Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information.Comment: Published and presented in the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014

Scenarios for an autonomic micro smart grid

Autonomic computing is a bio-inspired vision elaborated to manage the increasing complexity of contemporary heterogeneous, large scale, dynamic computer systems. This paper presents a series of scenarios relative to micro smart grids – district-size “smart” electricity networks. These scenarios involve situations where autonomic management approaches could provide promising solutions. They therefore appear as short stories of a possible autonomic micro smart grid, that illustrate the concepts of autonomic computing as well as the potential behind this vision. At the same time, these scenarios reveal open issues as well as novel perspectives on the future of micro smart grids

A Dependability Assessment Process for Ensuring Consistent Provisioning of Network Recovery

AbstractWe have developed an engineering method to detect errors in provisioning automated recovery processes in multilayer and multi-protocol communications transport networks. Our dependability assessment process leverages inference techniques provided by Semantic Web technologies in order to detect network-device provisioning errors. Provisioning should be accompanied by methodologies, processes, and activities to ensure that it can be trusted to achieve a desired network state. Our method takes into account unique constraints in the telecommunications domain including bottom-up evolution of physical layer technologies to provide connectivity and lack of a universal model of network functionality. We apply our method to assessing the correctness of provisioning decisions for a protection switching application in a transport network in both the spatial and temporal domains

Conceptual framework for ubiquitous cyber-physical assembly systems in airframe assembly

Current sectoral drivers for the manufacturing of complex products - such as airframe assembly -require new manufacturing system paradigms to meet them. In this paper, we propose a conceptual framework for cyber-physical systems driven by ubiquitous context-awareness by drawing together a unique and coherent vision that merges several extant concepts. This framework leverages recent progress in agent-based systems, exible manufacturing, ubiquitous computing, and metrology-driven robotic assembly in the Evolvable Assembly Systems project. As such, although it is adapted for and grounded in manufacturing facilities for airframe assembly, it is not specifically tailored to that application and is a much more general framework. As well as outlining our conceptual framework, we also provide a vision for assembly grounded in a review of existing research in the area

Self-organization and autonomy in computational networks: agents-based contractual workflow paradigm

We describe an agents-based contractual workflow paradigm for Self-organization and autonomy in computational networks. The agent-based paradigm can be interpreted as the outcome arising out of deterministic, nondeterministic or stochastic interaction among a set of agents that includes the environment. These interactions are like chemical reactions and result in self-organization. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the agents carry out the required actions. Also we describe the application of this paradigm in finding short duration paths, chemical- patent mining, and in cloud computing services

Tuning adaptive computations for the performance improvement of applications in JEE server

With the increasing use of autonomic computing technologies, a Java Enterprise Edition (JEE) application server is implemented with more and more adaptive computations for self-managing the Middleware as well as its hosted applications. However, these adaptive computations consume resources such as CPU and memory, and can interfere with the normal business processing of applications at runtime due to resource competition, especially when the whole system is under heavy load. Tuning these adaptive computations from the perspective of resource management becomes necessary. In this article, we propose a tuning model for adaptive computations. Based on the model, tuning is carried out dynamically by upgrading or degrading the autonomic level of an adaptive computation so as to control its resource consumption. We implement the RSpring tuner and use it to optimize autonomic JEE servers such as PkuAS and JOnAS. RSpring is evaluated on ECperf and RUBiS benchmark applications. The results show that it can effectively improve the application performance by 13.6 % in PkuAS and 19.2 % in JOnAS with the same amount of resources. ? 2012 The Brazilian Computer Society.EI02143-158