Francisco Moo-Mena

Defining a Self-Healing QoS-based Infrastructure for Web Services Applications

Web services (WS) technology proposes tools to implement complex distributed systems. It allows creating networks of heterogeneous and cooperative WS on applications. Due to the complexities related to the dynamics of this kind of system (new WS could come in and come out of the system), applications are exposed to several failure points in both components and connections. Current efforts in WS literature offer a supporting infrastructure for this problem; most of them are oriented to repairing, mainly on applications workflow level. In this paper, we expose our ideas to define an infrastructure based on quality of service (QoS) analysis of WS-based applications. Our approach considers a complementary strategy, supported by the adaptability of the applications architecture. This strategy is planned to prevent and respond to QoS degradation in WS-based applications, trying to enhance their performance. Our solution is oriented to discover new approaches for the three main stages, identified in the related literature for self-healing infrastructure: monitoring, diagnosis and recovering. In order to show our strategy effectiveness, we are going to implement it in the access to a Digital Library based on WS.

A diagnosis module based on statistic and QoS techniques for self-healing architectures supporting WS based applications

In literature it is common to find that a self-healing architecture is made up basically of three modules: monitoring, diagnosis, and recovery. Of these three modules, the diagnosis module represents a crucial point, since in this one the state that keeps the system is established. Nevertheless, a standardized way does not exist to implement this module in this kind of architecture. In this paper we propose a strategy of implementation of diagnosis module based on statistic methods by using box plot diagrams. This technique allows us to calibrate the parameters of quality of service (QoS) in a Web services based application. This way, based on the values of QoS, the diagnosis module determines if the system is stable or if a QoS degradation is presented.

Composition of Web Services Using Markov Decision Processes and Dynamic Programming

We propose a Markov decision process model for solving the Web service composition (WSC) problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate our approach, with artificial and real data. The experimental results show the reliability of the model and the methods employed, with policy iteration being the best one in terms of the minimum number of iterations needed to estimate an optimal policy, with the highest Quality of Service attributes. Our experimental work shows how the solution of a WSC problem involving a set of 100,000 individual Web services and where a valid composition requiring the selection of 1,000 services from the available set can be computed in the worst case in less than 200 seconds, using an Intel Core i5 computer with 6 GB RAM. Moreover, a real WSC problem involving only 7 individual Web services requires less than 0.08 seconds, using the same computational power. Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity.

Dynamic web services selection using a hidden Markov model

In a service oriented architecture based on web services exists the possibility of failures occurring at the time a transaction between web services runs. These failures are undesired because they reduce the systems performance. Self-healing systems are based on the model of the human body to restore it from an unhealthy state to a healthy one. These self-healing systems represent a good option for handling failures in a system based on web services. However, self-healing systems could improve their performance by adding a mechanism than selects the most suitable web services to perform certain functions. Moreover the diagnosis module of the self healing system would benefit by reducing the failures situations caused by anomalous web services. In this paper we propose to reduce the number of systems failures by employing a hidden Markov model that assist in the selection of web services through the use a QoS-based model. By reducing the number of failures this mechanism would support the diagnosis module of a self-healing system.

A Smart Diagnostic Model for an Autonomic Service Bus Based on a Probabilistic Reasoning Approach

The growing complexity and scale of systems implies challenges to include Autonomic Computing capabilities that help to maintain or improve the performance, availability and reliability characteristics. The autonomic management of a system can be defined deterministically based on experiment observations on the system and possible results of associated plans. However in dynamic environments with changing conditions and requirements, a better technique to diagnose observations and learn about the functioning conditions of the managed system is needed to guide the autonomic management. In the case of medical diagnostic, tests have included statistical and probabilistic models to aid and improve the results and select better medical treatments. In this paper we also adopt a probabilistic approach to define a Bayesian network from monitored data of an Enterprise Service Bus under different workload conditions. This model is used by the Autonomic Service Bus as a knowledge base to diagnose the cause of degradation problems and repair them. Experimental results assess the effectiveness of our approach.

Self-collision Detection using Sphere Chains

An algorithm to detect self-collisions in a human object is presented. We proposed to approximate the human object by spheres, which are placed inside the object mesh to fill the correspondent volume. We introduce the concept of sphere chain, a set of joined spheres which contains some regions of the human mesh. The object is approximated by several chains in the preprocessing stage to be prepared for the running stage to perform the collision detection.