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CS Colloquium: QoS-based service selection for multi-user composite applications
Mon, Jul 01, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Adrian Satja Kurdija / Goran Delac / Marin Silic, University of Zagreb
Talk Title: QoS-based service selection for multi-user composite applications
Series: CS Colloquium
Abstract: Cloud computing paradigms such as Service-Based Systems (SBSs) or Software as a Service (SaaS) are based on multiple cloud services providing various functionalities and responding to a number of client requests. For a given task, selecting the actual service instance per request can be an issue, if requirements for multiple Quality of Service (QoS) attributes need to be satisfied for many users simultaneously. The problem becomes more complex if we take into account the compositeness of the users' applications, which are workflows consisting of many tasks, where the QoS properties are calculated over the whole composition. We describe a fast heuristic method for multi-criteria service selection, designed for multi-user composite (multi-task) workflows with the goal of satisfying all, or as many as possible, of the given QoS requirements.
Biography: Adrian Satja Kurdija is a PhD student and research assistant at the University of Zagreb, Faculty of Electrical Engineering and Computing, Consumer Computing Lab. He received a masters degree in Computer Science and Mathematics from the University of Zagreb in 2015. From 2008 to 2010 he participated in International Mathematics Olympiad and International Olympiad in Informatics, winning a bronze and a silver medal. His research focuses on recommender systems for service-oriented architectures. He has published in IEEE Communications Letters, Wireless Networks, European Journal of Operational Research, International Journal of Web and Grid Services, and Knowledge-based systems.
Goran Delac is an assistant professor at the University of Zagreb, Faculty of Electrical Engineering and Computing, Consumer Computing Lab. He received his Ph.D. in Computer Science from the University of Zagreb Faculty of Electrical Engineering and Computing in 2014. His research interests include distributed systems, fault tolerant systems and service-oriented computing and recently mining massive datasets and machine learning. He has published papers in several significant journals according to the Web of Science Citation Index such as: IEEE Transactions on Services Computing, IEEE Transactions on Reliable and Dependable Computing and Knowledge-based systems. Also, he has published at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. He is a member of the IEEE.
Marin Silic is an assistant professor at the University of Zagreb, Faculty of Electrical Engineering and Computing, Consumer Computing Lab. He received his Ph.D. in Computer Science from the University of Zagreb Faculty of Electrical Engineering and Computing in 2013. His research interests span distributed systems, service-oriented computing, software engineering, software reliability and recently data mining and machine learning. He has published papers in several significant journals according to the Web of Science Citation Index such as: IEEE Transactions on Services Computing, IEEE Transactions on Reliable and Dependable Computing and Knowledge-based systems. Also, he has published at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. He is a member of the IEEE.
Host: Nenad Medvidovic
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Defense - Palash Goyal
Tue, Jul 23, 2019 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Palash Goyal
Date:
Tuesday July 23rd, 2019
1:00 p.m. - 3:00 pm
Location: OHE 136
Committee: Emilio Ferrara, Gaurav Sukhatme, Cauligi Raghavendra
Title:
Graph Embedding Algorithms for Attributed and Temporal Graphs
Abstract:
Learning low-dimensional representations of nodes in a graph has recently gained attention due to its wide applicability in network tasks such as graph visualization, link prediction, node classification and clustering. The models proposed often preserve certain characteristic properties of the graph and are tested on these tasks. In this thesis, I propose to extend the graph embedding work in three directions. Firstly, I yield insights into the existing models and understand the universality of the learned embeddings and embedding methods. Specifically, I study the dependence of performance on a task on the model hyperparameters of the embedding method. I also analyze the characteristics of models required for each network task. Further, I propose a benchmark to evaluate any graph embedding approach and draw insights into it. Secondly, I propose an extension of graph embedding approach which can capture edge attributes of a graph. I show that capturing such attributes can be useful in link prediction and propose a model to learn edge attributes along with higher order proximity and social roles. Thirdly, I build models which can update embeddings efficiently for streaming graphs and can capture temporal patterns in sequential graphs.
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
