Process-algebraic modelling of priority queueing networks

We consider a closed multiclass queueing network model in which each class receives a different priority level and jobs with lower priority are served only if there are no higher-priority jobs in the queue. Such systems do not enjoy a product form solution, thus their analysis is typically carried out through approximate mean value analysis (AMVA) techniques. We formalise the problem in PEPA in a way amenable to differential analysis. Experimental results show that our approach is competitive with simulation and AMVA methods

Anomaly Detection for Big Data Technologies

The main goal of this research is to contribute to automated performance anomaly detection for large-scale and complex distributed systems, especially for Big Data applications within cloud computing. The main points that we will investigate are: - Automated detection of anomalous performance behaviors by finding the relevant performance metrics with which to characterize behavior of systems. - Performance anomaly localization: To pinpoint the cause of a performance anomaly due to internal or external faults. - Investigation of the possibility of anomaly prediction. Failure prediction aims to determine the possible occurrences of catastrophic events in the near future and will enable system developers to utilize effective monitoring solutions to guarantee system availability. - Assessment for the potential of hybrid methods that combine machine learning with traditional methods used in performance for anomaly detection. The topic of this research proposal will offer me the opportunity to more deeply apply my interest in the field of performance anomaly detection and prediction by investigating and using novel optimization strategies. In addition, this research provides a very interesting case of utilizing the anomaly detection techniques in a large-scale Big Data and cloud computing environment. Among the various Big Data technologies, in-memory processing technology like Apache Spark has become widely adopted by industries as result of its speed, generality, ease of use, and compatibility with other Big Data systems. Although Spark is developing gradually, currently there are still shortages in comprehensive performance analyses that specifically build for Spark and are used to detect performance anomalies. Therefore, this raises my interest in addressing this challenge by investigating new hybrid learning techniques for anomaly detection in large-scale and complex systems, especially for in-memory processing Big Data platforms within cloud computing

Cognitive distance and research output in computing education:a case-study

Contribution: This paper quantifies the phenomenon of more versus better research output in computing research education and elaborates on how the organizational variable known as cognitive distance plays a fundamental role in mediating such more versus better research output relation. Background: To improve the current educational system, investigation and quantification is needed of the 'silos.' Cognitive distance - a measure of the differences in background, culture, and expertise between collaborators - may be a factor influencing the lack of quality and variety in research outputs. Addressing this is a key enabler for fruitful collaboration. Research Question: Does collaboration with similarly expert researchers yield better research? Methodology: A quantitative survey provides baseline data for cognitive distance while publication data allowed creation of a co-authorship network between 123 researchers in a European computing research department. The network was analyzed through quantitative and qualitative research methods. Findings: Increased expertise overlaps across sub-fields of computing is a strong predictor for further collaboration (quantity), but research impact (quality) decreases with larger overlaps. This reveals an educational silo effect in doctoral computing education and, consequently, a flaw in the connected research output. The lack of a single, agreed way to evaluate research impact across sub-fields further hinders cross-departmental collaboration among doctoral students. Conclusion: Three recommendations emerge for policy makers and educational leaders: 1) departments should be cross-functional and focused on societal interests; 2) communities of practice should be created at the level of doctoral education and upward; and 3) departments should hold matchmaking and speed-meeting events regularly within and across institutions.</p

Process-Algebraic Modelling of Priority Queueing Networks

We consider a closed multiclass queueing network model in which each class receives a different priority level and jobs with lower priority are served only if there are no higher-priority jobs in the queue. Such systems do not enjoy a product form solution, thus their analysis is typically carried out through approximate mean value analysis (AMVA) techniques. We formalise the problem in PEPA in a way amenable to differential analysis. Experimental results show that our approach is competitive with simulation and AMVA methods

Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management