Towards Autonomic Renewable Energy-Aware Osmotic Computing: IoTSim-Osmosis-RES Framework

מושגי ליבה

The paper proposes a novel simulation framework, IoTSim-Osmosis-RES, that enables research on sustainable and autonomic IoT systems by incorporating renewable energy sources and autonomous osmotic agents.

תקציר

The paper presents a new simulation model, IoTSim-Osmosis-RES, that extends the existing IoTSim-Osmosis simulator to enable research on sustainable and autonomic IoT systems. The key features of the proposed framework include:

Renewable Energy Sources (RES) Module:

Modeling of photovoltaic (PV) installations, energy storage, and power grid integration for datacenters and IoT devices.
Incorporation of historical solar radiation data to simulate the dynamic availability of renewable energy.
Implementation of different energy management policies to optimize the usage of renewable energy.

Osmotic Agents Module:

Deployment of autonomous agents on IoT devices, edge datacenters, and cloud datacenters.
Agents implement the Autonomic Computing MAPE (Monitor-Analyze-Plan-Execute) loop to manage the system adaptively.
Agents can cooperate through communication models (independent, communicating, or centralized) to coordinate system adaptation.

The authors evaluate the proposed framework using a case study focused on managing renewable energy sources in an IoT system. The simulation results demonstrate the ability to assess various parameters, such as the level of solar radiation, usage of renewable energy sources (RES), usage of low-emission sources, and IoT device battery capacity, under different adaptation algorithms implemented by the osmotic agents.

סטטיסטיקה

The average power consumption at datacenter i is calculated as:
𝑒𝑖 = 𝑠𝑎𝑛𝑛
𝑖 / (𝑒𝑢
𝑖 ⋅ 365 ⋅ 24)
The energy self-consumption metric 𝑀𝑠𝑒𝑙𝑓 is calculated as:
𝑀𝑠𝑒𝑙𝑓 = ∑𝑘 (∑𝑖 𝑡𝑘
𝑖 𝑒𝑠𝑒𝑙𝑓(𝑡𝑘)
𝑖 / ∑𝑖 𝑡𝑘
𝑖)
The metric 𝑀𝑙𝑜𝑤 for the use of low-emission sources is calculated as:
𝑀𝑙𝑜𝑤 = ∑𝑘 (∑𝑖 𝑡𝑘
𝑖 𝑒𝑠𝑒𝑙𝑓(𝑡𝑘)
𝑖 + ∑𝑖 𝑡𝑘
𝑖 𝑝𝑙𝑜𝑤
𝑖 (1 − 𝑒𝑠𝑒𝑙𝑓(𝑡𝑘)
𝑖) / ∑𝑖 𝑡𝑘
𝑖)

ציטוטים

None.

תובנות מפתח מזוקקות מ:

IoTSim-Osmosis-RES: Towards autonomic renewable energy-aware osmotic computing

by Tomasz Szydl... ב- arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11481.pdf

שאלות מעמיקות

How can the proposed framework be extended to incorporate other renewable energy sources, such as wind or hydropower, and their impact on the management of IoT systems

To extend the proposed framework to incorporate other renewable energy sources like wind or hydropower, several modifications and additions would be necessary. Firstly, the renewable energy module would need to be expanded to include parameters specific to wind or hydropower generation, such as wind speed, water flow rate, and turbine efficiency. Historical data for these sources would also need to be integrated into the simulation for accurate modeling.
Secondly, the energy storage component would require adjustments to account for the intermittent nature of wind and hydropower generation. Energy storage systems like pumped hydro storage or battery banks could be included to store excess energy during peak generation periods for use during low generation periods.
Thirdly, the power grid module would need to consider the integration of multiple renewable sources into the grid, as well as the variability of wind and water-based generation. Algorithms for balancing energy supply and demand from these sources would need to be developed to ensure grid stability.
Lastly, the adaptation algorithms for osmotic agents would need to be enhanced to consider the availability and reliability of different renewable sources. Agents could be trained to prioritize energy sources based on factors like weather conditions, energy cost, and environmental impact, optimizing the use of wind or hydropower in the IoT system.

What are the potential challenges and limitations in applying reinforcement learning techniques for the autonomous adaptation of osmotic agents, and how can they be addressed

Applying reinforcement learning techniques for the autonomous adaptation of osmotic agents poses several challenges and limitations. One challenge is the complexity of the IoT system and the large number of variables that can influence agent decisions. Reinforcement learning algorithms may struggle to converge on optimal policies in such complex environments.
Another challenge is the need for extensive training data to ensure the agents learn effective strategies. The dynamic nature of IoT systems and renewable energy availability may require continuous retraining of the agents, which can be resource-intensive.
Addressing these challenges involves careful algorithm design and training methodology. Techniques like experience replay, reward shaping, and curriculum learning can help improve the stability and efficiency of reinforcement learning algorithms in complex environments. Additionally, incorporating domain knowledge into the learning process can guide the agents towards more effective decision-making.
Regular evaluation and fine-tuning of the reinforcement learning models based on real-world data and feedback from the IoT system can help address limitations and improve the performance of the autonomous osmotic agents over time.

How can the framework be used to investigate the trade-offs between energy efficiency, system performance, and cost in the context of sustainable IoT systems

The framework can be used to investigate the trade-offs between energy efficiency, system performance, and cost in sustainable IoT systems by conducting simulation experiments with different scenarios and adaptation algorithms.

Energy Efficiency: By varying the parameters related to renewable energy utilization, energy storage, and device operation, the framework can analyze the impact on the self-consumption of renewable energy and the overall energy efficiency of the system. Different adaptation algorithms can be tested to optimize energy usage and reduce reliance on non-renewable sources.

System Performance: The framework can evaluate the performance of the IoT system in terms of data processing speed, latency, and reliability under different energy management strategies. By measuring the processing time, data transfer rates, and system response times, the trade-offs between energy-efficient operation and system performance can be assessed.

Cost Analysis: By incorporating cost metrics for energy consumption from renewable and non-renewable sources, the framework can analyze the financial implications of different energy management approaches. This analysis can help in determining the most cost-effective strategies for sustainable IoT operation.

By conducting comprehensive simulations and analyzing the results, the framework can provide insights into the optimal balance between energy efficiency, system performance, and cost in sustainable IoT systems, enabling informed decision-making for system design and operation.