Optimization of adaptation plans for a service-oriented architecture with cost, reliability, availability and performance tradeoff

A service-based system may require adaptation for several reasons, such as service evolution (e.g., a new version may be available), hardware volatility (e.g., network quality changes), and varying user demands and new requirements (e.g., a new functionality or a different level of quality of service). Therefore, it is suitable to dynamically adapt a service-based system in an automated manner. However, service adaptations often do not consider software quality attributes and, if they do, they relay on a single attribute in isolation. In this paper, we present an optimization model, which aims to minimize the adaptation costs of a Service-Oriented Architecture (SOA), in correspondence with a certain change scenario (i.e., a set of new requirements) under reliability, availability and performance tradeoff. The model predicts the quality of the new SOA obtained by changing both its structure and behavior. Specifically, it suggests how to replace existing services with available instances and/or adding new services, and how to remove or introduce interaction(s) between existing services and/or new services. We show how our model works on a smartphone mobile application example, and through the sensitivity analysis we highlight its potential to drive architectural decisions

A QoS-based framework for the adaptation of service-based systems

Since a system may require dynamic adaptation for several reasons (e.g., a new version may be available and a new functionality or a different level of quality of service) it should be possible to dynamically adapt a service-based system in an automated manner. In this paper we give a general overview of the main components of a framework, based on an optimization model, that dynamically adapts a service based system (i.e., both the structural and behavioral software and hardware architecture) while minimizing the adaptation costs and guaranteeing a required level of the system qualities. Adaptation actions can be triggered both by a user request and/or automatically after the runtime violation of system quality constraints, or the appearing/disappearing of services into the environment. In this paper we provide also a deeper discussion of the optimization model that is the core of the framework by providing an example of instantiation of the model together with a first experimentation

Identifying and Handling Uncertainties in the Feedback Control Loop

In the feedback control loop, uncertainty is associated to different sources (e.g., the environment), and appears in different forms (e.g., as noise in variables or imperfections in techniques being used). In the MAPE-K control loop, uncertainty is normally handled by the decision maker at the Plan stage. However, depending on the complexity of the stages of the MAPE-K control loop, uncertainties need also to be handled at other stages, depending on the uncertainties associated with that stage. Moreover, uncertainties may also propagate between the stages of the control loop, which might affect how uncertainties are handled. In this position paper, we claim that uncertainties should be identified and handled at the different stages of the feedback control loop. We propose an approach for the identification of internal and external sources of uncertainty for a given stage, and we promote error propagation analysis as a method for analysing the propagation of uncertainties between stages. In terms of tradeoff analysis, which can take place at any stage of the MAPE-K control loop, such an approach provides a clear benefit since it leads to a more accurate estimation of the system quality attributes because uncertainties are handled in the context where they arise

Resource allocation, trading and adaptation in self-managing systems

The allocation of a limited number of resources among multiple self-interested stakeholders is a challenging issue for many real life systems and applications. Resource management for this type of systems is a challenging task because of the different objectives of the owner of the resources and the stakeholders. The owner aims to an efficient usage of the resources, while stakeholders have self-interested objectives. This paper presents a software framework for resource management based on the integration of dynamic allocation, trading, and self-adaptation mechanisms. Resource allocation and adaptation are performed in a centralized manner, while resource trading is achieved through a decentralized approach. Furthermore, the paper presents the application of the proposed framework in two different domains: aeronautics and Internetware

Architectural decisions for HW/SW partitioning based on multiple extra-functional properties

Growing advances in hardware technologies are enabling significant improvements in application performance by the deployment of components to dedicated executable units. This is particularly valid for Cyber Physical Systems in which the applications are partitioned in HW and SW execution units. The growing complexity of such systems, and increasing requirements, both project- and product-related, makes the partitioning decision process complex. Although different approaches to this decision process have been proposed during recent decades, they lack the ability to provide relevant decisions based on a larger number of requirements and project/business constraints. A sound approach to this problem is taking into account all relevant requirements and constraints and their relations to the properties of the components deployed either as HW or SW units. A typical approach for managing a large number of criteria is a multicriteria decision analysis. This, in its turn, requires uniform definitions of component properties and their realization in respect to their HW/SW deployment. The aim of this paper is twofold: a) to provide an architectural metamodel of component-based applications with specifications of their properties with respect to their partitioning, and b) to categorize component properties in relation to HW/SW deployment. The metamodel enables the transition of system requirements to system and component properties. The categorization provides support for architectural decisions. It is demonstrated through a property guideline for the partitioning of the System Automation and Control domain. The guideline is based on interviews with practitioners and researchers, the experts in this domain

Experimenting the Automated Selection of COTS Components Based on Cost and System Requirements

In a component-based development process the selection of components is an activity that takes place over multiple lifecycle phases that span from requirement specifications through design to implementation and integration. In different phases, different assumptions are valid and different granularity of information is available, which has a consequence that different procedure should be used in the selection process and an automated tool support for an optimized component selection would be very helpful in each phase. In this paper we analyze the assumptions and propose the selection procedure in the requirements phase. The selection criterion is based on cost minimization of the whole system while assuring a certain degree of satisfaction of the system requirements that can be considered before designing the whole architecture. For the selection and optimization procedure we have adopted the DEER (DEcision support for componEnt-based softwaRe) framework, previously developed to be used in the selection process in the design phase. The output of DEER indicates the optimal combination of single COTS (Commercial-Off-The-Shelf) components and assemblies of COTS that satisfy the requirements while minimizing costs. In a case study we illustrate the selection and optimization procedure and an analysis of the model sensitivity to changes in the requirements