Title
Graph Greenifier: Towards Sustainable and Energy-Aware Massive Graph Processing in the Computing Continuum
Author
Iosup, Alexandru (Vrije Universiteit Amsterdam)
Prodan, Radu (Aau Klagenfurt, Klagenfurt)
Varbanescu, Ana Lucia (University of Twente)
Talluri, Sacheendra (Aau Klagenfurt, Klagenfurt)
Magalhaes, Gilles (Aau Klagenfurt, Klagenfurt)
Hokstam, Kailhan (Aau Klagenfurt, Klagenfurt)
Zwaan, Hugo (Vrije Universiteit Amsterdam)
van Beek, V.S. (TU Delft Data-Intensive Systems)
Farahani, Reza (Aau Klagenfurt, Klagenfurt)
Date
2023
Abstract
Our society is increasingly digital, and its processes are increasingly digitalized. As an emerging technology for the digital society, graphs provide a universal abstraction to represent concepts and objects, and the relationships between them. However, processing graphs at a massive scale raises numerous sustainability challenges; becoming energy-aware could help graph-processing infrastructure alleviate its climate impact. Graph Greenifier aims to address this challenge in the conceptual framework offered by the Graph Massivizer architecture. We present an early vision of how Graph Greenifier could provide sustainability analysis and decision-making capabilities for extreme graph-processing workloads. Graph Greenifier leverages an advanced digital twin for data center operations, based on the OpenDC open-source simulator, a novel toolchain for workload-driven simulation of graph processing at scale, and a sustainability predictor. The input to the digital twin combines monitoring of the information and communication technology infrastructure used for graph processing with data collected from the power grid. Graph Greenifier thus informs providers and consumers on operational sustainability aspects, requiring mutual information sharing, reducing energy consumption for graph analytics, and increasing the use of electricity from renewable sources.
Subject
computing continuum
digital twin
energy-awareness
graph greenifier
graph massivizer
graph processing
scalability
sustainability
To reference this document use:
http://resolver.tudelft.nl/uuid:109da31e-54c6-47b2-8b6e-8232daa5eb72
DOI
10.1145/3578245.3585329
Publisher
Association for Computing Machinery (ACM)
ISBN
979-840070072-9
Source
ICPE 2023 - Companion of the 2023 ACM/SPEC International Conference on Performance Engineering
Event
14th Annual ACM/SPEC International Conference on Performance Engineering, ICPE 2023, 2023-04-15 → 2023-04-19, Coimbra, Portugal
Series
ICPE 2023 - Companion of the 2023 ACM/SPEC International Conference on Performance Engineering
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2023 Alexandru Iosup, Radu Prodan, Ana Lucia Varbanescu, Sacheendra Talluri, Gilles Magalhaes, Kailhan Hokstam, Hugo Zwaan, V.S. van Beek, Reza Farahani, More Authors