Liliana Rosa

Self-Management of Adaptable Component-Based Applications

The problem of self-optimization and adaptation in the context of customizable systems is becoming increasingly important with the emergence of complex software systems and unpredictable execution environments. Here, a general framework for automatically deciding on when and how to adapt a system whenever it deviates from the desired behavior is presented. In this framework, the systems target behavior is described as a high-level policy that establishes goals for a set of performance indicators. The decision process is based on information provided independently for each component that describes the available adaptations, their impact on performance indicators, and any limitations or requirements. The technique consists of both offline and online phases. Offline, rules are generated specifying component adaptations that may help to achieve the established goals when a given change in the execution context occurs. Online, the corresponding rules are evaluated when a change occurs to choose which adaptations to perform. Experimental results using a prototype framework in the context of a web-based application demonstrate the effectiveness of this approach.

Context adaptation of the communication stack

This position paper presents a middleware framework to support the development of context-aware adaptive communication protocols that can be reconfigured according, not only to the local context, but also to the context of the remaining remote participants. The advantages of the framework are illustrated using the concrete example of an adaptive group communication protocol. The protocol supports a distributed chat application that can be executed in both fixed PCs and mobile devices.

Policy-Driven Adaptation of Protocol Stacks

Todays mobile applications need to execute in a wide range of heterogeneous devices that operate in different conditions. In this context, dynamic adaptation of the underlying communication support is fundamental to achieve adequate performance. We address the problem of supporting dynamic adaptation of communication protocol stacks through a policy-oriented approach, which promotes the separation of adaptation from protocol logic. In this paper, we provide an approach overview, and focus on the policy language and modeling primitives that allow to capture the adaptation requirements identified from the experience with Appia framework

Large-Scale Peer-to-Peer Autonomic Monitoring

The increasing scale and complexity of distributed system motivates the need for autonomous management. One of the key aspects in the management of distributed systems is the issue of component monitoring. Component monitoring is particularly challenging in large-scale dynamic systems, given the need to ensure that each component is monitored by at least one non-faulty component, despite joins, leaves, and failures, both at node and at network level. This paper proposes that components self-organize in an unstructured overlay network of constant degree in order to ensure that each component is always monitored by a threshold of other components.

A framework to support multiple reconfiguration strategies

Self-management is a key feature of autonomic systems. This often demands the dynamic reconfiguration of a distributed application. An important issue in the reconfiguration process is the strategy that is used to coordinate the multiple participants involved in the reconfiguration. This paper addresses the problem of providing support for multiple reconfiguration strategies in autonomic systems that are designed as self-reconfigurable service compositions. We decompose existing strategies in two separate aspects -- an orchestration protocol and a local reconfiguration procedure. This separation allowed us to design a set of generic pluggable components that can be integrated in concrete service compositions, in order to support different strategies. The strategy selection is performed according to the semantics of each reconfiguration. To illustrate our approach, we have implemented an instance of these pluggable components for the RAppia composition framework.

Modelling adaptive services for distributed systems

There exists a growing class of distributed applications that require adaptive middleware services, i.e., services that are able to monitor changes in the execution environment and in the users requirements, reacting to these changes by adapting their behaviour. This paper proposes modelling primitives that allow to describe the adaptation logic of distributed applications that use reconfigurable service compositions.

Goal-oriented Self-management of In-memory Distributed Data Grid Platforms

This paper addresses the self-management of in-memory distributed data grid platforms. A growing number of applications rely in these platforms to speed up access to large sets of data. However, they are complex to manage due to the diversity of configuration and load profiles. The proposed approach employs an adaptation policy expressed in terms of high-level goals to facilitate the task of the system manager, and address the complexity issues posed by the management of multiple configurations. The approach is validated experimentally using the open-source RedHat´s Infinispan platform.

From Local Impact Functions to Global Adaptation of Service Compositions

The problem of self-optimization and adaptation in the context of customizable systems is becoming increasingly important with the emergence of complex software systems and unpredictable execution environments. Here, a general framework for automatically deciding on when and how to adapt a system whenever it deviates from the desired behavior is presented. In this framework, the adaptation targets of the system are described in terms of a high-level policy that establishes goals for a set of performance indicators. The decision process is based on information provided independently for each service that describes the available adaptations, their impact on performance indicators, and any limitations or requirements. The technique consists of both offline and online phases. Offline, rules are generated specifying service adaptations that may help to achieve the specified goals when a given change in the execution context occurs. Online, the corresponding rule is evaluated when a change occurs to choose which adaptations to perform. Experimental results using a prototype framework in the context of a web-based application demonstrate the effectiveness of this approach.

Self-management of Distributed Systems Using High-Level Goal Policies

A growing number of systems have to face dynamic and unpredictable execution conditions and workloads. In an attempt to address the challenges posed by such environments, many systems are built from customizable components. These components can be tuned according to the execution context, allowing the system to meet its QoS requirements. In this context, self-management is an essential quality. This chapter presents an approach for the self-management of systems built from customizable components based on high-level goal policies. With this approach, in response to changes in the execution context, the necessary system adaptations are automatically selected and deployed. The evaluation of different aspects of the approach relies on a web-based application deployed as a distributed clustered-based architecture.

Building adaptive systems with service composition frameworks

Frameworks that support the implementation and execution of service compositions are a fundamental component of middleware infrastructures that support the design of adaptive systems. This paper discusses the requirements imposed by adaptive middleware on service composition frameworks, and discusses how they have been addressed by previous work. As a result, it describes the design of a novel adaptation-friendly service composition framework that takes into consideration the requirements at three different levels: service programming model level, adaptation-friendly services level, and kernel mechanisms level.