IoTSim-Osmosis-RES: Towards autonomic renewable energy-aware osmotic computing
Tomasz Szydlo, Amadeusz Szabala, Nazar Kordiumov, Konrad Siuzdak, Lukasz Wolski, Khaled Alwasel, Fawzy Habeeb, Rajiv Ranjan
TL;DR
The paper addresses the challenge of autonomic, renewable-energy-aware data processing for IoT ecosystems by extending the IoTSim-Osmosis simulator with a Renewable Energy Sources module and Autonomic Osmotic Agents (MAPE-based). It demonstrates how energy availability and emissions profiles shape adaptive data-flow migrations between edge and cloud, using five agent algorithms and metrics for renewable self-consumption and low-emission usage. The results show that adaptation logic crucially affects energy efficiency and environmental impact, indicating that system performance can be steered by the choice of energy-aware policies and routing strategies. The work advances practical simulation tools for designing sustainable osmotic IoT deployments and lays groundwork for reinforcement-learning-based, autonomic energy management.
Abstract
Internet of Things systems exists in various areas of our everyday life. For example, sensors installed in smart cities and homes are processed in edge and cloud computing centres providing several benefits that improve our lives. The place of data processing is related to the required system response times -- processing data closer to its source results in a shorter system response time. The Osmotic Computing concept enables flexible deployment of data processing services and their possible movement, just like particles in the osmosis phenomenon move between regions of different densities. At the same time, the impact of complex computer architecture on the environment is increasingly being compensated by the use of renewable and low-carbon energy sources. However, the uncertainty of supplying green energy makes the management of Osmotic Computing demanding, and therefore their autonomy is desirable. In the paper, we present a framework enabling osmotic computing simulation based on renewable energy sources and autonomic osmotic agents, allowing the analysis of distributed management algorithms. We discuss the challenges posed to the framework and analyze various management algorithms for cooperating osmotic agents. In the evaluation we show that changing the adaptation logic of the osmotic agents, it is possible to increase the self-consumption of renewable energy sources or increase the usage of low emission ones.