MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management
Sirui Qi, Dejan Milojicic, Cullen Bash, Sudeep Pasricha

TL;DR
MOSAIC is a multi-objective optimization framework that efficiently manages geographically distributed datacenters to minimize environmental impact and operational costs through advanced algorithms.
Contribution
It introduces a novel co-optimization framework combining adaptive local search and evolutionary algorithms for sustainable datacenter management.
Findings
Achieves 27.45x speedup over prior methods.
Improves Pareto Hypervolume by 1.53x.
Reduces carbon footprint by up to 1.33x, water footprint by 3.09x, and energy costs by 1.40x.
Abstract
In recent years, cloud service providers have been building and hosting datacenters across multiple geographical locations to provide robust services. However, the geographical distribution of datacenters introduces growing pressure to both local and global environments, particularly when it comes to water usage and carbon emissions. Unfortunately, efforts to reduce the environmental impact of such datacenters often lead to an increase in the cost of datacenter operations. To co-optimize the energy cost, carbon emissions, and water footprint of datacenter operation from a global perspective, we propose a novel framework for multi-objective sustainable datacenter management (MOSAIC) that integrates adaptive local search with a collaborative decomposition-based evolutionary algorithm to intelligently manage geographical workload distribution and datacenter operations. Our framework…
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Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Software-Defined Networks and 5G
Methodstravel james
