distributed computing
The Internet of Things and edge computing are fostering a future of ecosystems
hosting complex decentralized computations, deeply integrated with our very dynamic
environments. Digitalized buildings, communities of people, and cities will be the
next-generation “hardware and platform”, counting myriads of interconnected devices, on top of
which intrinsically-distributed computational processes will run and self-organize. They will
spontaneously spawn, diffuse to pertinent logical/physical regions, cooperate and compete,
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We introduce a benchmark of distributed algorithms execution over big data. The datasets are composed of metrics about the computational impact (resource usage) of eleven well-known machine learning techniques on a real computational cluster regarding system resource agnostic indicators: CPU consumption, memory usage, operating system processes load, net traffic, and I/O operations. The metrics were collected every five seconds for each algorithm on five different data volume scales, totaling 275 distinct datasets.
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