  An increasing amount of data is being injected into the network from IoT
(Internet of Things) applications. Many of these applications, developed to
improve society's quality of life, are latency-critical and inject large
amounts of data into the network. These requirements of IoT applications
trigger the emergence of Edge computing paradigm. Currently, data centers are
responsible for a global energy use between 2% and 3%. However, this trend is
difficult to maintain, as bringing computing infrastructures closer to the edge
of the network comes with its own set of challenges for energy efficiency. In
this paper, we propose our approach for the sustainability of future computing
infrastructures to provide (i) an energy-efficient and economically viable
deployment, (ii) a fault-tolerant automated operation, and (iii) a
collaborative resource management to improve resource efficiency. We identify
the main limitations of applying Cloud-based approaches close to the data
sources and present the research challenges to Edge sustainability arising from
these constraints. We propose two-phase immersion cooling, formal modeling,
machine learning, and energy-centric federated management as Edge-enabling
technologies. We present our early results towards the sustainability of an
Edge infrastructure to demonstrate the benefits of our approach for future
computing environments and deployments.
