A Uniform Programming Abstraction for Effecting Autonomic Adaptations onto Software Systems

Most general-purpose work towards autonomic or self-managing systems has emphasized the front end of the feedback control loop, with some also concerned with controlling the back end enactment of runtime adaptations but usually employing an effector technology peculiar to one type of target system. While completely generic 'one size fits all' effector technologies seem implausible, we propose a general-purpose programming model and interaction layer that abstracts away from the peculiarities of target-specific effectors, enabling a uniform approach to controlling and coordinating the low-level execution of reconfigurations, repairs, micro-reboots, etc

A Uniform Programming Abstraction for Effecting Autonomic Adaptations onto Software Systems

Most general-purpose work towards autonomic or self-managing systems has emphasized the front end of the feedback control loop, with some also concerned with controlling the back end enactment of runtime adaptations -- but usually employing an effector technology peculiar to one type of target system. While completely generic "one size fits all" effector technologies seem implausible, we propose a general purpose programming model and interaction layer that abstracts away from the peculiarities of target specific effectors,enabling a uniform approach to controlling and coordinating the low-level execution of reconfigurations, repairs,micro-reboots, etc

Dynamic Adaptation of Temporal Event Correlation Rules

Temporal event correlation is essential to realizing self-managing distributed systems. Autonomic controllers often require that events be correlated across multiple components using rule patterns with timer-based transitions, e.g., to detect denial of service attacks and to warn of staging problems with business critical applications. This short paper discusses automatic adjustment of timer values for event correlation rules, in particular compensating for the variability of event propagation delays due to factors such as contention for network and server resources. We describe a corresponding Management Station architecture and present experimental studies on a testbed system that suggest that this approach can produce results at least as good as an optimal fixed setting of timer values

Elaborating a decentralized market information system

A Decentralized Market Information System (DMIS) that aggregates and provides information about markets is an important component for achieving markets in Grid and Peer-to-Peer systems. The proposed work is the development of a framework for the DMIS, which fulfils the economic provision within the main technical requirements like scalability towards nodes and data attributes and robustness against failures. The proposed work also allows obtaining results concerning the trade-off between economic benefits and technical costs. Introducing dynamic adaptive processes promises improvements in efficiency with regards to distributed queries and routing structures. This research proposal presents and discusses the research questions and challenges, the current knowledge and the research methodology proposed for the development of the DMIS framework.Peer Reviewe

Evolving Software Systems for Self-Adaptation

There is a strong synergy between the concepts of evolution and adaptation in software engineering: software adaptation refers to both the current software being adapted and to the evolution process that leads to the new adapted software. Evolution changes for the purpose of adaptation are usually made at development or compile time, and are meant to handle predictable situations in the form of software change requests. On the other hand, software may also change and adapt itself based on the changes in its environment. Such adaptive changes are usually dynamic, and are suitable for dealing with unpredictable or temporary changes in the software's operating environment. A promising solution for software adaptation is to develop self-adaptive software systems that can manage changes dynamically at runtime in a rapid and reliable way. One of the main advantages of self-adaptive software is its ability to manage the complexity that stems from highly dynamic and nondeterministic operating environments. If a self-adaptive software system has been engineered and used properly, it can greatly improve the cost-effectiveness of software change through its lifespan. However, in practice, many of the existing approaches towards self-adaptive software are rather expensive and may increase the overall system complexity, as well as subsequent future maintenance costs. This means that in many cases, self-adaptive software is not a good solution, because its development and maintenance costs are not paid off. The situation is even worse in the case of making current (legacy) systems adaptive. There are several factors that have an impact on the cost-effectiveness and usability of self-adaptive software; however the main objective of this thesis is to make a software system adaptive in a cost-effective way, while keeping the target adaptive software generic, usable, and evolvable, so as to support future changes. In order to effectively engineer and use self-adaptive software systems, in this thesis we propose a new conceptual model for identifying and specifying problem spaces in the context of self-adaptive software systems. Based on the foundations of this conceptual model, we propose a model-centric approach for engineering self-adaptive software by designing a generic adaptation framework and a supporting evolution process. This approach is particularly tailored to facilitate and simplify the process of evolving and adapting current (legacy) software towards runtime adaptivity. The conducted case studies reveal the applicability and effectiveness of this approach in bringing self-adaptive behaviour into non-adaptive applications that essentially demand adaptive behaviour to sustain

Orchestrating the Dynamic Adaptation of Distributed Software with Process Technology

Software systems are becoming increasingly complex to develop, understand, analyze, validate, deploy, configure, manage and maintain. Much of that complexity is related to ensuring adequate quality levels to services provided by software systems after they are deployed in the field, in particular when those systems are built from and operated as a mix of proprietary and non-proprietary components. That translates to increasing costs and difficulties when trying to operate large-scale distributed software ensembles in a way that continuously guarantees satisfactory levels of service. A solution can be to exert some form of dynamic adaptation upon running software systems: dynamic adaptation can be defined as a set of automated and coordinated actions that aim at modifying the structure, behavior and performance of a target software system, at run time and without service interruption, typically in response to the occurrence of some condition(s). To achieve dynamic adaptation upon a given target software system, a set of capabilities, including monitoring, diagnostics, decision, actuation and coordination, must be put in place. This research addresses the automation of decision and coordination in the context of an end-to-end and externalized approach to dynamic adaptation, which allows to address as its targets legacy and component-based systems, as well as new systems developed from scratch. In this approach, adaptation provisions are superimposed by a separate software platform, which operates from the outside of and orthogonally to the target application as a whole; furthermore, a single adaptation possibly spans concerted interventions on a multiplicity of target components. To properly orchestrate those interventions, decentralized process technology is employed for describing, activating and coordinating the work of a cohort of software actuators, towards the intended end-to-end dynamic adaptation. The approach outlined above, has been implemented in a prototype, code-named Workflakes, within the Kinesthetics eXtreme project investigating externalized dynamic adaptation, carried out by the Programming Systems Laboratory of Columbia University, and has been employed in a set of diverse case studies. This dissertation discusses and evaluates the concept of process-based orchestration of dynamic adaptation and the Workflakes prototype on the basis of the results of those case studies

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control

Evaluating Architectural Safeguards for Uncertain AI Black-Box Components

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability

A Dynamic Software Product Line Approach for Planning and Execution of Reconfigurations in Self-Adaptive Systems

Model-based autonomic computing systems facilitate the planning capabilities inside the adaptation logic. However, it is challenging to capture the complete reconfiguration behavior in a model. Context Feature Models used in Dynamic Software Product Lines help to specify the capabilities of a software as well as the monitored context values with the possibility to add constraints. Additionally, most adaptation logics are tailored to single use cases without the possibility for later reuse. This thesis presents an adaptation logic approach based on Dynamic Software Product Line variability models. The complete adaptation knowledge is encapsulated inside a knowledge component. This enables reuse of the complete adaptation logic. After the introduction of the approach, the adaptation logic is evaluated in a distributed computing use case