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Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling. [ABSTRACT FROM AUTHOR]

New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered. [ABSTRACT FROM AUTHOR]