No abstract

Autonomic Managers (AMs) have been largely used to autonomously control reconfigurations within software components. This management is performed based on past monitoring events, configurations as well as behavioural programs defining the adaptation logics and invariant properties. The challenge here is to provide assurances on navigation through the configuration space, which requires taking decisions that involve predictions on possible futures of the system. This paper proposes the design of AMs based on logical discrete control approaches, where the use of behavioural models enriches the manager with a knowledge not only on events, states and past history, but also with possible future configurations. We define a Domain Specific Language, named Ctrl-F, which provides high-level constructs to describe behavioural programs in the context of software components. The formal definition of Ctrl-F is given by translation to Finite State Automata, which allow for the exploration of behavioural programs by verification or Discrete Controller Synthesis, automatically generating a controller enforcing correct behaviours. We implement an AM by integrating the result of Ctrl-F compilation and validate it with an adaptation scenario over Znn.com, a self-adaptive case study.

Autonomic Computing has largely contributed to the development of self-manageable Cloud services. It notably allows freeing Cloud administrators of the burden of manually managing varying-demand services, while still enforcing Service-Level Agreements (SLAs). All Cloud artifacts, regardless of the layer carrying them, share many common characteristics. Thus, it should be possible to specify, (re)configure and monitor any XaaS (Anything-as-a-Service) layer in an homogeneous way. To this end, the CoMe4ACloud approach proposes a generic model-based architecture for autonomic management of Cloud systems. We derive a generic unique Autonomic Manager (AM) capable of managing any Cloud service, regardless of the layer. This AM is based on a constraint solver which aims at finding the optimal configuration for the modeled XaaS, i.e. the best balance between costs and revenues while meeting the constraints established by the SLA. We evaluate our approach in two different ways. Firstly, we analyze qualitatively the impact of the AM behaviour on the system configuration when a given series of events occurs. We show that the AM takes decisions in less than 10 seconds for several hundred nodes simulating virtual/physical machines. Secondly, we demonstrate the feasibility of the integration with real Cloud systems, such as Openstack, while still remaining generic. Finally, we discuss our approach according to the current state-of-the-art.

In spite of the indubitable advantages of elasticity in Cloud infrastructures, some technical and conceptual limitations are still to be considered. For instance, resource start up time is generally too long to react to unexpected workload spikes. Also, the billing cycles' granularity of existing pricing models may incur consumers to suffer from partial usage waste. We advocate that the software layer can take part in the elasticity process as the overhead of software reconfigurations can be usually considered negligible if compared to infrastructure one. Thanks to this extra level of elasticity, we are able to define cloud reconfigurations that enact elasticity in both software and infrastructure layers so as to meet demand changes while tackling those limitations. This paper presents an autonomic approach to manage cloud elasticity in a crosslayered manner. First, we enhance cloud elasticity with the software elasticity model. Then, we describe how our autonomic cloud elasticity model relies on dynamic selection of elasticity tactics. We present an experimental analysis of a subset of those elasticity tactics under different scenarios in order to provide insights on strategies that could drive the autonomic selection of the proper tactics to be applied.

Self-adaptive behaviors in the context of Component-based Architecture are generally designed based on past monitoring events, configurations (component assemblies) as well as behavioural programs defining the adaptation logics and invariant properties. Providing assurances on the navigation through the configuration space remains a challenge. That requires taking decisions on predictions on the possible futures of the system in order to avoid going into branches of the behavioural program leading to bad configurations. This article proposes the design of self-adaptive software components based on logical discrete control approaches, in which the self-adaptive behavioural models enriches component controllers with a knowledge not only on events, configurations and past history, but also with possible future configurations. We present Ctrl-F, a Domain-specific Language whose objective is to provide high-level support for describing these control policies. Ctrl-F is formally defined by translation to Finite State Automata, which allow for the exploration of behavioural programs by verification or Discrete Controller Synthesis, i.e., by automatically generating a controller to enforce correct self-adaptive behaviours. We integrate Ctrl-F with FraSCAti, a Service Component Architecture middleware platform and we illustrate the use of Ctrl-F by applying it to two case studies: a news web application and a mutual exclusive task workflow.