributed scheduling platforms and to achieve business and user objectives. In this paper we propose a self-adaptive distributed scheduling platform composed of multiple agents implemented as intelligent feedback control loops to support policy-based scheduling and expose self-healing capabilities. Our platform leverages distributed scheduling processes by (i) allowing each provider to maintain its own internal scheduling process, and (ii) implementing self-healing capabilities based on agent module recovery. Simulated tests are performed to determine the optimal number of agents to be used in the negotiation phase without affecting the scheduling cost function. Test results on a real-life platform are presented to evaluate recovery times and optimize platform parameters.

Runtime Evolution of Highly Dynamic Software

Highly dynamic software systems are applications whose operations are particularly affected by changing requirements and uncertainty in their execution environments. Ideally such systems must evolve while they execute. To achieve this, highly dynamic software systems must be instrumented with self-adaptation mechanisms to monitor selected requirements and environment conditions to assess the need for evolution, plan desired changes, as well as validate and verify the resulting system. This chapter introduces fundamental concepts, methods, and techniques gleaned from self-adaptive systems engineering, as well as discusses their application to runtime evolution and their relationship with off-line software evolution theories. To illustrate the presented concepts, the chapter revisits a case study conducted as part of our research work, where self-adaptation techniques allow the engineering of a dynamic context monitoring infrastructure that is able to evolve at runtime. In other words, the monitoring infrastructure supports changes in monitoring requirements without requiring maintenance tasks performed manually by developers. The goal of this chapter is to introduce practitioners, researchers and students to the foundational elements of self-adaptive software, and their application to the continuos evolution of software systems at runtime.

Optimizing run-time SOA governance through context-driven SLAs and dynamic monitoring

End-users increasingly demand the provisioning of secure, scalable, reliable, flexible, resilient, and cost-efficient infrastructures, platforms, and software. However, the preservation of these properties, particularly in SOA and cloud environments, is extremely affected by distributed, heterogeneous, transient, and volatile context information. We envision the implementation of governance feedback loops, an innovative approach that equips service-oriented systems with run-time governance capabilities able to control the fulfillment of service level agreements (SLA) under changing execution environments. However, the effectiveness of our approach depends on the capability of governance infrastructures to guarantee the consistency between monitoring strategies, governance objectives, and context situations. To advance our vision, this paper proposes (i) contextual RDF graphs, a machine-readable specification of monitoring requirements that enable governance feedback loops with dynamic context monitoring capabilities; and (ii) context-driven SLAs, an extension of SLAs where context requirements are explicitly mapped to service level objectives (SLO) to optimize the run-time control of contracted obligations.