Semantic-Oriented Performance Monitoring of Distributed Applications

Monitoring services are an essential component of large-scale computing infrastructures due to providing information which can be used by humans as well as applications to closely follow the progress of computations, to evaluate the performance of ongoing computing, etc. However, the users are usually left alone with performance measurements as to the interpreting and detecting of execution flaws. In this paper we present an approach to the performance monitoring of distributed applications based on semantic information about the monitored objects involved in the application execution. This allows to automate the guidance on what to measure further to come to a source of performance flaws as well to enable reacting on interesting events, e.g. on exceeding SLA parameters. Our research comprises the implementation of a robust system with semantics, which is not biased to an underlying ``physical'' monitoring system, giving the end user the power of intelligent monitoring functionality as well as the independence of the heterogeneity of distributed infrastructures

Optimizing monitorability of multi-cloud applications

When adopting a multi-cloud strategy, the selection of cloud providers where to deploy VMs is a crucial task for ensuring a good behaviour for the developed application. This selection is usually focused on the general information about performances and capabilities offered by the cloud providers. Less attention has been paid to the monitoring services although, for the application developer, is fundamental to understand how the application behaves while it is running. In this paper we propose an approach based on a multi-objective mixed integer linear optimization problem for supporting the selection of the cloud providers able to satisfy constraints on monitoring dimensions associated to VMs. The balance between the quality of data monitored and the cost for obtaining these data is considered, as well as the possibility for the cloud provider to enrich the set of monitored metrics through data analysis

Use of Self-Healing Techniques for Highly-Available Distributed Monitoring

The paper addresses the self-healing aspects of the monitoring systems. Nowadays, when the complex distributed systems are concerned, the monitoring system should become "intelligent" - as the first step it can guide the user what should be monitored. The next level of the "intelligence" can be described by the term "self-healing". The goal is to provide the capability that a decision made automatically by the monitoring system should force the system under monitoring to behave more stable, reliable and predictable. In the paper a new monitoring system is presented: AgeMon is an agent based, distributed monitoring system with strictly defined roles which can be performed by the agents. In the paper we discuss self-healing in the context of monitoring. When the self-healing of the monitoring system is concerned, a good example is the case where it is possible to lose the monitoring data due to the storage problems. AgeMon can handle such problems and automatically elects substitute persistence agents to store the data