Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Applying tropos to socio-technical system design and runtime configuration

Recent trends in Software Engineering have introduced the importance of reconsidering the traditional idea of software design as a socio-tecnical problem, where human agents are integral part of the system along with hardware and software components. Design and runtime support for Socio-Technical Systems (STSs) requires appropriate modeling techniques and non-traditional infrastructures. Agent-oriented software methodologies are natural solutions to the development of STSs, both humans and technical components are conceptualized and analyzed as part of the same system. In this paper, we illustrate a number of Tropos features that we believe fundamental to support the development and runtime reconfiguration of STSs. Particularly, we focus on two critical design issues: risk analysis and location variability. We show how they are integrated and used into a planning-based approach to support the designer in evaluating and choosing the best design alternative. Finally, we present a generic framework to develop self-reconfigurable STSs

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system's efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid's availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids' digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid's digitization is still ongoing there is a general problem of a lack of data on the grid's status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop a framework to simulate a behavior of distribution networks in the presence of faults, and fluctuating generation and load that, alone or combined, may cause disturbances. With the framework we generate a large set of data that we use to develop and evaluate a voltage-sag disturbance predictor. To quantify how prediction and proactive mitigation of disturbances enhance availability we create an availability model of a proactive management. The model is generic and may be applied to evaluate the effect of proactive management on availability in other types of systems, and adapted for quantifying other types of properties as well. Also, we design a metric and a method for optimizing failure prediction to maximize availability with proactive approach. In our conclusion, the level of availability improvement with proactive approach is comparable to the one when using high-reliability and costly components. Following the results of the case study conducted for a 14-bus ADN, grid's availability may be improved by up to an order of magnitude if disturbances are managed proactively instead of reactively. The main results and contributions may be summarized as follows: (i) Taxonomy of faults in Smart Grid has been developed; (ii) Methodology and methods for proactive management of disturbances have been proposed; (iii) Model to quantify availability with proactive management has been developed; (iv) Simulation and fault-injection framework has been designed and implemented to generate disturbance-related data; (v) In the scope of a case study, a voltage-sag predictor, based on machine- learning classification algorithms, has been designed and the effect of proactive disturbance management on downtime and availability has been quantified

Critical Infrastructure Protection Approaches: Analytical Outlook on Capacity Responsiveness to Dynamic Trends

Overview: Critical infrastructures (CIs) – any asset with a functionality that is critical to normal societal functions, safety, security, economic or social wellbeing of people, and disruption or destruction of which would have a very significant negative societal impact. CIs are clearly central to the normal functioning of a nation’s economy and require to be protected from both intentional and unintentional sabotages. It is important to correctly discern and aptly manage security risks within CI domains. The protection (security) of CIs and their networks can provide clear benefits to owner organizations and nations including: enabling the attainment of a properly functioning social environment and economic market, improving service security, enabling integration to external markets, and enabling service recipients (consumers, clients, and users) to benefit from new and emerging technological developments. To effectively secure CI system, firstly, it is crucial to understand three things - what can happen, how likely it is to happen, and the consequences of such happenings. One way to achieve this is through modelling and simulations of CI attributes, functionalities, operations, and behaviours to support security analysis perspectives, and especially considering the dynamics in trends and technological adoptions. Despite the availability of several security-related CI modelling approaches (tools and techniques), trends such as inter-networking, internet and IoT integrations raise new issues. Part of the issues relate to how to effectively (more precisely and realistically) model the complex behavior of interconnected CIs and their protection as system of systems (SoS). This report attempts to address the broad goal around this issue by reviewing a sample of critical infrastructure protection approaches; comprising tools, techniques, and frameworks (methodologies). The analysis covers contexts relating to the types of critical infrastructures, applicable modelling techniques, risk management scope covered, considerations for resilience, interdependency, and policy and regulations factors. Key Findings: This research presents the following key findings: 1. There is not a single specific Critical Infrastructure Protection (CIP) approach – tool, technique, methodology or framework – that exists or emerges as a ‘fit-for-all’; to allow the modelling and simulation of cyber security risks, resilience, dependency, and impact attributes in all critical infrastructure set-ups. 2. Typically, two or more modelling techniques can be (need to be) merged to cover a broader scope and context of modelling and simulation applications (areas) to achieve desirable highlevel protection and security for critical infrastructures. 3. Empirical-based, network-based, agent-based, and system dynamics-based modelling techniques are more widely used, and all offer gains for their use. 4. The deciding factors for choosing modelling techniques often rest on; complexity of use, popularity of approach, types and objectives of user Organisation and sector. 5. The scope of modelling functions and operations also help to strike the balance between ‘specificity’ and ‘generality’ of modelling technique and approach for the gains of in-depth analysis and wider coverage respectively. 6. Interdependency and resilience modelling and simulations in critical infrastructure operations, as well as associated security and safety risks; are crucial characteristics that need to be considered and explored in revising existing or developing new CIP modelling approaches. Recommendations: Key recommendations from this research include: 1. Other critical infrastructure sectors such as emergency services, food & agriculture, and dams; need to draw lessons from the energy and transportation sectors for the successive benefits of: i. Amplifying the drive and efforts towards evaluating and understanding security risks to their infrastructure and operations. ii. Support better understanding of any associated dependencies and cascading impacts. iii. Learning how to establish effective security and resilience. iv. Support the decision-making process linked with measuring the effectiveness of preparedness activities and investments. v. Improve the behavioural security-related responses of CI to disturbances or disruptions. 2. Security-related critical infrastructure modelling approaches should be developed or revised to include wider scopes of security risk management – from identification to effectiveness evaluations, to support: i. Appropriate alignment and responsiveness to the dynamic trends introduced by new technologies such as IoT and IIoT. ii. Dynamic security risk management – especially the assessment section needs to be more dynamic than static, to address the recurrent and impactful risks that emerge in critical infrastructures

Model-Based Mitigation of Availability Risks

The assessment and mitigation of risks related to the availability of the IT infrastructure is becoming increasingly important in modern organizations. Unfortunately, present standards for Risk Assessment and Mitigation show limitations when evaluating and mitigating availability risks. This is due to the fact that they do not fully consider the dependencies between the constituents of an IT infrastructure that are paramount in large enterprises. These dependencies make the technical problem of assessing availability issues very challenging. In this paper we define a method and a tool for carrying out a Risk Mitigation activity which allows to assess the global impact of a set of risks and to choose the best set of countermeasures to cope with them. To this end, the presence of a tool is necessary due to the high complexity of the assessment problem. Our approach can be integrated in present Risk Management methodologies (e.g. COBIT) to provide a more precise Risk Mitigation activity. We substantiate the viability of this approach by showing that most of the input required by the tool is available as part of a standard business continuity plan, and/or by performing a common tool-assisted Risk Management

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems