The advent of 5G drives the growth of multi-access edge computing (MEC), a revolutionary paradigm that utilises edge resources to enable low-latency mobile access and support complex service execution. Deploying services across geographically distributed edge nodes challenges providers to optimise performance metrics like end-to-end latency and resource efficiency, impacting user experience, operational cost, and environmental footprint. The energy harvesting (EH) technology provides clean and renewable energy at the edge, promoting the MEC system to minimise the impacts on the environment. However, the integration of EH can introduce energy limits and uncertainty to the powered devices. In the context of service scheduling with data flow dependencies, we propose two offline and heuristic-based service placement algorithms that balance minimizing latency and maximizing resource efficiency with fast execution. The two algorithms, evaluated in a simulated environment using state-of-the-art workload benchmarks, achieve significant energy consumption improvements while maintaining comparable latency. Based on the designed algorithms, we take a step further by developing an online dynamic resource scheduling and service offloading approach for MEC systems with EH capabilities. Simulation results demonstrate that the proposed strategy effectively utilise the harvested energy while granting a low user-experienced latency and low operational cost.

Agent-to-agent communications is an important operation in multi-agent systems and their simulation. Given the data-centric nature of agent-simulations, direct agent-to-agent communication is generally an orthogonal operation to accessing shared data in the simulation. In distributed multi-agent-system simulations in particular, implementing direct agent-to-agent communication may impose serious performance degradation due to potentially large communication and synchronization overheads. In this paper, we propose an efficient agent-to-agent communication method in the context of optimistic distributed simulation of multi-agent systems. An implementation of the proposed method is demonstrated and quantitatively evaluated through its integration into the PDES-MAS simulation kernel.