Paper accepted: Towards Sustainable Serverless Processing of Massive Graphs on the Computing Continuum @ ICPE

Title: Towards Sustainable Serverless Processing of Massive Graphs on the Computing Continuum

The First Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys), Co-located with ICPE 2023, April 15-19, Coimbra, Portugal (https://sites.google.com/view/graphsys23/home)

Authors: Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Dragi Kimovski (Alpen-Adria-Universität Klagenfurt, Austria), Sashko Ristov (Universität Innsbruck, Austria), Alexandru Iosup (Vrije Universiteit Amsterdam, Netherland), Radu Prodan (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: With the ever-increasing volume of data and the demand to analyze and comprehend it, graph processing has become an essential apapproach for solving complex problems in various domains, like social networks, bioinformatics, and finance. Despite the potential benefits
of current graph processing platforms, they often encounter difficultures supporting diverse workloads, models, and languages. Moreover, existing specialized platforms suffer from limited portability and interoperability, resulting in redundant efforts and inefficient resource and energy utilization due to vendor and even platform lock-in. To bridge the aforementioned gaps, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. In this paper, we briefly introduce the Graph-Massivizer platform. We then leverage the emerging serverless computing paradigm to devise Graph-Serverlizer, a scalable graph analytics tool over a codesigned computing continuum infrastructure. Finally, we sketch six crucial research questions in Graph-Serverlizer’s design and outline three ongoing and future research directions for addressing them.