A Multi-Agent System Toward the Green Edge Computing with Microgrid

The nature of multi-access edge computing (MEC) is to deal with heterogeneous computational tasks near to the end users, which induces the volatile energy consumption for the MEC network. As an energy supplier, a microgrid is able to enable seamless energy flow from renewable and non-renewable sources. In particular, the risk of energy demand and supply is increased due to nondeterministic nature of both energy consumption and generation. In this paper, we impose a risk-sensitive energy profiling problem for a microgrid-enabled MEC network, where we first formulate an optimization problem by considering Conditional Value-at-Risk (CVaR). Hence, the formulated problem can determine the risk of expected energy shortfall by coordinating with the uncertainties of both demand and supply, and we show this problem is NP-hard. Second, we design a multi-agent system that can determine a risk-sensitive energy profiling by coping with an optimal scheduling policy among the agents. Third, we devise the solution by applying a multi-agent deep reinforcement learning (MADRL) based on asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This approach mitigates the curse of dimensionality for state space and also, can admit the best energy profile policy among the agents. Finally, the experimental results establish the significant performance gain of the proposed model than that a single agent solution and achieves a high accuracy energy profiling with respect to risk constraint.