Mazeiar Salehie

Self-adaptive software: Landscape and research challenges

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software systems self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-* properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

Autonomic computing: emerging trends and open problems

The increasing heterogeneity, dynamism and interconnectivity in software applications, services and networks led to complex, unmanageable and insecure systems. Coping with such a complexity necessitates to investigate a new paradigm namely Autonomic Computing. Although academic and industry efforts are beginning to proliferate in this research area, there are still a lots of open issues that remain to be solved. This paper proposes a categorization of complexity in I/T systems and presents an overview of autonomic computing research area. The paper also discusses a summary of the major autonomic computing systems that have been already developed both in academia and industry, and finally outlines the underlying research issues and challenges from a practical as well as a theoretical point of view.

StarMX: A framework for developing self-managing Java-based systems

Realizing self-managing systems poses several development and operational challenges. Reusable software frameworks assist in addressing these challenges by utilizing appropriate patterns, and also providing essential runtime services for self-managing systems. This paper presents the StarMX framework, designed for building self-managing Java-based applications. It is a generic framework based on standards and well-established principles, and supports common tasks in the development of such systems. StarMX facilitates creating the management closed loop using various mechanisms such as action policies. The framework architecture and its utilization process, along with an example of its application are presented in this paper. Moreover, quality attributes and autonomic characteristics of the proposed framework are discussed.

Requirements-driven adaptive security: Protecting variable assets at runtime

Security is primarily concerned with protecting assets from harm. Identifying and evaluating assets are therefore key activities in any security engineering process - from modeling threats and attacks, discovering existing vulnerabilities, to selecting appropriate countermeasures. However, despite their crucial role, assets are often neglected during the development of secure software systems. Indeed, many systems are designed with fixed security boundaries and assumptions, without the possibility to adapt when assets change unexpectedly, new threats arise, or undiscovered vulnerabilities are revealed. To handle such changes, systems must be capable of dynamically enabling different security countermeasures. This paper promotes assets as first-class entities in engineering secure software systems. An asset model is related to requirements, expressed through a goal model, and the objectives of an attacker, expressed through a threat model. These models are then used as input to build a causal network to analyze system security in different situations, and to enable, when necessary, a set of countermeasures to mitigate security threats. The causal network is conceived as a runtime entity that tracks relevant changes that may arise at runtime, and enables a new set of countermeasures. We illustrate and evaluate our proposed approach by applying it to a substantive example concerned with security of mobile phones.

A Metric-Based Heuristic Framework to Detect Object-Oriented Design Flaws

One of the important activities in re-engineering process is detecting design flaws. Such design flaws prevent an efficient maintenance, and further development of a system. This research proposes a novel metric-based heuristic framework to detect and locate object-oriented design flaws from the source code. It is accomplished by evaluating design quality of an object-oriented system through quantifying deviations from good design heuristics and principles. While design flaws can occur at any level, the proposed approach assesses the design quality of internal and external structure of a system at the class level which is the most fundamental level of a system. In a nutshell, design flaws are detected and located systematically in two phases using a generic OO design knowledge-base. In the first phase, hotspots are detected by primitive classifiers via measuring metrics indicating a design feature (e.g. complexity). In the second phase, individual design flaws are detected by composite classifiers using a proper set of metrics. We have chosen JBoss application server as the case study, due to its pure OO large size structure, and its success as an open source J2EE platform among developers

Towards a goal-driven approach to action selection in self-adaptive software

Self‐adaptive software is a closed‐loop system, since it continuously monitors its context (i.e. environment) and/or self (i.e. software entities) in order to adapt itself properly to changes. We believe that representing adaptation goals explicitly and tracing them at run‐time are helpful in decision making for adaptation. While goal‐driven models are used in requirements engineering, they have not been utilized systematically yet for run‐time adaptation. To address this research gap, this article focuses on the deciding process in self‐adaptive software, and proposes the Goal‐Action‐Attribute Model (GAAM). An action selection mechanism, based on cooperative decision making, is also proposed that uses GAAM to select the appropriate adaptation action(s). The emphasis is on building a light‐weight and scalable run‐time model which needs less design and tuning effort comparing with a typical rule‐based approach. The GAAM and action selection mechanism are evaluated using a set of experiments on a simulated multi‐tier enterprise application, and two sample ordinal and cardinal action preference lists. The evaluation is accomplished based on a systematic design of experiment and a detailed statistical analysis in order to investigate several research questions. The findings are promising, considering the obtained results, and other impacts of the approach on engineering self‐adaptive software. Although, one case study is not enough to generalize the findings, and the proposed mechanism does not always outperform a typical rule‐based approach, less effort, scalability, and flexibility of GAAM are remarkable. Copyright

Adaptive traffic management for secure and efficient emergency services in smart cities

Rapid increase in number of vehicles on the roads as well as growing size of cities have led to a plethora of challenges for road traffic management authorities such as traffic congestion, accidents and air pollution. The work presented in this paper focuses on the particular problem of traffic management for emergency services, for which a delay of few minutes may cause human lives risks as well as financial losses. The goal is to reduce the latency of emergency services for vehicles such as ambulances and police cars, with minimum unnecessary disruption to the regular traffic, and preventing potential misuses. To this end, we propose to design a framework in which the Traffic Management System (TMS) may adapt by dynamically adjusting traffic lights, changing related driving policies, recommending behavior change to drivers, and applying essential security controls. The choice of an adaptation depends on the emergency severity level announced by the emergency vehicle(s). The severity level may need to be verified by corresponding authorities to preserve security measures. We discuss the details of our proposed framework and the potential challenges in the paper.

A Weighted Voting Mechanism for Action Selection Problem in Self-Adaptive Software

Self-adaptive software is a closed-loop system which aims at adjusting itself in response to changes at runtime. Such a system is required to monitor domain events, detect significant changes, decide how to react, and act in order to execute the decisions. This paper focuses on the deciding process particularly for application-level adaptation actions. For this purpose, a weighted voting mechanism has been proposed which makes decisions based on a Goal-Action- Attribute Model (GAAM). The decision-making algorithm traverses GAAM, determines activated goals and feasible actions for voting, and ultimately selects an action as the social choice. The proposed mechanism and GAAM are evaluated within a simulated model of a news web site.

A Quality-Driven Approach to Enable Decision-Making in Self-Adaptive Software

Self-adaptive software is a closed-loop system aims at altering itself in response to changes at runtime. Such a system, normally, requires monitoring, detecting (analyzing), deciding (planning), and acting (effecting) processes to fulfill adaptation requirements. This research mainly focuses on developing a quality-driven framework to facilitate realizing the deciding process. The framework is required to capture goals of adaptation, utility information, and domain characteristics in a knowledge-base.

Requirements-driven adaptive digital forensics

We propose the use of forensic requirements to drive the automation of a digital forensics process. We augment traditional reactive digital forensics processes with proactive evidence collection and analysis activities, and provide immediate investigative suggestions before an investigation starts. These activities adapt depending on suspicious events, which in turn might require the collection and analysis of additional evidence. The reactive activities of a traditional digital forensics process are also adapted depending on the investigation findings.